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Sample records for geostatistics random sets

  1. Random vectors and spatial analysis by geostatistics for geotechnical applications

    SciTech Connect

    Young, D.S.

    1987-08-01

    Geostatistics is extended to the spatial analysis of vector variables by defining the estimation variance and vector variogram in terms of the magnitude of difference vectors. Many random variables in geotechnology are in vectorial terms rather than scalars, and its structural analysis requires those sample variable interpolations to construct and characterize structural models. A better local estimator will result in greater quality of input models; geostatistics can provide such estimators; kriging estimators. The efficiency of geostatistics for vector variables is demonstrated in a case study of rock joint orientations in geological formations. The positive cross-validation encourages application of geostatistics to spatial analysis of random vectors in geoscience as well as various geotechnical fields including optimum site characterization, rock mechanics for mining and civil structures, cavability analysis of block cavings, petroleum engineering, and hydrologic and hydraulic modelings.

  2. Fast Geostatistical Inversion using Randomized Matrix Decompositions and Sketchings for Heterogeneous Aquifer Characterization

    NASA Astrophysics Data System (ADS)

    O'Malley, D.; Le, E. B.; Vesselinov, V. V.

    2015-12-01

    We present a fast, scalable, and highly-implementable stochastic inverse method for characterization of aquifer heterogeneity. The method utilizes recent advances in randomized matrix algebra and exploits the structure of the Quasi-Linear Geostatistical Approach (QLGA), without requiring a structured grid like Fast-Fourier Transform (FFT) methods. The QLGA framework is a more stable version of Gauss-Newton iterates for a large number of unknown model parameters, but provides unbiased estimates. The methods are matrix-free and do not require derivatives or adjoints, and are thus ideal for complex models and black-box implementation. We also incorporate randomized least-square solvers and data-reduction methods, which speed up computation and simulate missing data points. The new inverse methodology is coded in Julia and implemented in the MADS computational framework (http://mads.lanl.gov). Julia is an advanced high-level scientific programing language that allows for efficient memory management and utilization of high-performance computational resources. Inversion results based on series of synthetic problems with steady-state and transient calibration data are presented.

  3. A general parallelization strategy for random path based geostatistical simulation methods

    NASA Astrophysics Data System (ADS)

    Mariethoz, Grégoire

    2010-07-01

    The size of simulation grids used for numerical models has increased by many orders of magnitude in the past years, and this trend is likely to continue. Efficient pixel-based geostatistical simulation algorithms have been developed, but for very large grids and complex spatial models, the computational burden remains heavy. As cluster computers become widely available, using parallel strategies is a natural step for increasing the usable grid size and the complexity of the models. These strategies must profit from of the possibilities offered by machines with a large number of processors. On such machines, the bottleneck is often the communication time between processors. We present a strategy distributing grid nodes among all available processors while minimizing communication and latency times. It consists in centralizing the simulation on a master processor that calls other slave processors as if they were functions simulating one node every time. The key is to decouple the sending and the receiving operations to avoid synchronization. Centralization allows having a conflict management system ensuring that nodes being simulated simultaneously do not interfere in terms of neighborhood. The strategy is computationally efficient and is versatile enough to be applicable to all random path based simulation methods.

  4. Geostatistics as a validation tool for setting ozone standards for durum wheat.

    PubMed

    De Marco, Alessandra; Screpanti, Augusto; Paoletti, Elena

    2010-02-01

    Which is the best standard for protecting plants from ozone? To answer this question, we must validate the standards by testing biological responses vs. ambient data in the field. A validation is missing for European and USA standards, because the networks for ozone, meteorology and plant responses are spatially independent. We proposed geostatistics as validation tool, and used durum wheat in central Italy as a test. The standards summarized ozone impact on yield better than hourly averages. Although USA criteria explained ozone-induced yield losses better than European criteria, USA legal level (75 ppb) protected only 39% of sites. European exposure-based standards protected > or =90%. Reducing the USA level to the Canadian 65 ppb or using W126 protected 91% and 97%, respectively. For a no-threshold accumulated stomatal flux, 22 mmol m(-2) was suggested to protect 97% of sites. In a multiple regression, precipitation explained 22% and ozone explained <0.9% of yield variability.

  5. Scaling random walks on arbitrary sets

    NASA Astrophysics Data System (ADS)

    Harris, Simon C.; Williams, David; Sibson, Robin

    1999-01-01

    Let I be a countably infinite set of points in [open face R] which we can write as I={ui: i[set membership][open face Z]}, with uirandom-walk, when repeatedly rescaled suitably in space and time, looks more and more like a Brownian motion. In this paper we explore the convergence properties of the Markov chain Y on the set I under suitable space-time scalings. Later, we consider some cases when the set I consists of the points of a renewal process and the jump rates assigned to each state in I are perhaps also randomly chosen.This work sprang from a question asked by one of us (Sibson) about ‘driftless nearest-neighbour’ Markov chains on countable subsets I of [open face R]d, work of Sibson [7] and of Christ, Friedberg and Lee [2] having identified examples of such chains in terms of the Dirichlet tessellation associated with I. Amongst methods which can be brought to bear on this d-dimensional problem is the theory of Dirichlet forms. There are potential problems in doing this because we wish I to be random (for example, a realization of a Poisson point process), we do not wish to impose artificial boundedness conditions which would clearly make things work for certain deterministic sets I. In the 1-dimensional case discussed here and in the following paper by Harris, much simpler techniques (where we embed the Markov chain in a Brownian motion using local time) work very effectively; and it is these, rather than the theory of Dirichlet forms, that we use.

  6. GEOSTATISTICAL SAMPLING DESIGNS FOR HAZARDOUS WASTE SITES

    EPA Science Inventory

    This chapter discusses field sampling design for environmental sites and hazardous waste sites with respect to random variable sampling theory, Gy's sampling theory, and geostatistical (kriging) sampling theory. The literature often presents these sampling methods as an adversari...

  7. Description and validation of a new set of object-based temporal geostatistical features for land-use/land-cover change detection

    NASA Astrophysics Data System (ADS)

    Gil-Yepes, Jose L.; Ruiz, Luis A.; Recio, Jorge A.; Balaguer-Beser, Ángel; Hermosilla, Txomin

    2016-11-01

    A new set of temporal features derived from codispersion and cross-semivariogram geostatistical functions is proposed, described, extracted, and evaluated for object-based land-use/land-cover change detection using high resolution images. Five features were extracted from the codispersion function and another six from the cross-semivariogram. The set of features describes the temporal behaviour of the internal structure of the image objects defined in a cadastral database. The set of extracted features was combined with spectral information and a feature selection study was performed using forward stepwise discriminant analysis, principal component analysis, as well as correlation and feature interpretation analysis. The temporal feature set was validated using high resolution aerial images from an agricultural area located in south-east Spain, in order to solve a tree crop change detection problem. Direct classification using decision tree classifier was used as change detection method. Different classifications were performed comparing various feature group combinations in order to obtain the most suitable features for this study. Results showed that the new sets of cross-semivariogram and codispersion features provided high global accuracy classification results (83.55% and 85.71% respectively), showing high potential for detecting changes related to the internal structure of agricultural tree crop parcels. A significant increase in accuracy value was obtained when combining features from both groups with spectral information (94.59%).

  8. Geostatistics and petroleum geology

    SciTech Connect

    Hohn, M.E.

    1988-01-01

    This book examines purpose and use of geostatistics in exploration and development of oil and gas with an emphasis on appropriate and pertinent case studies. It present an overview of geostatistics. Topics covered include: The semivariogram; Linear estimation; Multivariate geostatistics; Nonlinear estimation; From indicator variables to nonparametric estimation; and More detail, less certainty; conditional simulation.

  9. Imprecise (fuzzy) information in geostatistics

    SciTech Connect

    Bardossy, A.; Bogardi, I.; Kelly, W.E.

    1988-05-01

    A methodology based on fuzzy set theory for the utilization of imprecise data in geostatistics is presented. A common problem preventing a broader use of geostatistics has been the insufficient amount of accurate measurement data. In certain cases, additional but uncertain (soft) information is available and can be encoded as subjective probabilities, and then the soft kriging method can be applied (Journal, 1986). In other cases, a fuzzy encoding of soft information may be more realistic and simplify the numerical calculations. Imprecise (fuzzy) spatial information on the possible variogram is integrated into a single variogram which is used in a fuzzy kriging procedure. The overall uncertainty of prediction is represented by the estimation variance and the calculated membership function for each kriged point. The methodology is applied to the permeability prediction of a soil liner for hazardous waste containment. The available number of hard measurement data (20) was not enough for a classical geostatistical analysis. An additional 20 soft data made it possible to prepare kriged contour maps using the fuzzy geostatistical procedure.

  10. Application of geostatistics to risk assessment.

    PubMed

    Thayer, William C; Griffith, Daniel A; Goodrum, Philip E; Diamond, Gary L; Hassett, James M

    2003-10-01

    Geostatistics offers two fundamental contributions to environmental contaminant exposure assessment: (1) a group of methods to quantitatively describe the spatial distribution of a pollutant and (2) the ability to improve estimates of the exposure point concentration by exploiting the geospatial information present in the data. The second contribution is particularly valuable when exposure estimates must be derived from small data sets, which is often the case in environmental risk assessment. This article addresses two topics related to the use of geostatistics in human and ecological risk assessments performed at hazardous waste sites: (1) the importance of assessing model assumptions when using geostatistics and (2) the use of geostatistics to improve estimates of the exposure point concentration (EPC) in the limited data scenario. The latter topic is approached here by comparing design-based estimators that are familiar to environmental risk assessors (e.g., Land's method) with geostatistics, a model-based estimator. In this report, we summarize the basics of spatial weighting of sample data, kriging, and geostatistical simulation. We then explore the two topics identified above in a case study, using soil lead concentration data from a Superfund site (a skeet and trap range). We also describe several areas where research is needed to advance the use of geostatistics in environmental risk assessment.

  11. Randomization methods in emergency setting trials: a descriptive review

    PubMed Central

    Moe‐Byrne, Thirimon; Oddie, Sam; McGuire, William

    2015-01-01

    Background Quasi‐randomization might expedite recruitment into trials in emergency care settings but may also introduce selection bias. Methods We searched the Cochrane Library and other databases for systematic reviews of interventions in emergency medicine or urgent care settings. We assessed selection bias (baseline imbalances) in prognostic indicators between treatment groups in trials using true randomization versus trials using quasi‐randomization. Results Seven reviews contained 16 trials that used true randomization and 11 that used quasi‐randomization. Baseline group imbalance was identified in four trials using true randomization (25%) and in two quasi‐randomized trials (18%). Of the four truly randomized trials with imbalance, three concealed treatment allocation adequately. Clinical heterogeneity and poor reporting limited the assessment of trial recruitment outcomes. Conclusions We did not find strong or consistent evidence that quasi‐randomization is associated with selection bias more often than true randomization. High risk of bias judgements for quasi‐randomized emergency studies should therefore not be assumed in systematic reviews. Clinical heterogeneity across trials within reviews, coupled with limited availability of relevant trial accrual data, meant it was not possible to adequately explore the possibility that true randomization might result in slower trial recruitment rates, or the recruitment of less representative populations. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd. PMID:26333419

  12. Geostatistics and petroleum geology

    SciTech Connect

    Hohn, M.E.

    1988-01-01

    The book reviewed is designed as a practical guide to geostatistics or kriging for the petroleum geologists. The author's aim in the book is to explain geostatistics as a working tool for petroleum geologists through extensive use of case-study material mostly drawn from his own research in gas potential evaluation in West Virginia. Theory and mathematics are pared down to immediate needs.

  13. Geostatistical model to estimate in stream pollutant loads and concentrations.

    NASA Astrophysics Data System (ADS)

    Polus, E.; Flipo, N.; de Fouquet, C.; Poulin, M.

    2009-04-01

    Models that estimate loads and concentrations of pollutants in streams can roughly be classified into two categories: physically-based and stochastic models. While the first ones tend to reproduce physical processes that occur in streams, the stochastic models consider loads and concentrations as random variables. This work is interesting in such models and particularly in geostatistical models, which provide an estimate of loads and concentrations and the joint measurement of uncertainty also: the estimation variance. Along a stream network that can be modelled as a graph, most of usual geostatistical covariance or variogram models are not valid anymore. Based on recent models applied on tree graphs, we present a covariance or variogram construction combining one-dimensional Random Functions (RF) defined on each path between sources and the outlet. The model properties are examined, namely the consistency conditions at the confluences for different variables. In practice, the scarcity of spatial data makes a precise inference of covariances difficult. Can then a phenomenological model be used to guide the geostatistical modelling? To answer this question the example of a portion of the Seine River (France) is examined, where both measurement data and the outputs of the physically-based model ProSe are used. The comparison between both data sets shows an excellent agreement for discharges and a consistent one for nitrate concentrations. Nevertheless, a detailed exploratory analysis brings to light the importance of the boundary conditions, which ones are not consistent with the downstream measurements. The agreement between data and modelled values can be improved thanks to a reconstruction of consistent boundary conditions by cokriging. This is an example of the usefulness of using jointly physically-based models and geostatistics. The next step is a joint modelling of discharges, loads and concentrations along the stream network. This modelling should improve the

  14. Randomization Methods in Emergency Setting Trials: A Descriptive Review

    ERIC Educational Resources Information Center

    Corbett, Mark Stephen; Moe-Byrne, Thirimon; Oddie, Sam; McGuire, William

    2016-01-01

    Background: Quasi-randomization might expedite recruitment into trials in emergency care settings but may also introduce selection bias. Methods: We searched the Cochrane Library and other databases for systematic reviews of interventions in emergency medicine or urgent care settings. We assessed selection bias (baseline imbalances) in prognostic…

  15. Dissecting random and systematic differences between noisy composite data sets.

    PubMed

    Diederichs, Kay

    2017-04-01

    Composite data sets measured on different objects are usually affected by random errors, but may also be influenced by systematic (genuine) differences in the objects themselves, or the experimental conditions. If the individual measurements forming each data set are quantitative and approximately normally distributed, a correlation coefficient is often used to compare data sets. However, the relations between data sets are not obvious from the matrix of pairwise correlations since the numerical value of the correlation coefficient is lowered by both random and systematic differences between the data sets. This work presents a multidimensional scaling analysis of the pairwise correlation coefficients which places data sets into a unit sphere within low-dimensional space, at a position given by their CC* values [as defined by Karplus & Diederichs (2012), Science, 336, 1030-1033] in the radial direction and by their systematic differences in one or more angular directions. This dimensionality reduction can not only be used for classification purposes, but also to derive data-set relations on a continuous scale. Projecting the arrangement of data sets onto the subspace spanned by systematic differences (the surface of a unit sphere) allows, irrespective of the random-error levels, the identification of clusters of closely related data sets. The method gains power with increasing numbers of data sets. It is illustrated with an example from low signal-to-noise ratio image processing, and an application in macromolecular crystallography is shown, but the approach is completely general and thus should be widely applicable.

  16. Fractal and geostatistical methods for modeling of a fracture network

    SciTech Connect

    Chiles, J.P.

    1988-08-01

    The modeling of fracture networks is useful for fluid flow and rock mechanics studies. About 6600 fracture traces were recorded on drifts of a uranium mine in a granite massif. The traces have an extension of 0.20-20 m. The network was studied by fractal and by geostatistical methods but can be considered neither as a fractal with a constant dimension nor a set of purely randomly located fractures. Two kinds of generalization of conventional models can still provide more flexibility for the characterization of the network: (a) a nonscaling fractal model with variable similarity dimension (for a 2-D network of traces, the dimension varying from 2 for the 10-m scale to 1 for the centimeter scale, (b) a parent-daughter model with a regionalized density; the geostatistical study allows a 3-D model to be established where: fractures are assumed to be discs; fractures are grouped in clusters or swarms; and fracturation density is regionalized (with two ranges at about 30 and 300 m). The fractal model is easy to fit and to simulate along a line, but 2-D and 3-D simulations are more difficult. The geostatistical model is more complex, but easy to simulate, even in 3-D.

  17. Finitely approximable random sets and their evolution via differential equations

    NASA Astrophysics Data System (ADS)

    Ananyev, B. I.

    2016-12-01

    In this paper, random closed sets (RCS) in Euclidean space are considered along with their distributions and approximation. Distributions of RCS may be used for the calculation of expectation and other characteristics. Reachable sets on initial data and some ways of their approximate evolutionary description are investigated for stochastic differential equations (SDE) with initial state in some RCS. Markov property of random reachable sets is proved in the space of closed sets. For approximate calculus, the initial RCS is replaced by a finite set on the integer multidimensional grid and the multistage Markov chain is substituted for SDE. The Markov chain is constructed by methods of SDE numerical integration. Some examples are also given.

  18. Geostatistics and spatial analysis in biological anthropology.

    PubMed

    Relethford, John H

    2008-05-01

    A variety of methods have been used to make evolutionary inferences based on the spatial distribution of biological data, including reconstructing population history and detection of the geographic pattern of natural selection. This article provides an examination of geostatistical analysis, a method used widely in geology but which has not often been applied in biological anthropology. Geostatistical analysis begins with the examination of a variogram, a plot showing the relationship between a biological distance measure and the geographic distance between data points and which provides information on the extent and pattern of spatial correlation. The results of variogram analysis are used for interpolating values of unknown data points in order to construct a contour map, a process known as kriging. The methods of geostatistical analysis and discussion of potential problems are applied to a large data set of anthropometric measures for 197 populations in Ireland. The geostatistical analysis reveals two major sources of spatial variation. One pattern, seen for overall body and craniofacial size, shows an east-west cline most likely reflecting the combined effects of past population dispersal and settlement. The second pattern is seen for craniofacial height and shows an isolation by distance pattern reflecting rapid spatial changes in the midlands region of Ireland, perhaps attributable to the genetic impact of the Vikings. The correspondence of these results with other analyses of these data and the additional insights generated from variogram analysis and kriging illustrate the potential utility of geostatistical analysis in biological anthropology.

  19. Geostatistical case studies

    SciTech Connect

    Matheron, G.; Armstrong, M.

    1987-01-01

    The objective of this volume of contributed chapters is to present a series of applications of geostatistics. These range from a careful variographic analysis on uranium data, through detailed studies on geologically complex deposits, right up to the latest nonlinear methods applied to deposits with highly skewed data contributions. Applications of new techniques such as the external drift method for combining well data with seismic information have also been included. The volume emphasizes geostatistics in practice. Notation has been kept to a minimum and mathematical details have been relegated to annexes.

  20. The use of a genetic algorithm-based search strategy in geostatistics: application to a set of anisotropic piezometric head data

    NASA Astrophysics Data System (ADS)

    Abedini, M. J.; Nasseri, M.; Burn, D. H.

    2012-04-01

    In any geostatistical study, an important consideration is the choice of an appropriate, repeatable, and objective search strategy that controls the nearby samples to be included in the location-specific estimation procedure. Almost all geostatistical software available in the market puts the onus on the user to supply search strategy parameters in a heuristic manner. These parameters are solely controlled by geographical coordinates that are defined for the entire area under study, and the user has no guidance as to how to choose these parameters. The main thesis of the current study is that the selection of search strategy parameters has to be driven by data—both the spatial coordinates and the sample values—and cannot be chosen beforehand. For this purpose, a genetic-algorithm-based ordinary kriging with moving neighborhood technique is proposed. The search capability of a genetic algorithm is exploited to search the feature space for appropriate, either local or global, search strategy parameters. Radius of circle/sphere and/or radii of standard or rotated ellipse/ellipsoid are considered as the decision variables to be optimized by GA. The superiority of GA-based ordinary kriging is demonstrated through application to the Wolfcamp Aquifer piezometric head data. Assessment of numerical results showed that definition of search strategy parameters based on both geographical coordinates and sample values improves cross-validation statistics when compared with that based on geographical coordinates alone. In the case of a variable search neighborhood for each estimation point, optimization of local search strategy parameters for an elliptical support domain—the orientation of which is dictated by anisotropic axes—via GA was able to capture the dynamics of piezometric head in west Texas/New Mexico in an efficient way.

  1. Applications of geostatistics in plant nematology.

    PubMed

    Wallace, M K; Hawkins, D M

    1994-12-01

    The application of geostatistics to plant nematology was made by evaluating soil and nematode data acquired from 200 soil samples collected from the A(p) horizon of a reed canary-grass field in northern Minnesota. Geostatistical concepts relevant to nematology include semi-variogram modelling, kriging, and change of support calculations. Soil and nematode data generally followed a spherical semi-variogram model, with little random variability associated with soil data and large inherent variability for nematode data. Block kriging of soil and nematode data provided useful contour maps of the data. Change of snpport calculations indicated that most of the random variation in nematode data was due to short-range spatial variability in the nematode population densities.

  2. Robust geostatistical analysis of spatial data

    NASA Astrophysics Data System (ADS)

    Papritz, Andreas; Künsch, Hans Rudolf; Schwierz, Cornelia; Stahel, Werner A.

    2013-04-01

    Most of the geostatistical software tools rely on non-robust algorithms. This is unfortunate, because outlying observations are rather the rule than the exception, in particular in environmental data sets. Outliers affect the modelling of the large-scale spatial trend, the estimation of the spatial dependence of the residual variation and the predictions by kriging. Identifying outliers manually is cumbersome and requires expertise because one needs parameter estimates to decide which observation is a potential outlier. Moreover, inference after the rejection of some observations is problematic. A better approach is to use robust algorithms that prevent automatically that outlying observations have undue influence. Former studies on robust geostatistics focused on robust estimation of the sample variogram and ordinary kriging without external drift. Furthermore, Richardson and Welsh (1995) proposed a robustified version of (restricted) maximum likelihood ([RE]ML) estimation for the variance components of a linear mixed model, which was later used by Marchant and Lark (2007) for robust REML estimation of the variogram. We propose here a novel method for robust REML estimation of the variogram of a Gaussian random field that is possibly contaminated by independent errors from a long-tailed distribution. It is based on robustification of estimating equations for the Gaussian REML estimation (Welsh and Richardson, 1997). Besides robust estimates of the parameters of the external drift and of the variogram, the method also provides standard errors for the estimated parameters, robustified kriging predictions at both sampled and non-sampled locations and kriging variances. Apart from presenting our modelling framework, we shall present selected simulation results by which we explored the properties of the new method. This will be complemented by an analysis a data set on heavy metal contamination of the soil in the vicinity of a metal smelter. Marchant, B.P. and Lark, R

  3. Geostatistical analysis as applied to two environmental radiometric time series.

    PubMed

    Dowdall, Mark; Lind, Bjørn; Gerland, Sebastian; Rudjord, Anne Liv

    2003-03-01

    This article details the results of an investigation into the application of geostatistical data analysis to two environmental radiometric time series. The data series employed consist of 99Tc values for seaweed (Fucus vesiculosus) and seawater samples taken as part of a marine monitoring program conducted on the coast of northern Norway by the Norwegian Radiation Protection Authority. Geostatistical methods were selected in order to provide information on values of the variables at unsampled times and to investigate the temporal correlation exhibited by the data sets. This information is of use in the optimisation of future sampling schemes and for providing information on the temporal behaviour of the variables in question that may not be obtained during a cursory analysis. The results indicate a high degree of temporal correlation within the data sets, the correlation for the seawater and seaweed data being modelled with an exponential and linear function, respectively. The semi-variogram for the seawater data indicates a temporal range of correlation of approximately 395 days with no apparent random component to the overall variance structure and was described best by an exponential function. The temporal structure of the seaweed data was best modelled by a linear function with a small nugget component. Evidence of drift was present in both semi-variograms. Interpolation of the data sets using the fitted models and a simple kriging procedure were compared, using a cross-validation procedure, with simple linear interpolation. Results of this exercise indicate that, for the seawater data, the kriging procedure outperformed the simple interpolation with respect to error distribution and correlation of estimates with actual values. Using the unbounded linear model with the seaweed data produced estimates that were only marginally better than those produced by the simple interpolation.

  4. Geostatistical applications in environmental remediation

    SciTech Connect

    Stewart, R.N.; Purucker, S.T.; Lyon, B.F.

    1995-02-01

    Geostatistical analysis refers to a collection of statistical methods for addressing data that vary in space. By incorporating spatial information into the analysis, geostatistics has advantages over traditional statistical analysis for problems with a spatial context. Geostatistics has a history of success in earth science applications, and its popularity is increasing in other areas, including environmental remediation. Due to recent advances in computer technology, geostatistical algorithms can be executed at a speed comparable to many standard statistical software packages. When used responsibly, geostatistics is a systematic and defensible tool can be used in various decision frameworks, such as the Data Quality Objectives (DQO) process. At every point in the site, geostatistics can estimate both the concentration level and the probability or risk of exceeding a given value. Using these probability maps can assist in identifying clean-up zones. Given any decision threshold and an acceptable level of risk, the probability maps identify those areas that are estimated to be above or below the acceptable risk. Those areas that are above the threshold are of the most concern with regard to remediation. In addition to estimating clean-up zones, geostatistics can assist in designing cost-effective secondary sampling schemes. Those areas of the probability map with high levels of estimated uncertainty are areas where more secondary sampling should occur. In addition, geostatistics has the ability to incorporate soft data directly into the analysis. These data include historical records, a highly correlated secondary contaminant, or expert judgment. The role of geostatistics in environmental remediation is a tool that in conjunction with other methods can provide a common forum for building consensus.

  5. Determining the Significance of Item Order in Randomized Problem Sets

    ERIC Educational Resources Information Center

    Pardos, Zachary A.; Heffernan, Neil T.

    2009-01-01

    Researchers who make tutoring systems would like to know which sequences of educational content lead to the most effective learning by their students. The majority of data collected in many ITS systems consist of answers to a group of questions of a given skill often presented in a random sequence. Following work that identifies which items…

  6. An application of geostatistics and fractal geometry for reservoir characterization

    SciTech Connect

    Aasum, Y.; Kelkar, M.G. ); Gupta, S.P. )

    1991-03-01

    This paper presents an application of geostatistics and fractal geometry concepts for 2D characterization of rock properties (k and {phi}) in a dolomitic, layered-cake reservoir. The results indicate that lack of closely spaced data yield effectively random distributions of properties. Further, incorporation of geology reduces uncertainties in fractal interpolation of wellbore properties.

  7. Preferential sampling and Bayesian geostatistics: Statistical modeling and examples.

    PubMed

    Cecconi, Lorenzo; Grisotto, Laura; Catelan, Dolores; Lagazio, Corrado; Berrocal, Veronica; Biggeri, Annibale

    2016-08-01

    Preferential sampling refers to any situation in which the spatial process and the sampling locations are not stochastically independent. In this paper, we present two examples of geostatistical analysis in which the usual assumption of stochastic independence between the point process and the measurement process is violated. To account for preferential sampling, we specify a flexible and general Bayesian geostatistical model that includes a shared spatial random component. We apply the proposed model to two different case studies that allow us to highlight three different modeling and inferential aspects of geostatistical modeling under preferential sampling: (1) continuous or finite spatial sampling frame; (2) underlying causal model and relevant covariates; and (3) inferential goals related to mean prediction surface or prediction uncertainty.

  8. Random Access: The DotsPlus Tactile Font Set.

    ERIC Educational Resources Information Center

    Gardner, John

    1998-01-01

    Describes "DotsPlus," a tactile font set that allows computers to print documents in any language which uses the Roman alphabet in tactile form. DotsPlus overcomes such Braille problems as code translation, Braille numbers, exotic symbols, and symbols out of context. A new printing technology (TIGER--Tactile Graphics Embosser) produces DotsPlus…

  9. A Practical Primer on Geostatistics

    USGS Publications Warehouse

    Olea, Ricardo A.

    2009-01-01

    THE CHALLENGE Most geological phenomena are extraordinarily complex in their interrelationships and vast in their geographical extension. Ordinarily, engineers and geoscientists are faced with corporate or scientific requirements to properly prepare geological models with measurements involving a small fraction of the entire area or volume of interest. Exact description of a system such as an oil reservoir is neither feasible nor economically possible. The results are necessarily uncertain. Note that the uncertainty is not an intrinsic property of the systems; it is the result of incomplete knowledge by the observer. THE AIM OF GEOSTATISTICS The main objective of geostatistics is the characterization of spatial systems that are incompletely known, systems that are common in geology. A key difference from classical statistics is that geostatistics uses the sampling location of every measurement. Unless the measurements show spatial correlation, the application of geostatistics is pointless. Ordinarily the need for additional knowledge goes beyond a few points, which explains the display of results graphically as fishnet plots, block diagrams, and maps. GEOSTATISTICAL METHODS Geostatistics is a collection of numerical techniques for the characterization of spatial attributes using primarily two tools: probabilistic models, which are used for spatial data in a manner similar to the way in which time-series analysis characterizes temporal data, or pattern recognition techniques. The probabilistic models are used as a way to handle uncertainty in results away from sampling locations, making a radical departure from alternative approaches like inverse distance estimation methods. DIFFERENCES WITH TIME SERIES On dealing with time-series analysis, users frequently concentrate their attention on extrapolations for making forecasts. Although users of geostatistics may be interested in extrapolation, the methods work at their best interpolating. This simple difference has

  10. Patient agenda setting in respiratory outpatients: A randomized controlled trial.

    PubMed

    Early, Frances; Everden, Angharad Jt; O'Brien, Cathy M; Fagan, Petrea L; Fuld, Jonathan P

    2015-11-01

    Soliciting a patient's agenda (the reason for their visit, concerns and expectations) is fundamental to health care but if not done effectively outcomes can be adversely affected. Forms to help patients consider important issues prior to a consultation have been tested with mixed results. We hypothesized that using an agenda form would impact the extent to which patients felt their doctor discussed the issues that were important to them. Patients were randomized to receive an agenda form to complete whilst waiting or usual care. The primary outcome measure was the proportion of patients agreeing with the statement 'My doctor discussed the issues that were important to me' rated on a four-point scale. Secondary outcomes included other experience and satisfaction measures, consultation duration and patient confidence. There was no significant effect of agenda form use on primary or secondary outcomes. Post hoc exploratory analyses suggested possible differential effects for new compared to follow-up patients. There was no overall benefit from the form and a risk of detrimental impact on patient experience for some patients. There is a need for greater understanding of what works for whom in supporting patients to get the most from their consultation.

  11. Geostatistics and GIS: tools for characterizing environmental contamination.

    PubMed

    Henshaw, Shannon L; Curriero, Frank C; Shields, Timothy M; Glass, Gregory E; Strickland, Paul T; Breysse, Patrick N

    2004-08-01

    Geostatistics is a set of statistical techniques used in the analysis of georeferenced data that can be applied to environmental contamination and remediation studies. In this study, the 1,1-dichloro-2,2-bis(p-chlorophenyl)ethylene (DDE) contamination at a Superfund site in western Maryland is evaluated. Concern about the site and its future clean up has triggered interest within the community because residential development surrounds the area. Spatial statistical methods, of which geostatistics is a subset, are becoming increasingly popular, in part due to the availability of geographic information system (GIS) software in a variety of application packages. In this article, the joint use of ArcGIS software and the R statistical computing environment are demonstrated as an approach for comprehensive geostatistical analyses. The spatial regression method, kriging, is used to provide predictions of DDE levels at unsampled locations both within the site and the surrounding areas where residential development is ongoing.

  12. Model Selection for Geostatistical Models

    SciTech Connect

    Hoeting, Jennifer A.; Davis, Richard A.; Merton, Andrew A.; Thompson, Sandra E.

    2006-02-01

    We consider the problem of model selection for geospatial data. Spatial correlation is typically ignored in the selection of explanatory variables and this can influence model selection results. For example, the inclusion or exclusion of particular explanatory variables may not be apparent when spatial correlation is ignored. To address this problem, we consider the Akaike Information Criterion (AIC) as applied to a geostatistical model. We offer a heuristic derivation of the AIC in this context and provide simulation results that show that using AIC for a geostatistical model is superior to the often used approach of ignoring spatial correlation in the selection of explanatory variables. These ideas are further demonstrated via a model for lizard abundance. We also employ the principle of minimum description length (MDL) to variable selection for the geostatistical model. The effect of sampling design on the selection of explanatory covariates is also explored.

  13. Identification of Fuzzy Sets with a Class of Canonically Induced Random Sets and Some Applications.

    DTIC Science & Technology

    1980-09-15

    Vol. I. 27. C.L. Chang, "Fuzzy Topological Spaces , J. Math. Anal. & Applic. 24, 182-190 (1968). 28. B. Hutton and 1. Reilly, "Separation Axioms in...Fuzzy Topological Spaces ", Fuzzy Sets and SystemsI’ 3, 93-104 (1980). 29. L.A. Zadeh, Fuzzy Sets and Their Application to Pattern Classification and

  14. High-performance computational and geostatistical experiments for testing the capabilities of 3-d electrical tomography

    SciTech Connect

    Carle, S. F.; Daily, W. D.; Newmark, R. L.; Ramirez, A.; Tompson, A.

    1999-01-19

    This project explores the feasibility of combining geologic insight, geostatistics, and high-performance computing to analyze the capabilities of 3-D electrical resistance tomography (ERT). Geostatistical methods are used to characterize the spatial variability of geologic facies that control sub-surface variability of permeability and electrical resistivity Synthetic ERT data sets are generated from geostatistical realizations of alluvial facies architecture. The synthetic data sets enable comparison of the "truth" to inversion results, quantification of the ability to detect particular facies at particular locations, and sensitivity studies on inversion parameters

  15. Efficient geostatistical inversion of transient groundwater flow using preconditioned nonlinear conjugate gradients

    NASA Astrophysics Data System (ADS)

    Klein, Ole; Cirpka, Olaf A.; Bastian, Peter; Ippisch, Olaf

    2017-04-01

    In the geostatistical inverse problem of subsurface hydrology, continuous hydraulic parameter fields, in most cases hydraulic conductivity, are estimated from measurements of dependent variables, such as hydraulic heads, under the assumption that the parameter fields are autocorrelated random space functions. Upon discretization, the continuous fields become large parameter vectors with O (104 -107) elements. While cokriging-like inversion methods have been shown to be efficient for highly resolved parameter fields when the number of measurements is small, they require the calculation of the sensitivity of each measurement with respect to all parameters, which may become prohibitive with large sets of measured data such as those arising from transient groundwater flow. We present a Preconditioned Conjugate Gradient method for the geostatistical inverse problem, in which a single adjoint equation needs to be solved to obtain the gradient of the objective function. Using the autocovariance matrix of the parameters as preconditioning matrix, expensive multiplications with its inverse can be avoided, and the number of iterations is significantly reduced. We use a randomized spectral decomposition of the posterior covariance matrix of the parameters to perform a linearized uncertainty quantification of the parameter estimate. The feasibility of the method is tested by virtual examples of head observations in steady-state and transient groundwater flow. These synthetic tests demonstrate that transient data can reduce both parameter uncertainty and time spent conducting experiments, while the presented methods are able to handle the resulting large number of measurements.

  16. Model selection for geostatistical models.

    PubMed

    Hoeting, Jennifer A; Davis, Richard A; Merton, Andrew A; Thompson, Sandra E

    2006-02-01

    We consider the problem of model selection for geospatial data. Spatial correlation is often ignored in the selection of explanatory variables, and this can influence model selection results. For example, the importance of particular explanatory variables may not be apparent when spatial correlation is ignored. To address this problem, we consider the Akaike Information Criterion (AIC) as applied to a geostatistical model. We offer a heuristic derivation of the AIC in this context and provide simulation results that show that using AIC for a geostatistical model is superior to the often-used traditional approach of ignoring spatial correlation in the selection of explanatory variables. These ideas are further demonstrated via a model for lizard abundance. We also apply the principle of minimum description length (MDL) to variable selection for the geostatistical model. The effect of sampling design on the selection of explanatory covariates is also explored. R software to implement the geostatistical model selection methods described in this paper is available in the Supplement.

  17. Restricted spatial regression in practice: Geostatistical models, confounding, and robustness under model misspecification

    USGS Publications Warehouse

    Hanks, Ephraim M.; Schliep, Erin M.; Hooten, Mevin B.; Hoeting, Jennifer A.

    2015-01-01

    In spatial generalized linear mixed models (SGLMMs), covariates that are spatially smooth are often collinear with spatially smooth random effects. This phenomenon is known as spatial confounding and has been studied primarily in the case where the spatial support of the process being studied is discrete (e.g., areal spatial data). In this case, the most common approach suggested is restricted spatial regression (RSR) in which the spatial random effects are constrained to be orthogonal to the fixed effects. We consider spatial confounding and RSR in the geostatistical (continuous spatial support) setting. We show that RSR provides computational benefits relative to the confounded SGLMM, but that Bayesian credible intervals under RSR can be inappropriately narrow under model misspecification. We propose a posterior predictive approach to alleviating this potential problem and discuss the appropriateness of RSR in a variety of situations. We illustrate RSR and SGLMM approaches through simulation studies and an analysis of malaria frequencies in The Gambia, Africa.

  18. Examining the Missing Completely at Random Mechanism in Incomplete Data Sets: A Multiple Testing Approach

    ERIC Educational Resources Information Center

    Raykov, Tenko; Lichtenberg, Peter A.; Paulson, Daniel

    2012-01-01

    A multiple testing procedure for examining implications of the missing completely at random (MCAR) mechanism in incomplete data sets is discussed. The approach uses the false discovery rate concept and is concerned with testing group differences on a set of variables. The method can be used for ascertaining violations of MCAR and disproving this…

  19. Geostatistical inversion of moment eqations of groundwater flow at the Montalto Uffugo reasearch site (Italy)

    NASA Astrophysics Data System (ADS)

    Bianchi Janetti, E.; Riva, M.; Straface, S.; Guadagnini, A.

    2009-04-01

    We present the application of a methodology of inverting stochastic mean groundwater flow equations to characterize the spatial variability of (natural) log-transmissivity of the Montalto Uffugo research site (Italy). The methodology has been originally proposed by Hernandez et al. [2003, 2006]. It relies on a nonlinear geostatistical inverse algorithm for steady-state groundwater flow that allows estimating jointly the spatial variability of log-transmissivity, the underlying variogram and its parameters, and the variance-covariance of the estimates. Exact mean flow equations are rendered workable by means of a suitable second-order approximation (in terms of a small parameter, representing the standard deviation of the underlying random log-transmissivities). A unique feature of the method is its capability of providing estimates of prediction errors of hydraulic heads and fluxes, which are calculated a posteriori, upon solving corresponding moment equations. Prior estimates of the transmissivity variogram and its associated parameters at the test site are obtained on the basis of available electrical resistivity data. Transmissivity is parameterized geostatistically on the basis of an available measured value and a set of unknown values at discrete pilot points. While prior pilot point values are obtained by generalized kriging, posterior estimates at pilot points are obtained by calibrating mean flow against late-time values of hydraulic head collected during a pumping test. Information on hydraulic heads is obtained on the basis of self-potential signals recorded by 47 surface electrodes during the test. We explore the effectiveness of both a second-order and a lower-order closure of the mean flow equation at capturing the parameters of the estimated log-transmissivity variogram. The latter are estimated a posteriori using formal model selection criteria. Our results highlight that assimilating hydrogeophysical data within a second-order model for mean

  20. Identifying the minor set cover of dense connected bipartite graphs via random matching edge sets

    NASA Astrophysics Data System (ADS)

    Hamilton, Kathleen E.; Humble, Travis S.

    2017-04-01

    Using quantum annealing to solve an optimization problem requires minor embedding a logic graph into a known hardware graph. In an effort to reduce the complexity of the minor embedding problem, we introduce the minor set cover (MSC) of a known graph G: a subset of graph minors which contain any remaining minor of the graph as a subgraph. Any graph that can be embedded into G will be embeddable into a member of the MSC. Focusing on embedding into the hardware graph of commercially available quantum annealers, we establish the MSC for a particular known virtual hardware, which is a complete bipartite graph. We show that the complete bipartite graph K_{N,N} has a MSC of N minors, from which K_{N+1} is identified as the largest clique minor of K_{N,N}. The case of determining the largest clique minor of hardware with faults is briefly discussed but remains an open question.

  1. Geostatistical Modeling of Pore Velocity

    SciTech Connect

    Devary, J.L.; Doctor, P.G.

    1981-06-01

    A significant part of evaluating a geologic formation as a nuclear waste repository involves the modeling of contaminant transport in the surrounding media in the event the repository is breached. The commonly used contaminant transport models are deterministic. However, the spatial variability of hydrologic field parameters introduces uncertainties into contaminant transport predictions. This paper discusses the application of geostatistical techniques to the modeling of spatially varying hydrologic field parameters required as input to contaminant transport analyses. Kriging estimation techniques were applied to Hanford Reservation field data to calculate hydraulic conductivity and the ground-water potential gradients. These quantities were statistically combined to estimate the groundwater pore velocity and to characterize the pore velocity estimation error. Combining geostatistical modeling techniques with product error propagation techniques results in an effective stochastic characterization of groundwater pore velocity, a hydrologic parameter required for contaminant transport analyses.

  2. Geostatistics applied to gas reservoirs

    SciTech Connect

    Meunier, G.; Coulomb, C.; Laille, J.P. )

    1989-09-01

    The spatial distribution of many of the physical parameters connected with a gas reservoir is of primary interest to both engineers and geologists throughout the study, development, and operation of a field. It is therefore desirable for the distribution to be capable of statistical interpretation, to have a simple graphical representation, and to allow data to be entered from either two- or three-dimensional grids. To satisfy these needs while dealing with the geographical variables, new methods have been developed under the name geostatistics. This paper describes briefly the theory of geostatistics and its most recent improvements for the specific problem of subsurface description. The external-drift technique has been emphasized in particular, and in addition, four case studies related to gas reservoirs are presented.

  3. Using geostatistics to evaluate cleanup goals

    SciTech Connect

    Marcon, M.F.; Hopkins, L.P.

    1995-12-01

    Geostatistical analysis is a powerful predictive tool typically used to define spatial variability in environmental data. The information from a geostatistical analysis using kriging, a geostatistical. tool, can be taken a step further to optimize sampling location and frequency and help quantify sampling uncertainty in both the remedial investigation and remedial design at a hazardous waste site. Geostatistics were used to quantify sampling uncertainty in attainment of a risk-based cleanup goal and determine the optimal sampling frequency necessary to delineate the horizontal extent of impacted soils at a Gulf Coast waste site.

  4. Local Geostatistical Models and Big Data in Hydrological and Ecological Applications

    NASA Astrophysics Data System (ADS)

    Hristopulos, Dionissios

    2015-04-01

    The advent of the big data era creates new opportunities for environmental and ecological modelling but also presents significant challenges. The availability of remote sensing images and low-cost wireless sensor networks implies that spatiotemporal environmental data to cover larger spatial domains at higher spatial and temporal resolution for longer time windows. Handling such voluminous data presents several technical and scientific challenges. In particular, the geostatistical methods used to process spatiotemporal data need to overcome the dimensionality curse associated with the need to store and invert large covariance matrices. There are various mathematical approaches for addressing the dimensionality problem, including change of basis, dimensionality reduction, hierarchical schemes, and local approximations. We present a Stochastic Local Interaction (SLI) model that can be used to model local correlations in spatial data. SLI is a random field model suitable for data on discrete supports (i.e., regular lattices or irregular sampling grids). The degree of localization is determined by means of kernel functions and appropriate bandwidths. The strength of the correlations is determined by means of coefficients. In the "plain vanilla" version the parameter set involves scale and rigidity coefficients as well as a characteristic length. The latter determines in connection with the rigidity coefficient the correlation length of the random field. The SLI model is based on statistical field theory and extends previous research on Spartan spatial random fields [2,3] from continuum spaces to explicitly discrete supports. The SLI kernel functions employ adaptive bandwidths learned from the sampling spatial distribution [1]. The SLI precision matrix is expressed explicitly in terms of the model parameter and the kernel function. Hence, covariance matrix inversion is not necessary for parameter inference that is based on leave-one-out cross validation. This property

  5. Geostatistical inversion of transient moment equations of groundwater flow

    NASA Astrophysics Data System (ADS)

    Riva, M.; Guadagnini, A.; Neuman, S. P.; Bianchi Janetti, E.; Malama, B.

    2009-04-01

    We present a methodology for conditioning estimates of hydraulic heads and fluxes and their associated uncertainty on information about transmissivity, T , and hydraulic heads, h, collected within a randomly heterogeneous aquifer under transient conditions. Our approach is based on recursive finite-element approximations of exact nonlocal first and second conditional moment equations. We present a nonlinear geostatistical inverse algorithm for transient groundwater flow that allows estimating jointly the spatial variability of log-transmissivity, Y = ln T, the underlying variogram and its parameters, and the variance-covariance of the estimates. Log-transmissivity is parameterized geostatistically based on measured values at discrete locations and unknown values at discrete "pilot points." While prior pilot point values are obtained by generalized kriging, posterior estimates at pilot points are obtained by history matching of transient mean flow against values of hydraulic head collected during a pumping test. Parameters are then projected onto a computational grid by kriging. Prior information on hydraulic properties is included in the optimization process via a suitable regularization term which is included in the objective function to be minimized. The weight of the regularization term, hydraulic and unknown variogram parameters are then estimated by maximum likelihood calibration. The main features of the methodology are explored by means of a synthetic example. As alternative flow models we consider (a) a second-order and (b) a lower-order closure of the mean transient flow equation and assess the ability of these models at capturing the parameters of the estimated log-transmissivity variogram. With the aid of formal model selection criteria we associate each mean flow model and different sets of tested variogram parameters with a weight, or posterior probability, representing their relative degrees of likelihood. Our findings suggest that the weight of the

  6. Importance of stationarity for geostatistical assessment of environmental contamination

    SciTech Connect

    Dagdelen, K.; Turner, A.K.

    1996-12-31

    This paper describes a geostatistical case study to assess TCE contamination from multiple point sources that is migrating through the geologically complex conditions with several aquifers. The paper highlights the importance of the stationarity assumption by demonstrating how biased assessments of TCE contamination result when ordinary kriging of the data that violates stationarity assumptions. Division of the data set into more homogeneous geologic and hydrologic zones improved the accuracy of the estimates. Indicator kriging offers an alternate method for providing a stochastic model that is more appropriate for the data. Further improvement in the estimates results when indicator kriging is applied to individual subregional data sets that are based on geological considerations. This further enhances the data homogeneity and makes use of stationary model more appropriate. By combining geological and geostatistical evaluations, more realistic maps may be produced that reflect the hydrogeological environment and provide a sound basis for future investigations and remediation.

  7. Geostatistical enhancement of european hydrological predictions

    NASA Astrophysics Data System (ADS)

    Pugliese, Alessio; Castellarin, Attilio; Parajka, Juraj; Arheimer, Berit; Bagli, Stefano; Mazzoli, Paolo; Montanari, Alberto; Blöschl, Günter

    2016-04-01

    Geostatistical Enhancement of European Hydrological Prediction (GEEHP) is a research experiment developed within the EU funded SWITCH-ON project, which proposes to conduct comparative experiments in a virtual laboratory in order to share water-related information and tackle changes in the hydrosphere for operational needs (http://www.water-switch-on.eu). The main objective of GEEHP deals with the prediction of streamflow indices and signatures in ungauged basins at different spatial scales. In particular, among several possible hydrological signatures we focus in our experiment on the prediction of flow-duration curves (FDCs) along the stream-network, which has attracted an increasing scientific attention in the last decades due to the large number of practical and technical applications of the curves (e.g. hydropower potential estimation, riverine habitat suitability and ecological assessments, etc.). We apply a geostatistical procedure based on Top-kriging, which has been recently shown to be particularly reliable and easy-to-use regionalization approach, employing two different type of streamflow data: pan-European E-HYPE simulations (http://hypeweb.smhi.se/europehype) and observed daily streamflow series collected in two pilot study regions, i.e. Tyrol (merging data from Austrian and Italian stream gauging networks) and Sweden. The merger of the two study regions results in a rather large area (~450000 km2) and might be considered as a proxy for a pan-European application of the approach. In a first phase, we implement a bidirectional validation, i.e. E-HYPE catchments are set as training sites to predict FDCs at the same sites where observed data are available, and vice-versa. Such a validation procedure reveals (1) the usability of the proposed approach for predicting the FDCs over the entire river network of interest using alternatively observed data and E-HYPE simulations and (2) the accuracy of E-HYPE-based predictions of FDCs in ungauged sites. In a

  8. Auricular Therapy for Treatment of Musculoskeletal Pain in the Setting of Military Personnel: A Randomized Trial

    DTIC Science & Technology

    2015-10-01

    Award Number: W81XWH-10-2-0163 TITLE: Auricular Therapy for Treatment of Musculoskeletal Pain in the Setting of Military Personnel: A Randomized...TITLE AND SUBTITLE 5a. CONTRACT NUMBER W81XWH-10-2-0163 Auricular Therapy for Treatment of Musculoskeletal Pain in the Setting of Military Personnel: A...SUBJET TERMS Auricular Therapy; Musculoskeletal Pain 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME OF

  9. Using geostatistics to estimate coal reserves

    SciTech Connect

    Royle, A.G.

    1982-09-01

    Geostatistics have, in the past, been used for evaluating metallic ore reserves. Today they are finding more use for coal reserve determination. An example is given to show how geostatistics can be used to estimate mean thickness, sulphur content and other data from in situ coal. (3 refs.)

  10. On the geostatistical characterization of hierarchical media

    NASA Astrophysics Data System (ADS)

    Neuman, Shlomo P.; Riva, Monica; Guadagnini, Alberto

    2008-02-01

    The subsurface consists of porous and fractured materials exhibiting a hierarchical geologic structure, which gives rise to systematic and random spatial and directional variations in hydraulic and transport properties on a multiplicity of scales. Traditional geostatistical moment analysis allows one to infer the spatial covariance structure of such hierarchical, multiscale geologic materials on the basis of numerous measurements on a given support scale across a domain or "window" of a given length scale. The resultant sample variogram often appears to fit a stationary variogram model with constant variance (sill) and integral (spatial correlation) scale. In fact, some authors, who recognize that hierarchical sedimentary architecture and associated log hydraulic conductivity fields tend to be nonstationary, nevertheless associate them with stationary "exponential-like" transition probabilities and variograms, respectively, the latter being a consequence of the former. We propose that (1) the apparent ability of stationary spatial statistics to characterize the covariance structure of nonstationary hierarchical media is an artifact stemming from the finite size of the windows within which geologic and hydrologic variables are ubiquitously sampled, and (2) the artifact is eliminated upon characterizing the covariance structure of such media with the aid of truncated power variograms, which represent stationary random fields obtained upon sampling a nonstationary fractal over finite windows. To support our opinion, we note that truncated power variograms arise formally when a hierarchical medium is sampled jointly across all geologic categories and scales within a window; cite direct evidence that geostatistical parameters (variance and integral scale) inferred on the basis of traditional variograms vary systematically with support and window scales; demonstrate the ability of truncated power models to capture these variations in terms of a few scaling parameters

  11. Reservoir property grids improve with geostatistics

    SciTech Connect

    Vogt, J. . E and P Technology Dept.)

    1993-09-01

    Visualization software, reservoir simulators and many other E and P software applications need reservoir property grids as input. Using geostatistics, as compared to other gridding methods, to produce these grids leads to the best output from the software programs. For the purpose stated herein, geostatistics is simply two types of gridding methods. Mathematically, these methods are based on minimizing or duplicating certain statistical properties of the input data. One geostatical method, called kriging, is used when the highest possible point-by-point accuracy is desired. The other method, called conditional simulation, is used when one wants statistics and texture of the resulting grid to be the same as for the input data. In the following discussion, each method is explained, compared to other gridding methods, and illustrated through example applications. Proper use of geostatistical data in flow simulations, use of geostatistical data for history matching, and situations where geostatistics has no significant advantage over other methods, also will be covered.

  12. Random forests-based differential analysis of gene sets for gene expression data.

    PubMed

    Hsueh, Huey-Miin; Zhou, Da-Wei; Tsai, Chen-An

    2013-04-10

    In DNA microarray studies, gene-set analysis (GSA) has become the focus of gene expression data analysis. GSA utilizes the gene expression profiles of functionally related gene sets in Gene Ontology (GO) categories or priori-defined biological classes to assess the significance of gene sets associated with clinical outcomes or phenotypes. Many statistical approaches have been proposed to determine whether such functionally related gene sets express differentially (enrichment and/or deletion) in variations of phenotypes. However, little attention has been given to the discriminatory power of gene sets and classification of patients. In this study, we propose a method of gene set analysis, in which gene sets are used to develop classifications of patients based on the Random Forest (RF) algorithm. The corresponding empirical p-value of an observed out-of-bag (OOB) error rate of the classifier is introduced to identify differentially expressed gene sets using an adequate resampling method. In addition, we discuss the impacts and correlations of genes within each gene set based on the measures of variable importance in the RF algorithm. Significant classifications are reported and visualized together with the underlying gene sets and their contribution to the phenotypes of interest. Numerical studies using both synthesized data and a series of publicly available gene expression data sets are conducted to evaluate the performance of the proposed methods. Compared with other hypothesis testing approaches, our proposed methods are reliable and successful in identifying enriched gene sets and in discovering the contributions of genes within a gene set. The classification results of identified gene sets can provide an valuable alternative to gene set testing to reveal the unknown, biologically relevant classes of samples or patients. In summary, our proposed method allows one to simultaneously assess the discriminatory ability of gene sets and the importance of genes for

  13. A more complete task-set reconfiguration in random than in predictable task switch.

    PubMed

    Tornay, F J; Milán, E G

    2001-08-01

    Three experiments are presented that compare the cost found when switching from one task to another in two different conditions. In one of them, the tasks switch in predictable sequences. In the other condition, the tasks alternate at random. A smaller time cost is found in the random-switch condition when enough preparation time is allowed. Such an effect is due to the random-switch cost continuing to decrease with preparation time after the predictable-switch cost has reached an asymptote. Although the relationship between number of repetitions of one task and time cost is different in the random- and the predictable-switch conditions, only the latter shows the presence of an "exogenous" component of cost. The implications of this finding are discussed in relationship with the usual distinction between an endogenous component of switch cost that is affected by preparation time and another exogenous, residual component (e.g., Rogers & Monsell, 1995). It is proposed that a different kind of task-set preparation is at work when tasks alternate at random.

  14. [Geostatistical modeling of Ascaris lumbricoides infection].

    PubMed

    Fortes, Bruno de Paula Menezes Drumond; Ortiz Valencia, Luis Iván; Ribeiro, Simone do Vale; Medronho, Roberto de Andrade

    2004-01-01

    The following study intends to model the spatial distribution of ascariasis, through the use of geoprocessing and geostatistic analysis. The database used in the study was taken from the PAISQUA project, including a coproparasitologic and domiciliary survey, conducted in 19 selected census tracts of Rio de Janeiro State, Brazil, randomly selecting a group of 1,550 children aged 1 to 9 years old plotting them in their respective domicile's centroids. Risk maps of Ascaris lumbricoides were generated by indicator kriging. The estimated and observed values from the cross-validation were compared using a ROC curve. An isotropic spherical semivariogram model with a range of 30m and nugget effect of 50% was employed in ordinary indicator kriging to create a map of probability of A. lumbricoides infection. The area under the ROC curve indicated a significant global accuracy. The occurrence of disease could be estimated in the study area, and a risk map was elaborated through the use ordinary kriging. The spatial statistics analysis has proven itself adequate for predicting the occurrence of ascariasis, unrestricted to the regions political boundaries.

  15. Reservoir studies with geostatistics to forecast performance

    SciTech Connect

    Tang, R.W.; Behrens, R.A.; Emanuel, A.S. )

    1991-05-01

    In this paper example geostatistics and streamtube applications are presented for waterflood and CO{sub 2} flood in two low-permeability sandstone reservoirs. Thy hybrid approach of combining fine vertical resolution in cross-sectional models with streamtubes resulted in models that showed water channeling and provided realistic performance estimates. Results indicate that the combination of detailed geostatistical cross sections and fine-grid streamtube models offers a systematic approach for realistic performance forecasts.

  16. Efficient estimation in two-stage randomized clinical trials using ranked sets.

    PubMed

    Hossain, Syed Shahadat; Awan, Nabil

    2016-12-14

    Clinical trials designed for survival probability estimation of different treatment policies for chronic diseases like cancer, leukemia, and schizophrenia usually need randomization of treatments in two stages. Since complete remission is rare for these diseases, initially an induction therapy is given for patient's remission. Further treatment, which is often an expensive maintenance therapy, is administered only for the patients with remission. If the maintenance therapy is so expensive that the cost of the trial inflates, only a simple random sample of patients will be treated with the expensive maintenance due to budget constraint. In this article, we have implemented a design using ranked sets instead of simple randomization in the second stage and obtained an unbiased estimator of the overall survival distribution for a particular treatment combination. Through simulation studies under different conditions, we have found that the design we developed based on ranked sets gives an unbiased estimate of the population survival probability which is more efficient than the estimate obtained by the usual design.

  17. [Spatial distribution pattern of Chilo suppressalis analyzed by classical method and geostatistics].

    PubMed

    Yuan, Zheming; Fu, Wei; Li, Fangyi

    2004-04-01

    Two original samples of Chilo suppressalis and their grid, random and sequence samples were analyzed by classical method and geostatistics to characterize the spatial distribution pattern of C. suppressalis. The limitations of spatial distribution analysis with classical method, especially influenced by the original position of grid, were summarized rather completely. On the contrary, geostatistics characterized well the spatial distribution pattern, congregation intensity and spatial heterogeneity of C. suppressalis. According to geostatistics, the population was up to Poisson distribution in low density. As for higher density population, its distribution was up to aggregative, and the aggregation intensity and dependence range were 0.1056 and 193 cm, respectively. Spatial heterogeneity was also found in the higher density population. Its spatial correlativity in line direction was more closely than that in row direction, and the dependence ranges in line and row direction were 115 and 264 cm, respectively.

  18. A Random Finite Set Approach to Space Junk Tracking and Identification

    DTIC Science & Technology

    2014-09-03

    Jah, “An AEGIS-FISST integrated detection and tracking approach to Space Situational Awareness,” Proc. Int. Conf. Information Fusion, pp. 2065-2072...Final 3. DATES COVERED (From - To) 31 Jan 13 – 29 Apr 14 4. TITLE AND SUBTITLE A Random Finite Set Approach to Space Junk Tracking and...Identification 5a. CONTRACT NUMBER FA2386-13-1-4010 5b. GRANT NUMBER Grant AOARD-134010 5c. PROGRAM ELEMENT NUMBER 61102F 6. AUTHOR(S

  19. Reducing spatial uncertainty in climatic maps through geostatistical analysis

    NASA Astrophysics Data System (ADS)

    Pesquer, Lluís; Ninyerola, Miquel; Pons, Xavier

    2014-05-01

    ), applying different interpolation methods/parameters are shown: RMS (mm) error values obtained from the independent test set (20 % of the samples) follow, according to this order: IDW (exponent=1.5, 2, 2.5, 3) / SPT (tension=100, 125, 150, 175, 200) / OK. LOOCV: 92.5; 80.2; 74.2; 72.3 / 181.6; 90.6; 75.7; 71.1; 69.4; 68.8 RS: 101.2; 89.6; 83.9; 81.9 / 115.1; 92.4; 84.0; 81.4; 80.9; 81.1 / 81.1 EU: 57.4; 51.3; 53.1; 55.5 / 59.1; 57.1; 55.9; 55.0; 54.3 / 51.8 A3D: 48.3; 49.8; 52.5; 62.2 / 57.1; 54.4; 52.5; 51.2; 50.2 / 49.7 To study these results, a geostatistical analysis of uncertainty has been done. Main results: variogram analysis of the error (using the test set) shows that the total sill is reduced (50% EU, 60% A3D) when using the two new approaches, while the spatialized standard deviation model calculated from the OK shows significantly lower values when compared to the RS. In conclusion, A3D and EU highly improve LOOCV and RS, whereas A3D slightly improves EU. Also, LOOCV only shows slightly better results than RS, suggesting that non-random-split increases the power of both fitting-test steps. * Ninyerola, Pons, Roure. A methodological approach of climatological modelling of air temperature and precipitation through GIS techniques. IJC, 2000; 20:1823-1841.

  20. Characterizing gene sets using discriminative random walks with restart on heterogeneous biological networks

    PubMed Central

    Blatti, Charles; Sinha, Saurabh

    2016-01-01

    Motivation: Analysis of co-expressed gene sets typically involves testing for enrichment of different annotations or ‘properties’ such as biological processes, pathways, transcription factor binding sites, etc., one property at a time. This common approach ignores any known relationships among the properties or the genes themselves. It is believed that known biological relationships among genes and their many properties may be exploited to more accurately reveal commonalities of a gene set. Previous work has sought to achieve this by building biological networks that combine multiple types of gene–gene or gene–property relationships, and performing network analysis to identify other genes and properties most relevant to a given gene set. Most existing network-based approaches for recognizing genes or annotations relevant to a given gene set collapse information about different properties to simplify (homogenize) the networks. Results: We present a network-based method for ranking genes or properties related to a given gene set. Such related genes or properties are identified from among the nodes of a large, heterogeneous network of biological information. Our method involves a random walk with restarts, performed on an initial network with multiple node and edge types that preserve more of the original, specific property information than current methods that operate on homogeneous networks. In this first stage of our algorithm, we find the properties that are the most relevant to the given gene set and extract a subnetwork of the original network, comprising only these relevant properties. We then re-rank genes by their similarity to the given gene set, based on a second random walk with restarts, performed on the above subnetwork. We demonstrate the effectiveness of this algorithm for ranking genes related to Drosophila embryonic development and aggressive responses in the brains of social animals. Availability and Implementation: DRaWR was implemented as

  1. An Efficient ERP-Based Brain-Computer Interface Using Random Set Presentation and Face Familiarity

    PubMed Central

    Müller, Klaus-Robert; Lee, Seong-Whan

    2014-01-01

    Event-related potential (ERP)-based P300 spellers are commonly used in the field of brain-computer interfaces as an alternative channel of communication for people with severe neuro-muscular diseases. This study introduces a novel P300 based brain-computer interface (BCI) stimulus paradigm using a random set presentation pattern and exploiting the effects of face familiarity. The effect of face familiarity is widely studied in the cognitive neurosciences and has recently been addressed for the purpose of BCI. In this study we compare P300-based BCI performances of a conventional row-column (RC)-based paradigm with our approach that combines a random set presentation paradigm with (non-) self-face stimuli. Our experimental results indicate stronger deflections of the ERPs in response to face stimuli, which are further enhanced when using the self-face images, and thereby improving P300-based spelling performance. This lead to a significant reduction of stimulus sequences required for correct character classification. These findings demonstrate a promising new approach for improving the speed and thus fluency of BCI-enhanced communication with the widely used P300-based BCI setup. PMID:25384045

  2. Randomized Trial of Plastic Bags to Prevent Term Neonatal Hypothermia in a Resource-Poor Setting

    PubMed Central

    Belsches, Theodore C.; Tilly, Alyssa E.; Miller, Tonya R.; Kambeyanda, Rohan H.; Leadford, Alicia; Manasyan, Albert; Chomba, Elwyn; Ramani, Manimaran; Ambalavanan, Namasivayam

    2013-01-01

    OBJECTIVES: Term infants in resource-poor settings frequently develop hypothermia during the first hours after birth. Plastic bags or wraps are a low-cost intervention for the prevention of hypothermia in preterm and low birth weight infants that may also be effective in term infants. Our objective was to test the hypothesis that placement of term neonates in plastic bags at birth reduces hypothermia at 1 hour after birth in a resource-poor hospital. METHODS: This parallel-group randomized controlled trial was conducted at University Teaching Hospital, the tertiary referral center in Zambia. Inborn neonates with both a gestational age ≥37 weeks and a birth weight ≥2500 g were randomized 1:1 to either a standard thermoregulation protocol or to a standard thermoregulation protocol with placement of the torso and lower extremities inside a plastic bag within 10 minutes after birth. The primary outcome was hypothermia (<36.5°C axillary temperature) at 1 hour after birth. RESULTS: Neonates randomized to plastic bag (n = 135) or to standard thermoregulation care (n = 136) had similar baseline characteristics (birth weight, gestational age, gender, and baseline temperature). Neonates in the plastic bag group had a lower rate of hypothermia (60% vs 73%, risk ratio 0.76, confidence interval 0.60–0.96, P = .026) and a higher axillary temperature (36.4 ± 0.5°C vs 36.2 ± 0.7°C, P < .001) at 1 hour after birth compared with infants receiving standard care. CONCLUSIONS: Placement in a plastic bag at birth reduced the incidence of hypothermia at 1 hour after birth in term neonates born in a resource-poor setting, but most neonates remained hypothermic. PMID:23979082

  3. Model-Based Geostatistical Mapping of the Prevalence of Onchocerca volvulus in West Africa

    PubMed Central

    O’Hanlon, Simon J.; Slater, Hannah C.; Cheke, Robert A.; Boatin, Boakye A.; Coffeng, Luc E.; Pion, Sébastien D. S.; Boussinesq, Michel; Zouré, Honorat G. M.; Stolk, Wilma A.; Basáñez, María-Gloria

    2016-01-01

    Background The initial endemicity (pre-control prevalence) of onchocerciasis has been shown to be an important determinant of the feasibility of elimination by mass ivermectin distribution. We present the first geostatistical map of microfilarial prevalence in the former Onchocerciasis Control Programme in West Africa (OCP) before commencement of antivectorial and antiparasitic interventions. Methods and Findings Pre-control microfilarial prevalence data from 737 villages across the 11 constituent countries in the OCP epidemiological database were used as ground-truth data. These 737 data points, plus a set of statistically selected environmental covariates, were used in a Bayesian model-based geostatistical (B-MBG) approach to generate a continuous surface (at pixel resolution of 5 km x 5km) of microfilarial prevalence in West Africa prior to the commencement of the OCP. Uncertainty in model predictions was measured using a suite of validation statistics, performed on bootstrap samples of held-out validation data. The mean Pearson’s correlation between observed and estimated prevalence at validation locations was 0.693; the mean prediction error (average difference between observed and estimated values) was 0.77%, and the mean absolute prediction error (average magnitude of difference between observed and estimated values) was 12.2%. Within OCP boundaries, 17.8 million people were deemed to have been at risk, 7.55 million to have been infected, and mean microfilarial prevalence to have been 45% (range: 2–90%) in 1975. Conclusions and Significance This is the first map of initial onchocerciasis prevalence in West Africa using B-MBG. Important environmental predictors of infection prevalence were identified and used in a model out-performing those without spatial random effects or environmental covariates. Results may be compared with recent epidemiological mapping efforts to find areas of persisting transmission. These methods may be extended to areas where

  4. Book Review Geostatistical Analysis of Compositional Data

    SciTech Connect

    Carle, S F

    2007-03-26

    Compositional data are represented as vector variables with individual vector components ranging between zero and a positive maximum value representing a constant sum constraint, usually unity (or 100 percent). The earth sciences are flooded with spatial distributions of compositional data, such as concentrations of major ion constituents in natural waters (e.g. mole, mass, or volume fractions), mineral percentages, ore grades, or proportions of mutually exclusive categories (e.g. a water-oil-rock system). While geostatistical techniques have become popular in earth science applications since the 1970s, very little attention has been paid to the unique mathematical properties of geostatistical formulations involving compositional variables. The book 'Geostatistical Analysis of Compositional Data' by Vera Pawlowsky-Glahn and Ricardo Olea (Oxford University Press, 2004), unlike any previous book on geostatistics, directly confronts the mathematical difficulties inherent to applying geostatistics to compositional variables. The book righteously justifies itself with prodigious referencing to previous work addressing nonsensical ranges of estimated values and error, spurious correlation, and singular cross-covariance matrices.

  5. Context-free pairs of groups II — Cuts, tree sets, and random walks

    PubMed Central

    Woess, Wolfgang

    2012-01-01

    This is a continuation of the study, begun by Ceccherini-Silberstein and Woess (2009) [5], of context-free pairs of groups and the related context-free graphs in the sense of Muller and Schupp (1985) [22]. The graphs under consideration are Schreier graphs of a subgroup of some finitely generated group, and context-freeness relates to a tree-like structure of those graphs. Instead of the cones of Muller and Schupp (1985) [22] (connected components resulting from deletion of finite balls with respect to the graph metric), a more general approach to context-free graphs is proposed via tree sets consisting of cuts of the graph, and associated structure trees. The existence of tree sets with certain “good” properties is studied. With a tree set, a natural context-free grammar is associated. These investigations of the structure of context free pairs, resp. graphs are then applied to study random walk asymptotics via complex analysis. In particular, a complete proof of the local limit theorem for return probabilities on any virtually free group is given, as well as on Schreier graphs of a finitely generated subgoup of a free group. This extends, respectively completes, the significant work of Lalley (1993, 2001) [18,20]. PMID:22267873

  6. GEOSTATISTICS FOR WASTE MANAGEMENT: A USER'S MANUAL FOR THE GEOPACK (VERSION 1.0) GEOSTATISTICAL SOFTWARE SYSTEM

    EPA Science Inventory

    GEOPACK, a comprehensive user-friendly geostatistical software system, was developed to help in the analysis of spatially correlated data. The software system was developed to be used by scientists, engineers, regulators, etc., with little experience in geostatistical techniques...

  7. The Ethics of Randomized Controlled Trials in Social Settings: Can Social Trials Be Scientifically Promising and Must There Be Equipoise?

    ERIC Educational Resources Information Center

    Fives, Allyn; Russell, Daniel W.; Canavan, John; Lyons, Rena; Eaton, Patricia; Devaney, Carmel; Kearns, Norean; O'Brien, Aoife

    2015-01-01

    In a randomized controlled trial (RCT), treatments are assigned randomly and treatments are withheld from participants. Is it ethically permissible to conduct an RCT in a social setting? This paper addresses two conditions for justifying RCTs: that there should be a state of equipoise and that the trial should be scientifically promising.…

  8. Quantifying natural delta variability using a multiple-point geostatistics prior uncertainty model

    NASA Astrophysics Data System (ADS)

    Scheidt, Céline; Fernandes, Anjali M.; Paola, Chris; Caers, Jef

    2016-10-01

    We address the question of quantifying uncertainty associated with autogenic pattern variability in a channelized transport system by means of a modern geostatistical method. This question has considerable relevance for practical subsurface applications as well, particularly those related to uncertainty quantification relying on Bayesian approaches. Specifically, we show how the autogenic variability in a laboratory experiment can be represented and reproduced by a multiple-point geostatistical prior uncertainty model. The latter geostatistical method requires selection of a limited set of training images from which a possibly infinite set of geostatistical model realizations, mimicking the training image patterns, can be generated. To that end, we investigate two methods to determine how many training images and what training images should be provided to reproduce natural autogenic variability. The first method relies on distance-based clustering of overhead snapshots of the experiment; the second method relies on a rate of change quantification by means of a computer vision algorithm termed the demon algorithm. We show quantitatively that with either training image selection method, we can statistically reproduce the natural variability of the delta formed in the experiment. In addition, we study the nature of the patterns represented in the set of training images as a representation of the "eigenpatterns" of the natural system. The eigenpattern in the training image sets display patterns consistent with previous physical interpretations of the fundamental modes of this type of delta system: a highly channelized, incisional mode; a poorly channelized, depositional mode; and an intermediate mode between the two.

  9. Adaptive geostatistical sampling enables efficient identification of malaria hotspots in repeated cross-sectional surveys in rural Malawi

    PubMed Central

    Chipeta, Michael G.; McCann, Robert S.; Phiri, Kamija S.; van Vugt, Michèle; Takken, Willem; Diggle, Peter; Terlouw, Anja D.

    2017-01-01

    Introduction In the context of malaria elimination, interventions will need to target high burden areas to further reduce transmission. Current tools to monitor and report disease burden lack the capacity to continuously detect fine-scale spatial and temporal variations of disease distribution exhibited by malaria. These tools use random sampling techniques that are inefficient for capturing underlying heterogeneity while health facility data in resource-limited settings are inaccurate. Continuous community surveys of malaria burden provide real-time results of local spatio-temporal variation. Adaptive geostatistical design (AGD) improves prediction of outcome of interest compared to current random sampling techniques. We present findings of continuous malaria prevalence surveys using an adaptive sampling design. Methods We conducted repeated cross sectional surveys guided by an adaptive sampling design to monitor the prevalence of malaria parasitaemia and anaemia in children below five years old in the communities living around Majete Wildlife Reserve in Chikwawa district, Southern Malawi. AGD sampling uses previously collected data to sample new locations of high prediction variance or, where prediction exceeds a set threshold. We fitted a geostatistical model to predict malaria prevalence in the area. Findings We conducted five rounds of sampling, and tested 876 children aged 6–59 months from 1377 households over a 12-month period. Malaria prevalence prediction maps showed spatial heterogeneity and presence of hotspots—where predicted malaria prevalence was above 30%; predictors of malaria included age, socio-economic status and ownership of insecticide-treated mosquito nets. Conclusions Continuous malaria prevalence surveys using adaptive sampling increased malaria prevalence prediction accuracy. Results from the surveys were readily available after data collection. The tool can assist local managers to target malaria control interventions in areas with the

  10. Spatial continuity measures for probabilistic and deterministic geostatistics

    SciTech Connect

    Isaaks, E.H.; Srivastava, R.M.

    1988-05-01

    Geostatistics has traditionally used a probabilistic framework, one in which expected values or ensemble averages are of primary importance. The less familiar deterministic framework views geostatistical problems in terms of spatial integrals. This paper outlines the two frameworks and examines the issue of which spatial continuity measure, the covariance C(h) or the variogram ..sigma..(h), is appropriate for each framework. Although C(h) and ..sigma..(h) were defined originally in terms of spatial integrals, the convenience of probabilistic notation made the expected value definitions more common. These now classical expected value definitions entail a linear relationship between C(h) and ..sigma..(h); the spatial integral definitions do not. In a probabilistic framework, where available sample information is extrapolated to domains other than the one which was sampled, the expected value definitions are appropriate; furthermore, within a probabilistic framework, reasons exist for preferring the variogram to the covariance function. In a deterministic framework, where available sample information is interpolated within the same domain, the spatial integral definitions are appropriate and no reasons are known for preferring the variogram. A case study on a Wiener-Levy process demonstrates differences between the two frameworks and shows that, for most estimation problems, the deterministic viewpoint is more appropriate. Several case studies on real data sets reveal that the sample covariance function reflects the character of spatial continuity better than the sample variogram. From both theoretical and practical considerations, clearly for most geostatistical problems, direct estimation of the covariance is better than the traditional variogram approach.

  11. Geostatistics for environmental and geotechnical applications

    SciTech Connect

    Rouhani, S.; Srivastava, R.M.; Desbarats, A.J.; Cromer, M.V.; Johnson, A.I.

    1996-12-31

    This conference was held January 26--27, 1995 in Phoenix, Arizona. The purpose of this conference was to provide a multidisciplinary forum for exchange of state-of-the-art information on the technology of geostatistics and its applicability for environmental studies, especially site characterization. Individual papers have been processed separately for inclusion in the appropriate data bases.

  12. Cognitive remediation for individuals with psychosis in a supported education setting: a randomized controlled trial.

    PubMed

    Kidd, Sean A; Kaur, Jaswant; Virdee, Gursharan; George, Tony P; McKenzie, Kwame; Herman, Yarissa

    2014-08-01

    Cognitive remediation (CR) has demonstrated good outcomes when paired with supported employment, however little is known about its effectiveness when integrated into a supported education program. This randomized controlled trial examined the effectiveness of integrating CR within a supported education program compared with supported education without CR. Thirty-seven students with psychosis were recruited into the study in the 2012 academic year. Academic functioning, cognition, self-esteem, and symptomatology were assessed at baseline, at 4months following the first academic term in which CR was provided, and at 8months assessing maintenance of gains. The treatment group demonstrated better retention in the academic program and a trend of improvement across a range of academic functional domains. While both treatment and control groups showed improvement in cognitive measures, the outcomes were not augmented by CR training. CR was also associated with significant and sustained improvements in self esteem. Further research, investigating specific intervention components is required to clarify the mixed findings regarding the effectiveness of CR in an education setting.

  13. The Effect of Distributed Practice in Undergraduate Statistics Homework Sets: A Randomized Trial

    ERIC Educational Resources Information Center

    Crissinger, Bryan R.

    2015-01-01

    Most homework sets in statistics courses are constructed so that students concentrate or "mass" their practice on a certain topic in one problem set. Distributed practice homework sets include review problems in each set so that practice on a topic is distributed across problem sets. There is a body of research that points to the…

  14. Comparative Effectiveness of Goal Setting in Diabetes Mellitus Group Clinics:Randomized Clinical Trial

    PubMed Central

    Naik, Aanand D.; Palmer, Nynikka; Petersen, Nancy J.; Street, Richard L.; Rao, Radha; Suarez-Almazor, Maria; Haidet, Paul

    2011-01-01

    Background Diabetes group clinics can effectively control hypertension, but data to support glycemic control is equivocal. This study evaluated the comparative effectiveness of two diabetes group clinic interventions on glycosolated hemoglobin (HbA1c) levels in primary care. Methods Participants (n = 87) were recruited from a diabetes registry of a single regional VA medical center to participate in an open, randomized comparative effectiveness study. Two primary care based diabetes group interventions of three months duration were compared. Empowering Patients in Care (EPIC) was a clinician-led, patient-centered group clinic consisting of four sessions on setting self-management action plans (diet, exercise, home monitoring, medications, etc.) and communicating about progress with action plans. The comparison intervention consisted of group education sessions with a diabetes educator and dietician followed by an additional visit with one’s primary care provider. HbA1c levels were compared post-intervention and at one-year follow-up. Results Participants in the EPIC intervention had significantly greater improvements in HbA1c levels immediately following the active intervention (8.86 to 8.04 vs. 8.74 to 8.70, mean [SD] between-group difference 0.67±1.3, P=.03) and these differences persisted at 1 year follow-up (.59±1.4, P=.05). A repeated measures analysis using all study time points found a significant time-by-treatment interaction effect on HbA1c levels favoring the EPIC intervention (F(2,85) =3.55, P= .03). The effect of the time-by-treatment interaction appears to be partially mediated by diabetes self-efficacy (F(1,85) =10.39, P= .002). Conclusions Primary care based diabetes group clinics that include structured goal-setting approaches to self-management can significantly improve HbA1c levels post-intervention and maintain improvements for 1-year. Trial registration ClinicalTrials.gov Identifier: NCT00481286 PMID:21403042

  15. Mixed-point geostatistical simulation: A combination of two- and multiple-point geostatistics

    NASA Astrophysics Data System (ADS)

    Cordua, Knud Skou; Hansen, Thomas Mejer; Gulbrandsen, Mats Lundh; Barnes, Christophe; Mosegaard, Klaus

    2016-09-01

    Multiple-point-based geostatistical methods are used to model complex geological structures. However, a training image containing the characteristic patterns of the Earth model has to be provided. If no training image is available, two-point (i.e., covariance-based) geostatistical methods are typically applied instead because these methods provide fewer constraints on the Earth model. This study is motivated by the case where 1-D vertical training images are available through borehole logs, whereas little or no information about horizontal dependencies exists. This problem is solved by developing theory that makes it possible to combine information from multiple- and two-point geostatistics for different directions, leading to a mixed-point geostatistical model. An example of combining information from the multiple-point-based single normal equation simulation algorithm and two-point-based sequential indicator simulation algorithm is provided. The mixed-point geostatistical model is used for conditional sequential simulation based on vertical training images from five borehole logs and a range parameter describing the horizontal dependencies.

  16. Implementing Random Assignment: A Computer-Based Approach in a Field Experimental Setting.

    ERIC Educational Resources Information Center

    Dobson, Douglas; Cook, Thomas J.

    1979-01-01

    A major problem in social science research is that of successfully carrying out the random assignment of persons to experimental and control groups. In this study a computer-based random assignment procedure operated successfully on a weekly basis for 17 consecutive weeks in a program serving over 360 ex-offenders. (CTM)

  17. Multiple Point Geostatistics for automated landform mapping

    NASA Astrophysics Data System (ADS)

    Karssenberg, D.; Vannametee, E.; Babel, L.; Schuur, J.; Hendriks, M.; Bierkens, M. F.

    2011-12-01

    Land-surface processes are often studied at the level of elementary landform units, e.g. geomorphological units. To avoid expensive and difficult field surveys and to ensure a consistent mapping scheme, automated derivation of these units is desirable. However, automated classification based on two-point statistics of topographical attributes (e.g. semivarigram) is inadequate in reproducing complex, curvilinear landform patterns. Therefore, the spatial structure and configuration of terrain characteristics suitable for landform classification should be based on statistics from multiple points. In this study, a generic automated landform classification routine is developed which is based on Multiple Point Geostatistics (MPG) using information from a field map of geomorphology in a training area and a gridded Digital Elevation Model (DEM). Focus is on classification of geomorphologic units; e.g. alluvial fan, river terrace. The approach is evaluated using data from the French Alps. In the first procedural step, spatial statistics of the geomorphologic units are retrieved from a training data set, consisting of a digital elevation model and a geomorphologic map, created using field observations and 37.5 x 37.5 m2 cells. For each grid cell in the training data set, the geomorphological unit of the grid cell and a set of topographical attributes (i.e. a pattern) of the grid cell is stored in a frequency database. The set of topographical attributes stored is chosen such that it represents criteria used in field mapping. These are, for instance, topographical slope gradient, upstream area, or geomorphological units mapped in the neighborhood of the cell. Continuous information (e.g. slope) is converted to categorical data (slope class), which is required in the MPG approach. The second step is to use the knowledge stored in the frequency database for mapping. The algorithm reads a set of attribute classes from a classification target cell and its surrounding cells taking

  18. A PC-Windows-Based program for geostatistical modeling application

    SciTech Connect

    Wu, G.G.; Yang, A.P.

    1994-12-31

    This paper describes a technically advanced, user-friendly, PC-Windows{sup TM} based reservoir simulation tool (SIMTOOLS) that allows construction of realistic reservoir models using a geostatistical approach. This PC-Windows based product has three application tools: Digitizing, mapping, and geostatistics. It has been designed primarily to enable reservoir engineers to apply the geostatistical gridding technique in mapping and reservoir simulation practices.

  19. Preventing Depression among Early Adolescents in the Primary Care Setting: A Randomized Controlled Study of the Penn Resiliency Program

    ERIC Educational Resources Information Center

    Gillham, Jane E.; Hamilton, John; Freres, Derek R.; Patton, Ken; Gallop, Robert

    2006-01-01

    This study evaluated the Penn Resiliency Program's effectiveness in preventing depression when delivered by therapists in a primary care setting. Two-hundred and seventy-one 11- and 12-year-olds, with elevated depressive symptoms, were randomized to PRP or usual care. Over the 2-year follow-up, PRP improved explanatory style for positive events.…

  20. Implementing Randomized Controlled Trial Studies in Afterschool Settings: The State of the Field. Afterschool Research Brief. Issue No. 1

    ERIC Educational Resources Information Center

    Vaden-Kiernan, Michael; Jones, Debra Hughes; Rudo, Zena

    2008-01-01

    SEDL is providing analytic and technical support to three large-scale randomized controlled trials assessing the efficacy of promising literacy curriculum in afterschool settings on student academic achievement. In the field of educational research, competition among research organizations and researchers can often impede collaborative efforts in…

  1. Observation of Lévy distribution and replica symmetry breaking in random lasers from a single set of measurements

    PubMed Central

    Gomes, Anderson S. L.; Raposo, Ernesto P.; Moura, André L.; Fewo, Serge I.; Pincheira, Pablo I. R.; Jerez, Vladimir; Maia, Lauro J. Q.; de Araújo, Cid B.

    2016-01-01

    Random lasers have been recently exploited as a photonic platform for studies of complex systems. This cross-disciplinary approach opened up new important avenues for the understanding of random-laser behavior, including Lévy-type distributions of strong intensity fluctuations and phase transitions to a photonic spin-glass phase. In this work, we employ the Nd:YBO random laser system to unveil, from a single set of measurements, the physical origin of the complex correspondence between the Lévy fluctuation regime and the replica-symmetry-breaking transition to the spin-glass phase. A novel unexpected finding is also reported: the trend to suppress the spin-glass behavior for high excitation pulse energies. The present description from first principles of this correspondence unfolds new possibilities to characterize other random lasers, such as random fiber lasers, nanolasers and small lasers, which include plasmonic-based, photonic-crystal and bio-derived nanodevices. The statistical nature of the emission provided by random lasers can also impact on their prominent use as sources for speckle-free laser imaging, which nowadays represents one of the most promising applications of random lasers, with expected progress even in cancer research. PMID:27292095

  2. Observation of Lévy distribution and replica symmetry breaking in random lasers from a single set of measurements

    NASA Astrophysics Data System (ADS)

    Gomes, Anderson S. L.; Raposo, Ernesto P.; Moura, André L.; Fewo, Serge I.; Pincheira, Pablo I. R.; Jerez, Vladimir; Maia, Lauro J. Q.; de Araújo, Cid B.

    2016-06-01

    Random lasers have been recently exploited as a photonic platform for studies of complex systems. This cross-disciplinary approach opened up new important avenues for the understanding of random-laser behavior, including Lévy-type distributions of strong intensity fluctuations and phase transitions to a photonic spin-glass phase. In this work, we employ the Nd:YBO random laser system to unveil, from a single set of measurements, the physical origin of the complex correspondence between the Lévy fluctuation regime and the replica-symmetry-breaking transition to the spin-glass phase. A novel unexpected finding is also reported: the trend to suppress the spin-glass behavior for high excitation pulse energies. The present description from first principles of this correspondence unfolds new possibilities to characterize other random lasers, such as random fiber lasers, nanolasers and small lasers, which include plasmonic-based, photonic-crystal and bio-derived nanodevices. The statistical nature of the emission provided by random lasers can also impact on their prominent use as sources for speckle-free laser imaging, which nowadays represents one of the most promising applications of random lasers, with expected progress even in cancer research.

  3. Geostatistical Interpolation of Particle-Size Curves in Heterogeneous Aquifers

    NASA Astrophysics Data System (ADS)

    Guadagnini, A.; Menafoglio, A.; Secchi, P.

    2013-12-01

    We address the problem of predicting the spatial field of particle-size curves (PSCs) from measurements associated with soil samples collected at a discrete set of locations within an aquifer system. Proper estimates of the full PSC are relevant to applications related to groundwater hydrology, soil science and geochemistry and aimed at modeling physical and chemical processes occurring in heterogeneous earth systems. Hence, we focus on providing kriging estimates of the entire PSC at unsampled locations. To this end, we treat particle-size curves as cumulative distribution functions, model their densities as functional compositional data and analyze them by embedding these into the Hilbert space of compositional functions endowed with the Aitchison geometry. On this basis, we develop a new geostatistical methodology for the analysis of spatially dependent functional compositional data. Our functional compositional kriging (FCK) approach allows providing predictions at unsampled location of the entire particle-size curve, together with a quantification of the associated uncertainty, by fully exploiting both the functional form of the data and their compositional nature. This is a key advantage of our approach with respect to traditional methodologies, which treat only a set of selected features (e.g., quantiles) of PSCs. Embedding the full PSC into a geostatistical analysis enables one to provide a complete characterization of the spatial distribution of lithotypes in a reservoir, eventually leading to improved predictions of soil hydraulic attributes through pedotransfer functions as well as of soil geochemical parameters which are relevant in sorption/desorption and cation exchange processes. We test our new method on PSCs sampled along a borehole located within an alluvial aquifer near the city of Tuebingen, Germany. The quality of FCK predictions is assessed through leave-one-out cross-validation. A comparison between hydraulic conductivity estimates obtained

  4. Geostatistical Analysis of Spatial Variability of Mineral Abundance and Kd in Frenchman Flat, NTS, Alluvium

    SciTech Connect

    Carle, S F; Zavarin, M; Pawloski, G A

    2002-11-01

    LLNL hydrologic source term modeling at the Cambric site (Pawloski et al., 2000) showed that retardation of radionuclide transport is sensitive to the distribution and amount of radionuclide sorbing minerals. While all mineralogic information available near the Cambric site was used in these early simulations (11 mineral abundance analyses from UE-5n and 9 from RNM-l), these older data sets were qualitative in nature, with detection limits too high to accurately measure many of the important radionuclide sorbing minerals (e.g. iron oxide). Also, the sparse nature of the mineral abundance data permitted only a hypothetical description of the spatial distribution of radionuclide sorbing minerals. Yet, the modeling results predicted that the spatial distribution of sorbing minerals would strongly affect radionuclide transport. Clearly, additional data are needed to improve understanding of mineral abundances and their spatial distributions if model predictions in Frenchman Flat are to be defensible. This report evaluates new high-resolution quantitative X-Ray Diffraction (XRD) data on mineral distributions and their abundances from core samples recently collected from drill hole ER-5-4. The total of 94 samples from ER-5-4 were collected at various spacings to enable evaluation of spatial variability at a variety of spatial scales as small as 0.3 meters and up to hundreds of meters. Additional XRD analyses obtained from drillholes UE-Sn, ER-5-3, and U-11g-1 are used to augment evaluation of vertical spatial variability and permit some evaluation of lateral spatial variability. A total of 163 samples are evaluated. The overall goal of this study is to understand and characterize the spatial variation of sorbing minerals in Frenchman Flat alluvium using geostatistical techniques, with consideration for the potential impact on reactive transport of radionuclides. To achieve this goal requires an effort to ensure that plausible geostatistical models are used to

  5. Comparison of Direct and Indirect Laryngoscopes in Vomitus and Hematemesis Settings: A Randomized Simulation Trial

    PubMed Central

    Mihara, Ryosuke; Komasawa, Nobuyasu; Matsunami, Sayuri; Minami, Toshiaki

    2015-01-01

    Background. Videolaryngoscopes may not be useful in the presence of hematemesis or vomitus. We compared the utility of the Macintosh laryngoscope (McL), which is a direct laryngoscope, with that of the Pentax-AWS Airwayscope (AWS) and McGRATH MAC (McGRATH), which are videolaryngoscopes, in simulated hematemesis and vomitus settings. Methods. Seventeen anesthesiologists with more than 1 year of experience performed tracheal intubation on an adult manikin using McL, AWS, and McGRATH under normal, hematemesis, and vomitus simulations. Results. In the normal setting, the intubation success rate was 100% for all three laryngoscopes. In the hematemesis settings, the intubation success rate differed significantly among the three laryngoscopes (P = 0.021). In the vomitus settings, all participants succeeded in tracheal intubation with McL or McGRATH, while five failed in the AWS trial with significant difference (P = 0.003). The intubation time did not significantly differ in normal settings, while it was significantly longer in the AWS trial compared to McL or McGRATH trial in the hematemesis or vomitus settings (P < 0.001, compared to McL or McGRATH in both settings). Conclusion. The performance of McGRATH and McL can be superior to that of AWS for tracheal intubation in vomitus and hematemesis settings in adults. PMID:26618177

  6. Geostatistics: models and tools for the earth sciences

    SciTech Connect

    Journel, A.G.

    1986-01-01

    The probability construct underlying geostatistical methodology is recalled, stressing that stationary is a property of the model rather than of the phenomenon being represented. Geostatistics is more then interpolation and kriging(s) is more than linear interpolation through ordinary kriging. A few common misconceptions are addressed.

  7. Set statistics in conductive bridge random access memory device with Cu/HfO{sub 2}/Pt structure

    SciTech Connect

    Zhang, Meiyun; Long, Shibing Wang, Guoming; Xu, Xiaoxin; Li, Yang; Liu, Qi; Lv, Hangbing; Liu, Ming; Lian, Xiaojuan; Miranda, Enrique; Suñé, Jordi

    2014-11-10

    The switching parameter variation of resistive switching memory is one of the most important challenges in its application. In this letter, we have studied the set statistics of conductive bridge random access memory with a Cu/HfO{sub 2}/Pt structure. The experimental distributions of the set parameters in several off resistance ranges are shown to nicely fit a Weibull model. The Weibull slopes of the set voltage and current increase and decrease logarithmically with off resistance, respectively. This experimental behavior is perfectly captured by a Monte Carlo simulator based on the cell-based set voltage statistics model and the Quantum Point Contact electron transport model. Our work provides indications for the improvement of the switching uniformity.

  8. GEOSTATISTICS FOR WASTE MANAGEMENT: A USER'S MANUEL FOR THE GEOPACK (VERSION 1.0) GEOSTATISTICAL SOFTWARE SYSTEM

    EPA Science Inventory

    A comprehensive, user-friendly geostatistical software system called GEOPACk has been developed. The purpose of this software is to make available the programs necessary to undertake a geostatistical analysis of spatially correlated data. The programs were written so that they ...

  9. Does Pedometer Goal Setting Improve Physical Activity among Native Elders? Results from a Randomized Pilot Study

    ERIC Educational Resources Information Center

    Sawchuk, Craig N.; Russo, Joan E.; Charles, Steve; Goldberg, Jack; Forquera, Ralph; Roy-Byrne, Peter; Buchwald, Dedra

    2011-01-01

    We examined if step-count goal setting resulted in increases in physical activity and walking compared to only monitoring step counts with pedometers among American Indian/Alaska Native elders. Outcomes included step counts, self-reported physical activity and well-being, and performance on the 6-minute walk test. Although no significant…

  10. Adjustment of Open-Loop Settings to Improve Closed-Loop Results in Type 1 Diabetes: A Multicenter Randomized Trial

    PubMed Central

    Dassau, Eyal; Brown, Sue A.; Basu, Ananda; Pinsker, Jordan E.; Kudva, Yogish C.; Gondhalekar, Ravi; Patek, Steve; Lv, Dayu; Schiavon, Michele; Lee, Joon Bok; Dalla Man, Chiara; Hinshaw, Ling; Castorino, Kristin; Mallad, Ashwini; Dadlani, Vikash; McCrady-Spitzer, Shelly K.; McElwee-Malloy, Molly; Wakeman, Christian A.; Bevier, Wendy C.; Bradley, Paige K.; Kovatchev, Boris; Cobelli, Claudio; Zisser, Howard C.

    2015-01-01

    Context: Closed-loop control (CLC) relies on an individual's open-loop insulin pump settings to initialize the system. Optimizing open-loop settings before using CLC usually requires significant time and effort. Objective: The objective was to investigate the effects of a one-time algorithmic adjustment of basal rate and insulin to carbohydrate ratio open-loop settings on the performance of CLC. Design: This study reports a multicenter, outpatient, randomized, crossover clinical trial. Patients: Thirty-seven adults with type 1 diabetes were enrolled at three clinical sites. Interventions: Each subject's insulin pump settings were subject to a one-time algorithmic adjustment based on 1 week of open-loop (i.e., home care) data collection. Subjects then underwent two 27-hour periods of CLC in random order with either unchanged (control) or algorithmic adjusted basal rate and carbohydrate ratio settings (adjusted) used to initialize the zone-model predictive control artificial pancreas controller. Subject's followed their usual meal-plan and had an unannounced exercise session. Main Outcomes and Measures: Time in the glucose range was 80–140 mg/dL, compared between both arms. Results: Thirty-two subjects completed the protocol. Median time in CLC was 25.3 hours. The median time in the 80–140 mg/dl range was similar in both groups (39.7% control, 44.2% adjusted). Subjects in both arms of CLC showed minimal time spent less than 70 mg/dl (median 1.34% and 1.37%, respectively). There were no significant differences more than 140 mg/dL. Conclusions: A one-time algorithmic adjustment of open-loop settings did not alter glucose control in a relatively short duration outpatient closed-loop study. The CLC system proved very robust and adaptable, with minimal (<2%) time spent in the hypoglycemic range in either arm. PMID:26204135

  11. Can Geostatistical Models Represent Nature's Variability? An Analysis Using Flume Experiments

    NASA Astrophysics Data System (ADS)

    Scheidt, C.; Fernandes, A. M.; Paola, C.; Caers, J.

    2015-12-01

    The lack of understanding in the Earth's geological and physical processes governing sediment deposition render subsurface modeling subject to large uncertainty. Geostatistics is often used to model uncertainty because of its capability to stochastically generate spatially varying realizations of the subsurface. These methods can generate a range of realizations of a given pattern - but how representative are these of the full natural variability? And how can we identify the minimum set of images that represent this natural variability? Here we use this minimum set to define the geostatistical prior model: a set of training images that represent the range of patterns generated by autogenic variability in the sedimentary environment under study. The proper definition of the prior model is essential in capturing the variability of the depositional patterns. This work starts with a set of overhead images from an experimental basin that showed ongoing autogenic variability. We use the images to analyze the essential characteristics of this suite of patterns. In particular, our goal is to define a prior model (a minimal set of selected training images) such that geostatistical algorithms, when applied to this set, can reproduce the full measured variability. A necessary prerequisite is to define a measure of variability. In this study, we measure variability using a dissimilarity distance between the images. The distance indicates whether two snapshots contain similar depositional patterns. To reproduce the variability in the images, we apply an MPS algorithm to the set of selected snapshots of the sedimentary basin that serve as training images. The training images are chosen from among the initial set by using the distance measure to ensure that only dissimilar images are chosen. Preliminary investigations show that MPS can reproduce fairly accurately the natural variability of the experimental depositional system. Furthermore, the selected training images provide

  12. Randomized Trial of Two Dissemination Strategies for a Skin Cancer Prevention Program in Aquatic Settings

    PubMed Central

    Escoffery, Cam; Elliott, Tom; Nehl, Eric J.

    2015-01-01

    Objectives. We compared 2 strategies for disseminating an evidence-based skin cancer prevention program. Methods. We evaluated the effects of 2 strategies (basic vs enhanced) for dissemination of the Pool Cool skin cancer prevention program in outdoor swimming pools on (1) program implementation, maintenance, and sustainability and (2) improvements in organizational and environmental supports for sun protection. The trial used a cluster-randomized design with pools as the unit of intervention and outcome. The enhanced group received extra incentives, reinforcement, feedback, and skill-building guidance. Surveys were collected in successive years (2003–2006) from managers of 435 pools in 33 metropolitan areas across the United States participating in the Pool Cool Diffusion Trial. Results. Both treatment groups improved their implementation of the program, but pools in the enhanced condition had significantly greater overall maintenance of the program over 3 summers of participation. Furthermore, pools in the enhanced condition established and maintained significantly greater sun-safety policies and supportive environments over time. Conclusions. This study found that more intensive, theory-driven dissemination strategies can significantly enhance program implementation and maintenance of health-promoting environmental and policy changes. Future research is warranted through longitudinal follow-up to examine sustainability. PMID:25521872

  13. Mine planning and emission control strategies using geostatistics

    SciTech Connect

    Martino, F.; Kim, Y.C.

    1983-03-01

    This paper reviews the past four years' research efforts performed jointly by the University of Arizona and the Homer City Owners in which geostatistics were applied to solve various problems associated with coal characterization, mine planning, and development of emission control strategies. Because geostatistics is the only technique which can quantify the degree of confidence associated with a given estimate (or prediction), it played an important role throughout the research efforts. Through geostatistics, it was learned that there is an urgent need for closely spaced sample information, if short-term coal quality predictions are to be made for mine planning purposes.

  14. Probiotics reduce symptoms of antibiotic use in a hospital setting: a randomized dose response study.

    PubMed

    Ouwehand, Arthur C; DongLian, Cai; Weijian, Xu; Stewart, Morgan; Ni, Jiayi; Stewart, Tad; Miller, Larry E

    2014-01-16

    Probiotics are known to reduce antibiotic associated diarrhea (AAD) and Clostridium difficile associated diarrhea (CDAD) risk in a strain-specific manner. The aim of this study was to determine the dose-response effect of a four strain probiotic combination (HOWARU(®) Restore) on the incidence of AAD and CDAD and severity of gastrointestinal symptoms in adult in-patients requiring antibiotic therapy. Patients (n=503) were randomized among three study groups: HOWARU(®) Restore probiotic 1.70×10(10) CFU (high-dose, n=168), HOWARU(®) Restore probiotic 4.17×10(9) CFU (low-dose, n=168), or placebo (n=167). Subjects were stratified by gender, age, and duration of antibiotic treatment. Study products were administered daily up to 7 days after the final antibiotic dose. The primary endpoint of the study was the incidence of AAD. Secondary endpoints included incidence of CDAD, diarrhea duration, stools per day, bloody stools, fever, abdominal cramping, and bloating. A significant dose-response effect on AAD was observed with incidences of 12.5, 19.6, and 24.6% with high-dose, low-dose, and placebo, respectively (p=0.02). CDAD was the same in both probiotic groups (1.8%) but different from the placebo group (4.8%; p=0.04). Incidences of fever, abdominal pain, and bloating were lower with increasing probiotic dose. The number of daily liquid stools and average duration of diarrhea decreased with higher probiotic dosage. The tested four strain probiotic combination appears to lower the risk of AAD, CDAD, and gastrointestinal symptoms in a dose-dependent manner in adult in-patients.

  15. Geostatistics, remote sensing and precision farming.

    PubMed

    Mulla, D J

    1997-01-01

    Precision farming is possible today because of advances in farming technology, procedures for mapping and interpolating spatial patterns, and geographic information systems for overlaying and interpreting several soil, landscape and crop attributes. The key component of precision farming is the map showing spatial patterns in field characteristics. Obtaining information for this map is often achieved by soil sampling. This approach, however, can be cost-prohibitive for grain crops. Soil sampling strategies can be simplified by use of auxiliary data provided by satellite or aerial photo imagery. This paper describes geostatistical methods for estimating spatial patterns in soil organic matter, soil test phosphorus and wheat grain yield from a combination of Thematic Mapper imaging and soil sampling.

  16. Geostatistical Estimations of Regional Hydraulic Conductivity Fields

    NASA Astrophysics Data System (ADS)

    Patriarche, D.; Castro, M. C.; Goovaerts, P.

    2004-12-01

    Direct and indirect measurements of hydraulic conductivity (K) are commonly performed, providing information on the magnitude of this parameter at the local scale (tens of centimeters to hundreds of meters) and at shallow depths. By contrast, field information on hydraulic conductivities at regional scales of tens to hundreds of kilometers and at greater depths is relatively scarce. Geostatistical methods allow for sparsely sampled observations of a variable (primary information) to be complemented by a more densely sampled secondary attribute. Geostatistical estimations of the hydraulic conductivity field in the Carrizo aquifer, a major groundwater flow system extending along Texas, are performed using available primary (e.g., transmissivity, hydraulic conductivity) and secondary (specific capacity) information, for depths up to 2.2 km, and over three regional domains of increasing extent: 1) the domain corresponding to a three-dimensional groundwater flow model previously built (model domain); 2) the area corresponding to the ten counties encompassing the model domain (County domain), and; 3) the full extension of the Carrizo aquifer within Texas (Texas domain). Two different approaches are used: 1) an indirect approach are transmissivity (T) is estimated first and (K) is retrieved through division of the T estimate by the screening length of the wells, and; 2) a direct approach where K data are kriged directly. Prediction performances of the tested geostatistical procedures (kriging combined with linear regression, kriging with known local means, kriging of residuals, and cokriging) are evaluated through cross validation for both log-transformed variables and back-transformed ones. For the indirect approach, kriging of log T residuals yields the best estimates for both log-transformed and back-transformed variables in the model domain. For larger regional scales (County and Texas domains), cokriging performs generally better than univariate kriging procedures

  17. Technology demonstration: geostatistical and hydrologic analysis of salt areas. Assessment of effectiveness of geologic isolation systems

    SciTech Connect

    Doctor, P.G.; Oberlander, P.L.; Rice, W.A.; Devary, J.L.; Nelson, R.W.; Tucker, P.E.

    1982-09-01

    The Office of Nuclear Waste Isolation (ONWI) requested Pacific Northwest Laboratory (PNL) to: (1) use geostatistical analyses to evaluate the adequacy of hydrologic data from three salt regions, each of which contains a potential nuclear waste repository site; and (2) demonstrate a methodology that allows quantification of the value of additional data collection. The three regions examined are the Paradox Basin in Utah, the Permian Basin in Texas, and the Mississippi Study Area. Additional and new data became available to ONWI during and following these analyses; therefore, this report must be considered a methodology demonstration here would apply as illustrated had the complete data sets been available. A combination of geostatistical and hydrologic analyses was used for this demonstration. Geostatistical analyses provided an optimal estimate of the potentiometric surface from the available data, a measure of the uncertainty of that estimate, and a means for selecting and evaluating the location of future data. The hydrologic analyses included the calculation of transmissivities, flow paths, travel times, and ground-water flow rates from hypothetical repository sites. Simulation techniques were used to evaluate the effect of optimally located future data on the potentiometric surface, flow lines, travel times, and flow rates. Data availability, quality, quantity, and conformance with model assumptions differed in each of the salt areas. Report highlights for the three locations are given.

  18. Validation and comparison of geostatistical and spline models for spatial stream networks.

    PubMed

    Rushworth, A M; Peterson, E E; Ver Hoef, J M; Bowman, A W

    2015-08-01

    Scientists need appropriate spatial-statistical models to account for the unique features of stream network data. Recent advances provide a growing methodological toolbox for modelling these data, but general-purpose statistical software has only recently emerged, with little information about when to use different approaches. We implemented a simulation study to evaluate and validate geostatistical models that use continuous distances, and penalised spline models that use a finite discrete approximation for stream networks. Data were simulated from the geostatistical model, with performance measured by empirical prediction and fixed effects estimation. We found that both models were comparable in terms of squared error, with a slight advantage for the geostatistical models. Generally, both methods were unbiased and had valid confidence intervals. The most marked differences were found for confidence intervals on fixed-effect parameter estimates, where, for small sample sizes, the spline models underestimated variance. However, the penalised spline models were always more computationally efficient, which may be important for real-time prediction and estimation. Thus, decisions about which method to use must be influenced by the size and format of the data set, in addition to the characteristics of the environmental process and the modelling goals. ©2015 The Authors. Environmetrics published by John Wiley & Sons, Ltd.

  19. Novel Geostatistical Characterization of the Borden Aquifer, Canada

    NASA Astrophysics Data System (ADS)

    Maghrebi, M.; Jankovic, I.; Allen-King, R. M.; Rabideau, A. J.; Weissmann, G. S.

    2012-12-01

    Geostatistical (spatial) characterization of aquifer properties (hydraulic conductivity and sorption distribution coefficient) is the basis for development of contaminant transport models including stochastic models. Large datasets are required for complete spatial analysis of aquifer properties. The process of collecting the required field data is labor intensive and expensive. Because of limited data availability, spatial analysis is traditionally limited to 2-point overall covariance/variogram analysis. Additional assumptions, not supported by field data, are adopted to develop contaminant transport models. For example, hydraulic conductivity is often modeled as a MultiGaussian random field. A very large dataset of aquifer properties was collected and analyzed for the Borden aquifer, Canada. This is accomplished by exposing 15 panels in a sand quarry during our summer 2010 field study. The sedimentary facies from these 15 panels were then mapped using high-resolution photography, terrestrial Lidar images with field descriptions (Pickel, A.C. et al, Outcrop Analog Analysis of Lithofacies Distributions within Borden Aquifer Sediments, Ontario, CA, AGU 2011 Fall meeting, Poster ID: H51H-1297). The mapped images enable us to determine the three-dimensional positions of hydrofacies at the site and to classify these hydrofacies. Hydrofacies were classified into 6 general material groups with distinct magnitude of hydraulic conductivity and sorption distribution coefficient. The resulting dataset contains the information of 3D point coordinates and the group indices for approximately 3 million points. In order to perform the spatial analysis of this dataset, computer code GSLIB was parallelized with distributed and shared-memory directives and used to compute indicator semi-variograms of six material groups. While this does not constitute a full statistical analysis of the formation (multi-point and/or higher order correlations would be necessary for such analysis

  20. Incorporating reservoir heterogeneity with geostatistics to investigate waterflood recoveries

    SciTech Connect

    Wolcott, D.S. ); Chopra, A.K. )

    1993-03-01

    This paper presents an investigation of infill drilling performance and reservoir continuity with geostatistics and a reservoir simulator. The geostatistical technique provides many possible realizations and realistic descriptions of reservoir heterogeneity. Correlation between recovery efficiency and thickness of individual sand subunits is shown. Additional recovery from infill drilling results from thin, discontinuous subunits. The technique may be applied to variations in continuity for other sandstone reservoirs.

  1. Peaks and dips in Gaussian random fields: a new algorithm for the shear eigenvalues, and the excursion set theory

    NASA Astrophysics Data System (ADS)

    Rossi, Graziano

    2013-04-01

    We present a new algorithm to sample the constrained eigenvalues of the initial shear field associated with Gaussian statistics, called the `peak/dip excursion-set-based' algorithm, at positions which correspond to peaks or dips of the correlated density field. The computational procedure is based on a new formula which extends Doroshkevich's unconditional distribution for the eigenvalues of the linear tidal field, to account for the fact that haloes and voids may correspond to maxima or minima of the density field. The ability to differentiate between random positions and special points in space around which haloes or voids may form (i.e. peaks/dips), encoded in the new formula and reflected in the algorithm, naturally leads to a straightforward implementation of an excursion set model for peaks and dips in Gaussian random fields - one of the key advantages of this sampling procedure. In addition, it offers novel insights into the statistical description of the cosmic web. As a first physical application, we show how the standard distributions of shear ellipticity and prolateness in triaxial models of structure formation are modified by the constraint. In particular, we provide a new expression for the conditional distribution of shape parameters given the density peak constraint, which generalizes some previous literature work. The formula has important implications for the modelling of non-spherical dark matter halo shapes, in relation to their initial shape distribution. We also test and confirm our theoretical predictions for the individual distributions of eigenvalues subjected to the extremum constraint, along with other directly related conditional probabilities. Finally, we indicate how the proposed sampling procedure naturally integrates into the standard excursion set model, potentially solving some of its well-known problems, and into the ellipsoidal collapse framework. Several other ongoing applications and extensions, towards the development of

  2. Unsupervised classification of multivariate geostatistical data: Two algorithms

    NASA Astrophysics Data System (ADS)

    Romary, Thomas; Ors, Fabien; Rivoirard, Jacques; Deraisme, Jacques

    2015-12-01

    With the increasing development of remote sensing platforms and the evolution of sampling facilities in mining and oil industry, spatial datasets are becoming increasingly large, inform a growing number of variables and cover wider and wider areas. Therefore, it is often necessary to split the domain of study to account for radically different behaviors of the natural phenomenon over the domain and to simplify the subsequent modeling step. The definition of these areas can be seen as a problem of unsupervised classification, or clustering, where we try to divide the domain into homogeneous domains with respect to the values taken by the variables in hand. The application of classical clustering methods, designed for independent observations, does not ensure the spatial coherence of the resulting classes. Image segmentation methods, based on e.g. Markov random fields, are not adapted to irregularly sampled data. Other existing approaches, based on mixtures of Gaussian random functions estimated via the expectation-maximization algorithm, are limited to reasonable sample sizes and a small number of variables. In this work, we propose two algorithms based on adaptations of classical algorithms to multivariate geostatistical data. Both algorithms are model free and can handle large volumes of multivariate, irregularly spaced data. The first one proceeds by agglomerative hierarchical clustering. The spatial coherence is ensured by a proximity condition imposed for two clusters to merge. This proximity condition relies on a graph organizing the data in the coordinates space. The hierarchical algorithm can then be seen as a graph-partitioning algorithm. Following this interpretation, a spatial version of the spectral clustering algorithm is also proposed. The performances of both algorithms are assessed on toy examples and a mining dataset.

  3. Optimisation of groundwater level monitoring networks using geostatistical modelling based on the Spartan family variogram and a genetic algorithm method

    NASA Astrophysics Data System (ADS)

    Parasyris, Antonios E.; Spanoudaki, Katerina; Kampanis, Nikolaos A.

    2016-04-01

    Groundwater level monitoring networks provide essential information for water resources management, especially in areas with significant groundwater exploitation for agricultural and domestic use. Given the high maintenance costs of these networks, development of tools, which can be used by regulators for efficient network design is essential. In this work, a monitoring network optimisation tool is presented. The network optimisation tool couples geostatistical modelling based on the Spartan family variogram with a genetic algorithm method and is applied to Mires basin in Crete, Greece, an area of high socioeconomic and agricultural interest, which suffers from groundwater overexploitation leading to a dramatic decrease of groundwater levels. The purpose of the optimisation tool is to determine which wells to exclude from the monitoring network because they add little or no beneficial information to groundwater level mapping of the area. Unlike previous relevant investigations, the network optimisation tool presented here uses Ordinary Kriging with the recently-established non-differentiable Spartan variogram for groundwater level mapping, which, based on a previous geostatistical study in the area leads to optimal groundwater level mapping. Seventy boreholes operate in the area for groundwater abstraction and water level monitoring. The Spartan variogram gives overall the most accurate groundwater level estimates followed closely by the power-law model. The geostatistical model is coupled to an integer genetic algorithm method programmed in MATLAB 2015a. The algorithm is used to find the set of wells whose removal leads to the minimum error between the original water level mapping using all the available wells in the network and the groundwater level mapping using the reduced well network (error is defined as the 2-norm of the difference between the original mapping matrix with 70 wells and the mapping matrix of the reduced well network). The solution to the

  4. The role of geostatistics in medical geology

    NASA Astrophysics Data System (ADS)

    Goovaerts, Pierre

    2014-05-01

    Since its development in the mining industry, geostatistics has emerged as the primary tool for spatial data analysis in various fields, ranging from earth and atmospheric sciences, to agriculture, soil science, remote sensing, and more recently environmental exposure assessment. In the last few years, these tools have been tailored to the field of medical geography or spatial epidemiology, which is concerned with the study of spatial patterns of disease incidence and mortality and the identification of potential 'causes' of disease, such as environmental exposure, diet and unhealthy behaviors, economic or socio-demographic factors. On the other hand, medical geology is an emerging interdisciplinary scientific field studying the relationship between natural geological factors and their effects on human and animal health. This paper provides an introduction to the field of medical geology with an overview of geostatistical methods available for the analysis of geological and health data. Key concepts are illustrated using the mapping of groundwater arsenic concentrations across eleven Michigan counties and the exploration of its relationship to the incidence of prostate cancer at the township level. Arsenic in drinking-water is a major problem and has received much attention because of the large human population exposed and the extremely high concentrations (e.g. 600 to 700 μg/L) recorded in many instances. Few studies have however assessed the risks associated with exposure to low levels of arsenic (say < 50 μg/L) most commonly found in drinking water in the United States. In the Michigan thumb region, arsenopyrite (up to 7% As by weight) has been identified in the bedrock of the Marshall Sandstone aquifer, one of the region's most productive aquifers. Epidemiologic studies have suggested a possible associationbetween exposure to inorganic arsenic and prostate cancer mortality, including a study of populations residing in Utah. The information available for the

  5. A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations.

    PubMed

    Roth, Holger R; Lu, Le; Seff, Ari; Cherry, Kevin M; Hoffman, Joanne; Wang, Shijun; Liu, Jiamin; Turkbey, Evrim; Summers, Ronald M

    2014-01-01

    Automated Lymph Node (LN) detection is an important clinical diagnostic task but very challenging due to the low contrast of surrounding structures in Computed Tomography (CT) and to their varying sizes, poses, shapes and sparsely distributed locations. State-of-the-art studies show the performance range of 52.9% sensitivity at 3.1 false-positives per volume (FP/vol.), or 60.9% at 6.1 FP/vol. for mediastinal LN, by one-shot boosting on 3D HAAR features. In this paper, we first operate a preliminary candidate generation stage, towards -100% sensitivity at the cost of high FP levels (-40 per patient), to harvest volumes of interest (VOI). Our 2.5D approach consequently decomposes any 3D VOI by resampling 2D reformatted orthogonal views N times, via scale, random translations, and rotations with respect to the VOI centroid coordinates. These random views are then used to train a deep Convolutional Neural Network (CNN) classifier. In testing, the CNN is employed to assign LN probabilities for all N random views that can be simply averaged (as a set) to compute the final classification probability per VOI. We validate the approach on two datasets: 90 CT volumes with 388 mediastinal LNs and 86 patients with 595 abdominal LNs. We achieve sensitivities of 70%/83% at 3 FP/vol. and 84%/90% at 6 FP/vol. in mediastinum and abdomen respectively, which drastically improves over the previous state-of-the-art work.

  6. A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations

    PubMed Central

    Lu, Le; Seff, Ari; Cherry, Kevin M.; Hoffman, Joanne; Wang, Shijun; Liu, Jiamin; Turkbey, Evrim; Summers, Ronald M.

    2015-01-01

    Automated Lymph Node (LN) detection is an important clinical diagnostic task but very challenging due to the low contrast of surrounding structures in Computed Tomography (CT) and to their varying sizes, poses, shapes and sparsely distributed locations. State-of-the-art studies show the performance range of 52.9% sensitivity at 3.1 false-positives per volume (FP/vol.), or 60.9% at 6.1 FP/vol. for mediastinal LN, by one-shot boosting on 3D HAAR features. In this paper, we first operate a preliminary candidate generation stage, towards ~100% sensitivity at the cost of high FP levels (~40 per patient), to harvest volumes of interest (VOI). Our 2.5D approach consequently decomposes any 3D VOI by resampling 2D reformatted orthogonal views N times, via scale, random translations, and rotations with respect to the VOI centroid coordinates. These random views are then used to train a deep Convolutional Neural Network (CNN) classifier. In testing, the CNN is employed to assign LN probabilities for all N random views that can be simply averaged (as a set) to compute the final classification probability per VOI. We validate the approach on two datasets: 90 CT volumes with 388 mediastinal LNs and 86 patients with 595 abdominal LNs. We achieve sensitivities of 70%/83% at 3 FP/vol. and 84%/90% at 6 FP/vol. in mediastinum and abdomen respectively, which drastically improves over the previous state-of-the-art work. PMID:25333158

  7. Increasing Water Intake of Children and Parents in the Family Setting: A Randomized, Controlled Intervention Using Installation Theory.

    PubMed

    Lahlou, Saadi; Boesen-Mariani, Sabine; Franks, Bradley; Guelinckx, Isabelle

    2015-01-01

    On average, children and adults in developed countries consume too little water, which can lead to negative health consequences. In a one-year longitudinal field experiment in Poland, we compared the impact of three home-based interventions on helping children and their parents/caregivers to develop sustainable increased plain water consumption habits. Fluid consumption of 334 children and their caregivers were recorded over one year using an online specific fluid dietary record. They were initially randomly allocated to one of the three following conditions: Control, Information (child and carer received information on the health benefits of water), or Placement (in addition to information, free small bottles of still water for a limited time period were delivered at home). After three months, half of the non-controls were randomly assigned to Community (child and caregiver engaged in an online community forum providing support on water consumption). All conditions significantly increased the water consumption of children (by 21.9-56.7%) and of adults (by 22-89%). Placement + Community generated the largest effects. Community enhanced the impact of Placement for children and parents, as well as the impact of Information for parents but not children. The results suggest that the family setting offers considerable scope for successful installation of interventions encouraging children and caregivers to develop healthier consumption habits, in mutually reinforcing ways. Combining information, affordances, and social influence gives the best, and most sustainable, results.

  8. Estimation of extreme daily precipitation: comparison between regional and geostatistical approaches.

    NASA Astrophysics Data System (ADS)

    Hellies, Matteo; Deidda, Roberto; Langousis, Andreas

    2016-04-01

    We study the extreme rainfall regime of the Island of Sardinia in Italy, based on annual maxima of daily precipitation. The statistical analysis is conducted using 229 daily rainfall records with at least 50 complete years of observations, collected at different sites by the Hydrological Survey of the Sardinia Region. Preliminary analysis, and the L-skewness and L-kurtosis diagrams, show that the Generalized Extreme Value (GEV) distribution model performs best in describing daily rainfall extremes. The GEV distribution parameters are estimated using the method of Probability Weighted Moments (PWM). To obtain extreme rainfall estimates at ungauged sites, while minimizing uncertainties due to sampling variability, a regional and a geostatistical approach are compared. The regional approach merges information from different gauged sites, within homogeneous regions, to obtain GEV parameter estimates at ungauged locations. The geostatistical approach infers the parameters of the GEV distribution model at locations where measurements are available, and then spatially interpolates them over the study region. In both approaches we use local rainfall means as index-rainfall. In the regional approach we define homogeneous regions by applying a hierarchical cluster analysis based on Ward's method, with L-moment ratios (i.e. L-CV and L-Skewness) as metrics. The analysis results in four contiguous regions, which satisfy the Hosking and Wallis (1997) homogeneity tests. The latter have been conducted using a Monte-Carlo approach based on a 4-parameter Kappa distribution model, fitted to each station cluster. Note that the 4-parameter Kappa model includes the GEV distribution as a sub-case, when the fourth parameter h is set to 0. In the geostatistical approach we apply kriging for uncertain data (KUD), which accounts for the error variance in local parameter estimation and, therefore, may serve as a useful tool for spatial interpolation of metrics affected by high uncertainty. In

  9. A geostatistical approach to contaminant source identification

    NASA Astrophysics Data System (ADS)

    Snodgrass, Mark F.; Kitanidis, Peter K.

    1997-04-01

    A geostatistical approach to contaminant source estimation is presented. The problem is to estimate the release history of a conservative solute given point concentration measurements at some time after the release. A Bayesian framework is followed to derive the best estimate and to quantify the estimation error. The relation between this approach and common regularization and interpolation schemes is discussed. The performance of the method is demonstrated for transport in a simple one-dimensional homogeneous medium, although the approach is directly applicable to transport in two- or three-dimensional domains. The methodology produces a best estimate of the release history and a confidence interval. Conditional realizations of the release history are generated that are useful in visualization and risk assessment. The performance of the method with sparse data and large measurement error is examined. Emphasis is placed on formulating the estimation method in a computationally efficient manner. The method does not require the inversion of matrices whose size depends on the grid size used to resolve the solute release history. The issue of model validation is addressed.

  10. High Performance Geostatistical Modeling of Biospheric Resources

    NASA Astrophysics Data System (ADS)

    Pedelty, J. A.; Morisette, J. T.; Smith, J. A.; Schnase, J. L.; Crosier, C. S.; Stohlgren, T. J.

    2004-12-01

    We are using parallel geostatistical codes to study spatial relationships among biospheric resources in several study areas. For example, spatial statistical models based on large- and small-scale variability have been used to predict species richness of both native and exotic plants (hot spots of diversity) and patterns of exotic plant invasion. However, broader use of geostastics in natural resource modeling, especially at regional and national scales, has been limited due to the large computing requirements of these applications. To address this problem, we implemented parallel versions of the kriging spatial interpolation algorithm. The first uses the Message Passing Interface (MPI) in a master/slave paradigm on an open source Linux Beowulf cluster, while the second is implemented with the new proprietary Xgrid distributed processing system on an Xserve G5 cluster from Apple Computer, Inc. These techniques are proving effective and provide the basis for a national decision support capability for invasive species management that is being jointly developed by NASA and the US Geological Survey.

  11. Focused Training for Humanitarian Responders in Regional Anesthesia Techniques for a Planned Randomized Controlled Trial in a Disaster Setting

    PubMed Central

    Aluisio, Adam R.; Teicher, Carrei; Wiskel, Tess; Guy, Allysia; Levine, Adam

    2016-01-01

    Background:Lower extremity trauma during earthquakes accounts for the largest burden of geophysical disaster-related injuries. Insufficient pain management is common in disaster settings, and regional anesthesia (RA) has the potential to reduce pain in injured patients beyond current standards. To date, no prospective research has evaluated the use of RA in a disaster setting. This cross-sectional study assesses knowledge translation and skill acquisition outcomes for lower extremity RA performed with and without ultrasound guidance among a cohort of Médecins Sans Frontières (MSF) volunteers who will function as proceduralists in a planned randomized controlled trial evaluating the efficacy of RA for pain management in an earthquake setting. Methods:Generalist humanitarian healthcare responders, including both physicians and nurses, were trained in ultrasound guided femoral nerve block (USGFNB) and landmark guided fascia iliaca compartment block (LGFICB) techniques using didactic sessions and interactive simulations during a one-day focused course. Outcome measures evaluated interval knowledge attainment and technical proficiency in performing the RA procedures. Knowledge attainment was assessed via pre- and post-test evaluations and procedural proficiency was evaluated through monitored simulations, with performance of critical actions graded by two independent observers. Results:Twelve humanitarian response providers were enrolled and completed the trainings and assessments. Knowledge scores significantly increased from a mean pre-test score of 79% to post-test score of 88% (p<0.001). In practical evaluation of the LGFICB, participants correctly performed a median of 15.0 (Interquartile Range (IQR) 14.0-16.0) out of 16 critical actions. For the USGFNB, the median score was also 15.0 (IQR 14.0-16.0) out of 16 critical actions. Inter-rater reliability for completion of critical actions was excellent, with inter-rater agreement of 83.3% and 91.7% for the LGFICB

  12. Selective remediation of contaminated sites using a two-level multiphase strategy and geostatistics.

    PubMed

    Saito, Hirotaka; Goovaerts, Pierre

    2003-05-01

    Selective soil remediation aims to reduce costs by cleaning only the fraction of an exposure unit (EU) necessary to lower the average concentration below the regulatory threshold. This approach requires a prior stratification of each EU into smaller remediation units (RU) which are then selected according to various criteria. This paper presents a geostatistical framework to account for uncertainties attached to both RU and EU average concentrations in selective remediation. The selection of RUs is based on their impact on the postremediation probability for the EU average concentration to exceed the regulatory threshold, which is assessed using geostatistical stochastic simulation. Application of the technique to a set of 600 dioxin concentrations collected at Piazza Road EPA Superfund site in Missouri shows a substantial decrease in the number of RU remediated compared with single phase remediation. The lower remediation costs achieved by the new strategy are obtained to the detriment of a higher risk of false negatives, yet for this data set this risk remains below the 5% rate set by EPA region 7.

  13. Geostatistical Sampling Methods for Efficient Uncertainty Analysis in Flow and Transport Problems

    NASA Astrophysics Data System (ADS)

    Liodakis, Stylianos; Kyriakidis, Phaedon; Gaganis, Petros

    2015-04-01

    In hydrogeological applications involving flow and transport of in heterogeneous porous media the spatial distribution of hydraulic conductivity is often parameterized in terms of a lognormal random field based on a histogram and variogram model inferred from data and/or synthesized from relevant knowledge. Realizations of simulated conductivity fields are then generated using geostatistical simulation involving simple random (SR) sampling and are subsequently used as inputs to physically-based simulators of flow and transport in a Monte Carlo framework for evaluating the uncertainty in the spatial distribution of solute concentration due to the uncertainty in the spatial distribution of hydraulic con- ductivity [1]. Realistic uncertainty analysis, however, calls for a large number of simulated concentration fields; hence, can become expensive in terms of both time and computer re- sources. A more efficient alternative to SR sampling is Latin hypercube (LH) sampling, a special case of stratified random sampling, which yields a more representative distribution of simulated attribute values with fewer realizations [2]. Here, term representative implies realizations spanning efficiently the range of possible conductivity values corresponding to the lognormal random field. In this work we investigate the efficiency of alternative methods to classical LH sampling within the context of simulation of flow and transport in a heterogeneous porous medium. More precisely, we consider the stratified likelihood (SL) sampling method of [3], in which attribute realizations are generated using the polar simulation method by exploring the geometrical properties of the multivariate Gaussian distribution function. In addition, we propose a more efficient version of the above method, here termed minimum energy (ME) sampling, whereby a set of N representative conductivity realizations at M locations is constructed by: (i) generating a representative set of N points distributed on the

  14. Tuberculosis case-finding through a village outreach programme in a rural setting in southern Ethiopia: community randomized trial.

    PubMed Central

    Shargie, Estifanos Biru; Mørkve, Odd; Lindtjørn, Bernt

    2006-01-01

    OBJECTIVE: To ascertain whether case-finding through community outreach in a rural setting has an effect on case-notification rate, symptom duration, and treatment outcome of smear-positive tuberculosis (TB). METHODS: We randomly allocated 32 rural communities to intervention or control groups. In intervention communities, health workers from seven health centres held monthly diagnostic outreach clinics at which they obtained sputum samples for sputum microscopy from symptomatic TB suspects. In addition, trained community promoters distributed leaflets and discussed symptoms of TB during house visits and at popular gatherings. Symptomatic individuals were encouraged to visit the outreach team or a nearby health facility. In control communities, cases were detected through passive case-finding among symptomatic suspects reporting to health facilities. Smear-positive TB patients from the intervention and control communities diagnosed during the study period were prospectively enrolled. FINDINGS: In the 1-year study period, 159 and 221 cases of smear-positive TB were detected in the intervention and control groups, respectively. Case-notification rates in all age groups were 124.6/10(5) and 98.1/10(5) person-years, respectively (P = 0.12). The corresponding rates in adults older than 14 years were 207/10(5) and 158/10(5) person-years, respectively (P = 0.09). The proportion of patients with >3 months' symptom duration was 41% in the intervention group compared with 63% in the control group (P<0.001). Pre-treatment symptom duration in the intervention group fell by 55-60% compared with 3-20% in the control group. In the intervention and control groups, 81% and 75%, respectively of patients successfully completed treatment (P = 0.12). CONCLUSION: The intervention was effective in improving the speed but not the extent of case finding for smear-positive TB in this setting. Both groups had comparable treatment outcomes. PMID:16501728

  15. Testing Allele Transmission of an SNP Set Using a Family-Based Generalized Genetic Random Field Method.

    PubMed

    Li, Ming; Li, Jingyun; He, Zihuai; Lu, Qing; Witte, John S; Macleod, Stewart L; Hobbs, Charlotte A; Cleves, Mario A

    2016-05-01

    Family-based association studies are commonly used in genetic research because they can be robust to population stratification (PS). Recent advances in high-throughput genotyping technologies have produced a massive amount of genomic data in family-based studies. However, current family-based association tests are mainly focused on evaluating individual variants one at a time. In this article, we introduce a family-based generalized genetic random field (FB-GGRF) method to test the joint association between a set of autosomal SNPs (i.e., single-nucleotide polymorphisms) and disease phenotypes. The proposed method is a natural extension of a recently developed GGRF method for population-based case-control studies. It models offspring genotypes conditional on parental genotypes, and, thus, is robust to PS. Through simulations, we presented that under various disease scenarios the FB-GGRF has improved power over a commonly used family-based sequence kernel association test (FB-SKAT). Further, similar to GGRF, the proposed FB-GGRF method is asymptotically well-behaved, and does not require empirical adjustment of the type I error rates. We illustrate the proposed method using a study of congenital heart defects with family trios from the National Birth Defects Prevention Study (NBDPS).

  16. Geostatistics for environmental and geotechnical applications: A technology transferred

    SciTech Connect

    Cromer, M.V.

    1996-12-31

    Although successfully applied during the past few decades for predicting the spatial occurrences of properties that are cloaked from direct observation, geostatistical methods remain somewhat of a mystery to practitioners in the environmental and geotechnical fields. The techniques are powerful analytical tools that integrate numerical and statistical methods with scientific intuition and professional judgment to resolve conflicts between conceptual interpretation and direct measurement. This paper examines the practicality of these techniques within the entitled field of study and concludes by introducing a practical case study in which the geostatistical approach is thoroughly executed.

  17. Hydrogeologic unit flow characterization using transition probability geostatistics.

    PubMed

    Jones, Norman L; Walker, Justin R; Carle, Steven F

    2005-01-01

    This paper describes a technique for applying the transition probability geostatistics method for stochastic simulation to a MODFLOW model. Transition probability geostatistics has some advantages over traditional indicator kriging methods including a simpler and more intuitive framework for interpreting geologic relationships and the ability to simulate juxtapositional tendencies such as fining upward sequences. The indicator arrays generated by the transition probability simulation are converted to layer elevation and thickness arrays for use with the new Hydrogeologic Unit Flow package in MODFLOW 2000. This makes it possible to preserve complex heterogeneity while using reasonably sized grids and/or grids with nonuniform cell thicknesses.

  18. Geostatistical Study of Precipitation on the Island of Crete

    NASA Astrophysics Data System (ADS)

    Agou, Vasiliki D.; Varouchakis, Emmanouil A.; Hristopulos, Dionissios T.

    2015-04-01

    precipitation which are fitted locally to a three-parameter probability distribution, based on which a normalized index is derived. We use the Spartan variogram function to model space-time correlations, because it is more flexible than classical models [3]. The performance of the variogram model is tested by means of leave-one-out cross validation. The variogram model is then used in connection with ordinary kriging to generate precipitation maps for the entire island. In the future, we will explore the joint spatiotemporal evolution of precipitation patterns on Crete. References [1] P. Goovaerts. Geostatistical approaches for incorporating elevation into the spatial interpolation of precipitation. Journal of Hydrology, 228(1):113-129, 2000. [2] N. B. Guttman. Accepting the standardized precipitation index: a calculation algorithm. American Water Resource Association, 35(2):311-322, 1999. [3] D. T Hristopulos. Spartan Gibbs random field models for geostatistical applications. SIAM Journal on Scientific Computing, 24(6):2125-2162, 2003. [4] A.G. Koutroulis, A.-E.K. Vrohidou, and I.K. Tsanis. Spatiotemporal characteristics of meteorological drought for the island of Crete. Journal of Hydrometeorology, 12(2):206-226, 2011. [5] T. B. McKee, N. J. Doesken, and J. Kleist. The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology, page 179-184, Anaheim, California, 1993.

  19. High-resolution Geostatistical Inversion of the Transient Richards Equation

    NASA Astrophysics Data System (ADS)

    Klein, Ole; Bastian, Peter; Ippisch, Olaf

    2015-04-01

    The vadose zone and the complex physical processes in it play a vital role in our understanding of the environment. The production of most food is directly or indirectly linked to the growth of organic matter sustained by subsurface flow. For a reliable assessment of the influence of natural and anthropogenic changes to such a coupled system detailed knowledge about the flow patterns and dynamics is important, but the high spatial variability of subsurface hydraulic parameters makes reliable predictions about flow patterns difficult. Direct measurement of these properties is not possible, making indirect observations through dependent quantities and parameter estimation a necessity. The geostatistical approach characterizes these hydraulic parameters without predetermined zonation. The parameter fields are treated as stochastic processes, optionally incorporating a priori information in the probability distribution. Maximizing the likelihood of the parameters with regard to the given observations yields a parameter estimate with high spatial resolution. This approach naturally leads to non-linear least squares optimization problems that may theoretically be solved using standard techniques. However, the accurate numerical representation of the Richards equation necessitates high spatio-temporal resolution and therefore a large number of parameters, while time series of observed physical quantities typically lead to many data points to invert. This high dimensionality in both the parameter and observation space makes standard techniques infeasible. We present an extension of one of these existing inversion methods, developed for stationary flow in confined aquifers, to instationary flow regimes in partially saturated porous media. Our approach uses a Conjugate Gradients scheme preconditioned with the prior covariance matrix to avoid both multiplications with its inverse and the explicit assembly of the sensitivity matrix. Instead, one combined adjoint model run is

  20. Reducing uncertainty in geostatistical description with well testing pressure data

    SciTech Connect

    Reynolds, A.C.; He, Nanqun; Oliver, D.S.

    1997-08-01

    Geostatistics has proven to be an effective tool for generating realizations of reservoir properties conditioned to static data, e.g., core and log data and geologic knowledge. Due to the lack of closely spaced data in the lateral directions, there will be significant variability in reservoir descriptions generated by geostatistical simulation, i.e., significant uncertainty in the reservoir descriptions. In past work, we have presented procedures based on inverse problem theory for generating reservoir descriptions (rock property fields) conditioned to pressure data and geostatistical information represented as prior means for log-permeability and porosity and variograms. Although we have shown that the incorporation of pressure data reduces the uncertainty below the level contained in the geostatistical model based only on static information (the prior model), our previous results assumed did not explicitly account for uncertainties in the prior means and the parameters defining the variogram model. In this work, we investigate how pressure data can help detect errors in the prior means. If errors in the prior means are large and are not taken into account, realizations conditioned to pressure data represent incorrect samples of the a posteriori probability density function for the rock property fields, whereas, if the uncertainty in the prior mean is incorporated properly into the model, one obtains realistic realizations of the rock property fields.

  1. Gstat: a program for geostatistical modelling, prediction and simulation

    NASA Astrophysics Data System (ADS)

    Pebesma, Edzer J.; Wesseling, Cees G.

    1998-01-01

    Gstat is a computer program for variogram modelling, and geostatistical prediction and simulation. It provides a generic implementation of the multivariable linear model with trends modelled as a linear function of coordinate polynomials or of user-defined base functions, and independent or dependent, geostatistically modelled, residuals. Simulation in gstat comprises conditional or unconditional (multi-) Gaussian sequential simulation of point values or block averages, or (multi-) indicator sequential simulation. Besides many of the popular options found in other geostatistical software packages, gstat offers the unique combination of (i) an interactive user interface for modelling variograms and generalized covariances (residual variograms), that uses the device-independent plotting program gnuplot for graphical display, (ii) support for several ascii and binary data and map file formats for input and output, (iii) a concise, intuitive and flexible command language, (iv) user customization of program defaults, (v) no built-in limits, and (vi) free, portable ANSI-C source code. This paper describes the class of problems gstat can solve, and addresses aspects of efficiency and implementation, managing geostatistical projects, and relevant technical details.

  2. Geostatistical Modeling of Evolving Landscapes by Means of Image Quilting

    NASA Astrophysics Data System (ADS)

    Mendes, J. H.; Caers, J.; Scheidt, C.

    2015-12-01

    Realistic geological representation of subsurface heterogeneity remains an important outstanding challenge. While many geostatistical methods exist for representing sedimentary systems, such as multiple-point geostatistics, rule-based methods or Boolean methods, the question of what the prior uncertainty on parameters (or training images) of such algorithms are, remains outstanding. In this initial work, we investigate the use of flume experiments to constrain better such prior uncertainty and to start understanding what information should be provided to geostatistical algorithms. In particular, we study the use of image quilting as a novel multiple-point method for generating fast geostatistical realizations once a training image is provided. Image quilting is a method emanating from computer graphics where patterns are extracted from training images and then stochastically quilted along a raster path to create stochastic variation of the stated training image. In this initial study, we use a flume experiment and extract 10 training images as representative for the variability of the evolving landscape over a period of 136 minutes. The training images consists of wet/dry regions obtained from overhead shots taken over the flume experiment. To investigate whether such image quilting reproduces the same variability of the evolving landscape in terms of wet/dry regions, we generate multiple realizations with all 10 training images and compare that variability with the variability seen in the entire flume experiment. By proper tuning of the quilting parameters we find generally reasonable agreement with the flume experiment.

  3. A geostatistical and sampling analysis of regraded spoil materials

    SciTech Connect

    Myers, J.C.; Brown, T.H.

    1990-12-31

    Characterization of the pH and acid-base account levels in regraded spoil materials from mining operations is a difficult task due to mixing and the directional nature of product extraction. Geostatistical analysis of regraded spoil materials is currently being studied as the eventual methodology for determining sample grid size and sub-sample number for minesoil monitoring programs in the State of Texas. It is anticipated that geostatistics will soon be utilized for similar reasons at mine sites in other regions. In view of this, it is necessary to develop a position on geostatistics as a method for determining sample intensity necessary to statistically characterize Acid Forming Material (AFM) conditions existing in post-mined soils. A group of six Texas lignite mines has been analyzed using geostatistical methods. Acid-base account and pH values were mapped at four levels in each site. Determinations as to the confidence of the sampling programs were performed for all sites. Recommendations and strategies were developed for future sampling programs. Additional techniques to minimize sub-sample spacing were also developed.

  4. Geostatistical analysis of soil properties at field scale using standardized data

    NASA Astrophysics Data System (ADS)

    Millan, H.; Tarquis, A. M.; Pérez, L. D.; Matos, J.; González-Posada, M.

    2012-04-01

    Indentifying areas with physical degradation is a crucial step to ameliorate the effects in soil erosion. The quantification and interpretation of spatial variability is a key issue for site-specific soil management. Geostatistics has been the main methodological tool for implementing precision agriculture using field data collected at different spatial resolutions. Even though many works have made significant contributions to the body of knowledge on spatial statistics and its applications, some other key points need to be addressed for conducting precise comparisons between soil properties using geostatistical parameters. The objectives of the present work were (i) to quantify the spatial structure of different physical properties collected from a Vertisol, (ii) to search for potential correlations between different spatial patterns and (iii) to identify relevant components through multivariate spatial analysis. The study was conducted on a Vertisol (Typic Hapludert) dedicated to sugarcane (Saccharum officinarum L.) production during the last sixty years. We used six soil properties collected from a squared grid (225 points) (penetrometer resistance (PR), total porosity, fragmentation dimension (Df), vertical electrical conductivity (ECv), horizontal electrical conductivity (ECh) and soil water content (WC)). All the original data sets were z-transformed before geostatistical analysis. Three different types of semivariogram models were necessary for fitting individual experimental semivariograms. This suggests the different natures of spatial variability patterns. Soil water content rendered the largest nugget effect (C0 = 0.933) while soil total porosity showed the largest range of spatial correlation (A = 43.92 m). The bivariate geostatistical analysis also rendered significant cross-semivariance between different paired soil properties. However, four different semivariogram models were required in that case. This indicates an underlying co

  5. Key Issues and Strategies for Recruitment and Implementation in Large-Scale Randomized Controlled Trial Studies in Afterschool Settings. Afterschool Research Brief. Issue No. 2

    ERIC Educational Resources Information Center

    Jones, Debra Hughes; Vaden-Kiernan, Michael; Rudo, Zena; Fitzgerald, Robert; Hartry, Ardice; Chambers, Bette; Smith, Dewi; Muller, Patricia; Moss, Marcey A.

    2008-01-01

    Under the larger scope of the National Partnership for Quality Afterschool Learning, SEDL funded three awardees to carry out large-scale randomized controlled trials (RCT) assessing the efficacy of promising literacy curricula in afterschool settings on student academic achievement. SEDL provided analytic and technical support to the RCT studies…

  6. Optimal design of hydraulic head monitoring networks using space-time geostatistics

    NASA Astrophysics Data System (ADS)

    Herrera, G. S.; Júnez-Ferreira, H. E.

    2013-05-01

    This paper presents a new methodology for the optimal design of space-time hydraulic head monitoring networks and its application to the Valle de Querétaro aquifer in Mexico. The selection of the space-time monitoring points is done using a static Kalman filter combined with a sequential optimization method. The Kalman filter requires as input a space-time covariance matrix, which is derived from a geostatistical analysis. A sequential optimization method that selects the space-time point that minimizes a function of the variance, in each step, is used. We demonstrate the methodology applying it to the redesign of the hydraulic head monitoring network of the Valle de Querétaro aquifer with the objective of selecting from a set of monitoring positions and times, those that minimize the spatiotemporal redundancy. The database for the geostatistical space-time analysis corresponds to information of 273 wells located within the aquifer for the period 1970-2007. A total of 1,435 hydraulic head data were used to construct the experimental space-time variogram. The results show that from the existing monitoring program that consists of 418 space-time monitoring points, only 178 are not redundant. The implied reduction of monitoring costs was possible because the proposed method is successful in propagating information in space and time.

  7. Geostatistical regularization of inverse models for the retrieval of vegetation biophysical variables

    NASA Astrophysics Data System (ADS)

    Atzberger, C.; Richter, K.

    2009-09-01

    The robust and accurate retrieval of vegetation biophysical variables using radiative transfer models (RTM) is seriously hampered by the ill-posedness of the inverse problem. With this research we further develop our previously published (object-based) inversion approach [Atzberger (2004)]. The object-based RTM inversion takes advantage of the geostatistical fact that the biophysical characteristics of nearby pixel are generally more similar than those at a larger distance. A two-step inversion based on PROSPECT+SAIL generated look-up-tables is presented that can be easily implemented and adapted to other radiative transfer models. The approach takes into account the spectral signatures of neighboring pixel and optimizes a common value of the average leaf angle (ALA) for all pixel of a given image object, such as an agricultural field. Using a large set of leaf area index (LAI) measurements (n = 58) acquired over six different crops of the Barrax test site, Spain), we demonstrate that the proposed geostatistical regularization yields in most cases more accurate and spatially consistent results compared to the traditional (pixel-based) inversion. Pros and cons of the approach are discussed and possible future extensions presented.

  8. [Geostatistical analysis on distribution pattern of the tobacco budworm larva in Enshi, Hubei, China].

    PubMed

    Xia, Peng-Liang; Wang, Rui; Tan, Jun

    2014-03-01

    Tobacco budworm (Helicoverpa assulta) larvae feed on tobacco leaves (Nicotiana sp.), resulting in significant loss in tobacco production. Geostatistical method was used to analyze H. assulta spatial patterns and dynamics in this paper. The results showed that, H. assulta larvae appeared 40 days after the tobacco plants transplanting, and reached its peak at the early-mature period. The nested spherical and exponential model was the major model for tobacco budworm larva in the field, suggesting its aggregated distribution. The spatial variability C/(C0 + C) was larger than 0.75, which indicated H. assulta larva had wider structural variation and narrower random variation. There was a massive migration of tobacco budworm larva in the fast-growing stage of tobacco. Its quantity became stable after that, especially at the mature stage of tobacco.

  9. Geostatistical joint inversion of seismic and potential field methods

    NASA Astrophysics Data System (ADS)

    Shamsipour, Pejman; Chouteau, Michel; Giroux, Bernard

    2016-04-01

    Interpretation of geophysical data needs to integrate different types of information to make the proposed model geologically realistic. Multiple data sets can reduce uncertainty and non-uniqueness present in separate geophysical data inversions. Seismic data can play an important role in mineral exploration, however processing and interpretation of seismic data is difficult due to complexity of hard-rock geology. On the other hand, the recovered model from potential field methods is affected by inherent non uniqueness caused by the nature of the physics and by underdetermination of the problem. Joint inversion of seismic and potential field data can mitigate weakness of separate inversion of these methods. A stochastic joint inversion method based on geostatistical techniques is applied to estimate density and velocity distributions from gravity and travel time data. The method fully integrates the physical relations between density-gravity, on one hand, and slowness-travel time, on the other hand. As a consequence, when the data are considered noise-free, the responses from the inverted slowness and density data exactly reproduce the observed data. The required density and velocity auto- and cross-covariance are assumed to follow a linear model of coregionalization (LCM). The recent development of nonlinear model of coregionalization could also be applied if needed. The kernel function for the gravity method is obtained by the closed form formulation. For ray tracing, we use the shortest-path methods (SPM) to calculate the operation matrix. The jointed inversion is performed on structured grid; however, it is possible to extend it to use unstructured grid. The method is tested on two synthetic models: a model consisting of two objects buried in a homogeneous background and a model with stochastic distribution of parameters. The results illustrate the capability of the method to improve the inverted model compared to the separate inverted models with either gravity

  10. Three-dimensional ERT imaging by the geostatistical approach

    NASA Astrophysics Data System (ADS)

    Kitanidis, P. K.; Lee, J. H.

    2015-12-01

    Electric resistivity tomography (ERT), with observations made at the surface or in boreholes, is a method of imaging the subsurface with many potential applications in areas that include hydrology and environmental engineering. The estimation of the resistivity function from observations is a classic inverse problem. One method to solve this problem is the Geostatistical Approach (GA), which is a stochastic method that allows one to explore the range of possible solutions. GA is an objective and empirical Bayes method. The emphasis of this talk is on methods to reduce the computational cost of implementing this approach. We will show examples of application of the Principal Component Geostatistical Approach (PCGA). PCGA is Jacobian-free and uses forward solvers as black boxes. It utilizes the leading principal components from the prior covariance to obtain a good approximation of the solution at a fraction of the cost.

  11. Stochastic Local Interaction (SLI) model: Bridging machine learning and geostatistics

    NASA Astrophysics Data System (ADS)

    Hristopulos, Dionissios T.

    2015-12-01

    Machine learning and geostatistics are powerful mathematical frameworks for modeling spatial data. Both approaches, however, suffer from poor scaling of the required computational resources for large data applications. We present the Stochastic Local Interaction (SLI) model, which employs a local representation to improve computational efficiency. SLI combines geostatistics and machine learning with ideas from statistical physics and computational geometry. It is based on a joint probability density function defined by an energy functional which involves local interactions implemented by means of kernel functions with adaptive local kernel bandwidths. SLI is expressed in terms of an explicit, typically sparse, precision (inverse covariance) matrix. This representation leads to a semi-analytical expression for interpolation (prediction), which is valid in any number of dimensions and avoids the computationally costly covariance matrix inversion.

  12. Hydrogeologic Unit Flow Characterization Using Transition Probability Geostatistics

    SciTech Connect

    Jones, N L; Walker, J R; Carle, S F

    2003-11-21

    This paper describes a technique for applying the transition probability geostatistics method for stochastic simulation to a MODFLOW model. Transition probability geostatistics has several advantages over traditional indicator kriging methods including a simpler and more intuitive framework for interpreting geologic relationships and the ability to simulate juxtapositional tendencies such as fining upwards sequences. The indicator arrays generated by the transition probability simulation are converted to layer elevation and thickness arrays for use with the new Hydrogeologic Unit Flow (HUF) package in MODFLOW 2000. This makes it possible to preserve complex heterogeneity while using reasonably sized grids. An application of the technique involving probabilistic capture zone delineation for the Aberjona Aquifer in Woburn, Ma. is included.

  13. Assessing the resolution-dependent utility of tomograms for geostatistics

    USGS Publications Warehouse

    Day-Lewis, F. D.; Lane, J.W.

    2004-01-01

    Geophysical tomograms are used increasingly as auxiliary data for geostatistical modeling of aquifer and reservoir properties. The correlation between tomographic estimates and hydrogeologic properties is commonly based on laboratory measurements, co-located measurements at boreholes, or petrophysical models. The inferred correlation is assumed uniform throughout the interwell region; however, tomographic resolution varies spatially due to acquisition geometry, regularization, data error, and the physics underlying the geophysical measurements. Blurring and inversion artifacts are expected in regions traversed by few or only low-angle raypaths. In the context of radar traveltime tomography, we derive analytical models for (1) the variance of tomographic estimates, (2) the spatially variable correlation with a hydrologic parameter of interest, and (3) the spatial covariance of tomographic estimates. Synthetic examples demonstrate that tomograms of qualitative value may have limited utility for geostatistics; moreover, the imprint of regularization may preclude inference of meaningful spatial statistics from tomograms.

  14. Geostatistics for natural resources characterization. Part 1 and part 2

    SciTech Connect

    Verly, G.

    1984-01-01

    This collection of 33 research papers (in two volumes) bears witness to the fact that applications of geostatistics are no longer limited to the mining industry. Given are reports made in fields such as hydrology, soil sciences, pollution control, and geotechnical engineering. Contents, abridged: (pt.1) Variogram. Kriging. Recoverage reserves. Spectral analysis and data analysis. (pt. 2) Applications in the petroleum industry and automatics contouring. Applications in hydrogeology and geochemical exploration. Case studies in ore reserves estimation. Simulation. Index.

  15. Modal Petrology and Geostatistics of the Blue Hills Igneous Complex, Boston, Massachusetts, by Rietveld X-ray Diffraction: Multi-scalar Investigation of Volcanic and Intrusive Relationships

    NASA Astrophysics Data System (ADS)

    Besancon, J. R.; Spence, T. M.

    2004-12-01

    The Blue Hills Igneous Complex of eastern Massachusetts consists of mildly peralkaline volcanic and intrusive units including the Quincy Granite, the Blue Hills Porphyry, and a set of mainly pyroclastic rhyolite flow units traditionally called the Aporhyolite. Similar whole-rock chemistry has led most workers to assume that they are related rocks, despite some unclear field relationships. Kaktins (1976) divided the volcanic rocks into six units, but buried contacts do not permit confidence in either their number or stratigraphic position. To test a new method of modal analysis of these rocks, thirty-five samples were crushed, ground to approximately 5 micrometers, spray-dried to produce randomly oriented powder, and analyzed by x-ray diffraction. A constant eleven-phase Rietveld starting model was applied to the x-ray spectra, and then refined to produce a modal database of phase proportions in each sample. Geostatistical analysis with GIS software delineates a number of trends, with statistical measures of uncertainty. Aegirine in volcanics decreases in abundance with distance south from the E-W contact of volcanic rocks and granite. Riebeckite is found in the granite (both as veins and as apparently magmatic crystals) and the porphyry, but is less abundant or absent among the volcanic rocks. Where both amphibole and pyroxene are present, they are negatively correlated. The goal is to develop an additional tool for correlation of volcanic rocks, one based on mineral proportions in both aphanitic and phaneritic rocks.

  16. Breast carcinoma, intratumour heterogeneity and histological grading, using geostatistics.

    PubMed

    Sharifi-Salamatian, V; de Roquancourt, A; Rigaut, J P

    2000-01-01

    Tumour progression is currently believed to result from genetic instability. Chromosomal patterns specific of a type of cancer are frequent even though phenotypic spatial heterogeneity is omnipresent. The latter is the usual cause of histological grading imprecision, a well documented problem, without any fully satisfactory solution up to now. The present article addresses this problem in breast carcinoma. The assessment of a genetic marker for human tumours requires quantifiable measures of intratumoral heterogeneity. If any invariance paradigm representing a stochastic or geostatistic function could be discovered, this might help in solving the grading problem. A novel methodological approach using geostatistics to measure heterogeneity is used. Twenty tumours from the three usual (Scarff-Bloom and Richardson) grades were obtained and paraffin sections stained by MIB-1 (Ki-67) and peroxidase staining. Whole two-dimensional sections were sampled. Morphometric grids of variable sizes allowed a simple and fast recording of positions of epithelial nuclei, marked or not by MIB-1. The geostatistical method is based here upon the asymptotic behaviour of dispersion variance. Measure of asymptotic exponent of dispersion variance shows an increase from grade 1 to grade 3. Preliminary results are encouraging: grades 1 and 3 on one hand and 2 and 3 on the other hand are totally separated. The final proof of an improved grading using this measure will of course require a confrontation with the results of survival studies.

  17. Addressing uncertainty in rock properties through geostatistical simulation

    SciTech Connect

    McKenna, S.A.; Rautman, A.; Cromer, M.V.; Zelinski, W.P.

    1996-09-01

    Fracture and matrix properties in a sequence of unsaturated, welded tuffs at Yucca Mountain, Nevada, are modeled in two-dimensional cross-sections through geostatistical simulation. In the absence of large amounts of sample data, an n interpretive, deterministic, stratigraphic model is coupled with a gaussian simulation algorithm to constrain realizations of both matrix porosity and fracture frequency. Use of the deterministic, stratigraphic model imposes scientific judgment, in the form of a conceptual geologic model, onto the property realizations. Linear coregionalization and a regression relationship between matrix porosity and matrix hydraulic conductivity are used to generate realizations of matrix hydraulic conductivity. Fracture-frequency simulations conditioned on the stratigraphic model represent one class of fractures (cooling fractures) in the conceptual model of the geology. A second class of fractures (tectonic fractures) is conceptualized as fractures that cut across strata vertically and includes discrete features such as fault zones. Indicator geostatistical simulation provides locations of this second class of fractures. The indicator realizations are combined with the realizations of fracture spacing to create realizations of fracture frequency that are a combination of both classes of fractures. Evaluations of the resulting realizations include comparing vertical profiles of rock properties within the model to those observed in boreholes and checking intra-unit property distributions against collected data. Geostatistical simulation provides an efficient means of addressing spatial uncertainty in dual continuum rock properties.

  18. Application of geostatistics to coal-resource characterization and mine planning. Final report

    SciTech Connect

    Kauffman, P.W.; Walton, D.R.; Martuneac, L.; Kim, Y.C.; Knudsen, H.P.; Baafi, E.Y.; Lonergan, J.E.; Martino, F.

    1981-12-01

    Geostatistics is a proven method of ore reserve estimation in many non-coal mining areas but little has been published concerning its application to coal resources. This report presents the case for using geostatistics for coal mining applications and describes how a coal mining concern can best utilize geostatistical techniques for coal resource characterization and mine planning. An overview of the theory of geostatistics is also presented. Many of the applications discussed are documented in case studies that are a part of the report. The results of an exhaustive literature search are presented and recommendations are made for needed future research and demonstration projects.

  19. Geostatistical analysis of disease data: estimation of cancer mortality risk from empirical frequencies using Poisson kriging

    PubMed Central

    Goovaerts, Pierre

    2005-01-01

    Background Cancer mortality maps are used by public health officials to identify areas of excess and to guide surveillance and control activities. Quality of decision-making thus relies on an accurate quantification of risks from observed rates which can be very unreliable when computed from sparsely populated geographical units or recorded for minority populations. This paper presents a geostatistical methodology that accounts for spatially varying population sizes and spatial patterns in the processing of cancer mortality data. Simulation studies are conducted to compare the performances of Poisson kriging to a few simple smoothers (i.e. population-weighted estimators and empirical Bayes smoothers) under different scenarios for the disease frequency, the population size, and the spatial pattern of risk. A public-domain executable with example datasets is provided. Results The analysis of age-adjusted mortality rates for breast and cervix cancers illustrated some key features of commonly used smoothing techniques. Because of the small weight assigned to the rate observed over the entity being smoothed (kernel weight), the population-weighted average leads to risk maps that show little variability. Other techniques assign larger and similar kernel weights but they use a different piece of auxiliary information in the prediction: global or local means for global or local empirical Bayes smoothers, and spatial combination of surrounding rates for the geostatistical estimator. Simulation studies indicated that Poisson kriging outperforms other approaches for most scenarios, with a clear benefit when the risk values are spatially correlated. Global empirical Bayes smoothers provide more accurate predictions under the least frequent scenario of spatially random risk. Conclusion The approach presented in this paper enables researchers to incorporate the pattern of spatial dependence of mortality rates into the mapping of risk values and the quantification of the

  20. Geostatistical upscaling of rain gauge data to support uncertainty analysis of lumped urban hydrological models

    NASA Astrophysics Data System (ADS)

    Muthusamy, Manoranjan; Schellart, Alma; Tait, Simon; Heuvelink, Gerard B. M.

    2017-02-01

    In this study we develop a method to estimate the spatially averaged rainfall intensity together with associated level of uncertainty using geostatistical upscaling. Rainfall data collected from a cluster of eight paired rain gauges in a 400 m × 200 m urban catchment are used in combination with spatial stochastic simulation to obtain optimal predictions of the spatially averaged rainfall intensity at any point in time within the urban catchment. The uncertainty in the prediction of catchment average rainfall intensity is obtained for multiple combinations of intensity ranges and temporal averaging intervals. The two main challenges addressed in this study are scarcity of rainfall measurement locations and non-normality of rainfall data, both of which need to be considered when adopting a geostatistical approach. Scarcity of measurement points is dealt with by pooling sample variograms of repeated rainfall measurements with similar characteristics. Normality of rainfall data is achieved through the use of normal score transformation. Geostatistical models in the form of variograms are derived for transformed rainfall intensity. Next spatial stochastic simulation which is robust to nonlinear data transformation is applied to produce realisations of rainfall fields. These realisations in transformed space are first back-transformed and next spatially aggregated to derive a random sample of the spatially averaged rainfall intensity. Results show that the prediction uncertainty comes mainly from two sources: spatial variability of rainfall and measurement error. At smaller temporal averaging intervals both these effects are high, resulting in a relatively high uncertainty in prediction. With longer temporal averaging intervals the uncertainty becomes lower due to stronger spatial correlation of rainfall data and relatively smaller measurement error. Results also show that the measurement error increases with decreasing rainfall intensity resulting in a higher

  1. Geostatistical modeling of a portion of the alluvial aquifer of Mexico City

    NASA Astrophysics Data System (ADS)

    Morales-Casique, E.; Medina-Ortega, P.; Escolero-Fuentes, O.; Hernandez Espriu, A.

    2012-12-01

    Mexico City is one of the largest cities in the world and the pressure exerted on water resources generates problems such as intensive groundwater exploitation, subsidence and groundwater pollution. Most of the main aquifer under exploitation underlies lacustrine sediments and it is composed of a highly heterogeneous mixture of alluvial deposits and volcanic rocks. Lithological records from 113 production water wells are analyzed using indicator geostatistics. The different lithological categories are grouped into four hydrofacies, where a hydrofacies is a set of lithological categories which have similar hydraulic properties. An exponential variogram model was fitted to each hydrofacies by minimizing cross validation errors. The data is then kriged to obtain the three-dimensional distribution of each hydrofacies within the alluvial aquifer of Mexico City.

  2. PRagmatic trial Of Video Education in Nursing homes: The design and rationale for a pragmatic cluster randomized trial in the nursing home setting.

    PubMed

    Mor, Vincent; Volandes, Angelo E; Gutman, Roee; Gatsonis, Constantine; Mitchell, Susan L

    2017-04-01

    Background/Aims Nursing homes are complex healthcare systems serving an increasingly sick population. Nursing homes must engage patients in advance care planning, but do so inconsistently. Video decision support tools improved advance care planning in small randomized controlled trials. Pragmatic trials are increasingly employed in health services research, although not commonly in the nursing home setting to which they are well-suited. This report presents the design and rationale for a pragmatic cluster randomized controlled trial that evaluated the "real world" application of an Advance Care Planning Video Program in two large US nursing home healthcare systems. Methods PRagmatic trial Of Video Education in Nursing homes was conducted in 360 nursing homes (N = 119 intervention/N = 241 control) owned by two healthcare systems. Over an 18-month implementation period, intervention facilities were instructed to offer the Advance Care Planning Video Program to all patients. Control facilities employed usual advance care planning practices. Patient characteristics and outcomes were ascertained from Medicare Claims, Minimum Data Set assessments, and facility electronic medical record data. Intervention adherence was measured using a Video Status Report embedded into electronic medical record systems. The primary outcome was the number of hospitalizations/person-day alive among long-stay patients with advanced dementia or cardiopulmonary disease. The rationale for the approaches to facility randomization and recruitment, intervention implementation, population selection, data acquisition, regulatory issues, and statistical analyses are discussed. Results The large number of well-characterized candidate facilities enabled several unique design features including stratification on historical hospitalization rates, randomization prior to recruitment, and 2:1 control to intervention facilities ratio. Strong endorsement from corporate leadership made randomization

  3. Mapping aboveground woody biomass using forest inventory, remote sensing and geostatistical techniques.

    PubMed

    Yadav, Bechu K V; Nandy, S

    2015-05-01

    Mapping forest biomass is fundamental for estimating CO₂ emissions, and planning and monitoring of forests and ecosystem productivity. The present study attempted to map aboveground woody biomass (AGWB) integrating forest inventory, remote sensing and geostatistical techniques, viz., direct radiometric relationships (DRR), k-nearest neighbours (k-NN) and cokriging (CoK) and to evaluate their accuracy. A part of the Timli Forest Range of Kalsi Soil and Water Conservation Division, Uttarakhand, India was selected for the present study. Stratified random sampling was used to collect biophysical data from 36 sample plots of 0.1 ha (31.62 m × 31.62 m) size. Species-specific volumetric equations were used for calculating volume and multiplied by specific gravity to get biomass. Three forest-type density classes, viz. 10-40, 40-70 and >70% of Shorea robusta forest and four non-forest classes were delineated using on-screen visual interpretation of IRS P6 LISS-III data of December 2012. The volume in different strata of forest-type density ranged from 189.84 to 484.36 m(3) ha(-1). The total growing stock of the forest was found to be 2,024,652.88 m(3). The AGWB ranged from 143 to 421 Mgha(-1). Spectral bands and vegetation indices were used as independent variables and biomass as dependent variable for DRR, k-NN and CoK. After validation and comparison, k-NN method of Mahalanobis distance (root mean square error (RMSE) = 42.25 Mgha(-1)) was found to be the best method followed by fuzzy distance and Euclidean distance with RMSE of 44.23 and 45.13 Mgha(-1) respectively. DRR was found to be the least accurate method with RMSE of 67.17 Mgha(-1). The study highlighted the potential of integrating of forest inventory, remote sensing and geostatistical techniques for forest biomass mapping.

  4. Analysis of vadose zone tritium transport from an underground storage tank release using numerical modeling and geostatistics

    SciTech Connect

    Lee, K.H.

    1997-09-01

    Numerical and geostatistical analyses show that the artificial smoothing effect of kriging removes high permeability flow paths from hydrogeologic data sets, reducing simulated contaminant transport rates in heterogeneous vadose zone systems. therefore, kriging alone is not recommended for estimating the spatial distribution of soil hydraulic properties for contaminant transport analysis at vadose zone sites. Vadose zone transport if modeled more effectively by combining kriging with stochastic simulation to better represent the high degree of spatial variability usually found in the hydraulic properties of field soils. However, kriging is a viable technique for estimating the initial mass distribution of contaminants in the subsurface.

  5. EFFICIENT MODEL-FITTING AND MODEL-COMPARISON FOR HIGH-DIMENSIONAL BAYESIAN GEOSTATISTICAL MODELS. (R826887)

    EPA Science Inventory

    Geostatistical models are appropriate for spatially distributed data measured at irregularly spaced locations. We propose an efficient Markov chain Monte Carlo (MCMC) algorithm for fitting Bayesian geostatistical models with substantial numbers of unknown parameters to sizable...

  6. Characterization of indoor air contaminants in a randomly selected set of commercial nail salons in Salt Lake County, Utah, USA.

    PubMed

    Alaves, Victor M; Sleeth, Darrah K; Thiese, Matthew S; Larson, Rodney R

    2013-01-01

    Air samples were collected in 12 randomly selected commercial nail salons in Salt Lake County, Utah. Measurements of salon physical/chemical parameters (room volume, CO2 levels) were obtained. Volatile organic compound (VOC) concentrations were collected using summa air canisters and sorbent media tubes for an 8-h period. Multivariate analyses were used to identify relationships between salon physical/chemical characteristics and the VOCs found in the air samples. The ACGIH(®) additive mixing formula was also applied to determine if there were potential overexposures to the combined airborne concentrations of chemicals monitored. Methyl methacrylate was detected in 58% of the establishments despite having been banned for use in nail products by the state of Utah. Formaldehyde was found above the NIOSH REL(®) (0.016 ppm) in 58% of the establishments. Given the assortment of VOCs to which nail salon workers are potentially exposed, a combination of engineering as well as personal protective equipment is recommended.

  7. Geostatistical modeling of uncertainty of the spatial distribution of available phosphorus in soil in a sugarcane field

    NASA Astrophysics Data System (ADS)

    Tadeu Pereira, Gener; Ribeiro de Oliveira, Ismênia; De Bortoli Teixeira, Daniel; Arantes Camargo, Livia; Rodrigo Panosso, Alan; Marques, José, Jr.

    2015-04-01

    Phosphorus is one of the limiting nutrients for sugarcane development in Brazilian soils. The spatial variability of this nutrient is great, defined by the properties that control its adsorption and desorption reactions. Spatial estimates to characterize this variability are based on geostatistical interpolation. Thus, the assessment of the uncertainty of estimates associated with the spatial distribution of available P (Plabile) is decisive to optimize the use of phosphate fertilizers. The purpose of this study was to evaluate the performance of sequential Gaussian simulation (sGs) and ordinary kriging (OK) in the modeling of uncertainty in available P estimates. A sampling grid with 626 points was established in a 200-ha experimental sugarcane field in Tabapuã, São Paulo State, Brazil. The soil was sampled in the crossover points of a regular grid with intervals of 50 m. From the observations, 63 points, approximately 10% of sampled points were randomly selected before the geostatistical modeling of the composition of a data set used in the validation process modeling, while the remaining 563 points were used for the predictions variable in a place not sampled. The sGs generated 200 realizations. From the realizations generated, different measures of estimation and uncertainty were obtained. The standard deviation, calculated point to point, all simulated maps provided the map of deviation, used to assess local uncertainty. The visual analysis of maps of the E-type and KO showed that the spatial patterns produced by both methods were similar, however, it was possible to observe the characteristic smoothing effect of the KO especially in regions with extreme values. The Standardized variograms of selected realizations sGs showed both range and model similar to the variogram of the Observed date of Plabile. The variogram KO showed a distinct structure of the observed data, underestimating the variability over short distances, presenting parabolic behavior near

  8. ON THE GEOSTATISTICAL APPROACH TO THE INVERSE PROBLEM. (R825689C037)

    EPA Science Inventory

    Abstract

    The geostatistical approach to the inverse problem is discussed with emphasis on the importance of structural analysis. Although the geostatistical approach is occasionally misconstrued as mere cokriging, in fact it consists of two steps: estimation of statist...

  9. Geospatial Interpolation and Mapping of Tropospheric Ozone Pollution Using Geostatistics

    PubMed Central

    Kethireddy, Swatantra R.; Tchounwou, Paul B.; Ahmad, Hafiz A.; Yerramilli, Anjaneyulu; Young, John H.

    2014-01-01

    Tropospheric ozone (O3) pollution is a major problem worldwide, including in the United States of America (USA), particularly during the summer months. Ozone oxidative capacity and its impact on human health have attracted the attention of the scientific community. In the USA, sparse spatial observations for O3 may not provide a reliable source of data over a geo-environmental region. Geostatistical Analyst in ArcGIS has the capability to interpolate values in unmonitored geo-spaces of interest. In this study of eastern Texas O3 pollution, hourly episodes for spring and summer 2012 were selectively identified. To visualize the O3 distribution, geostatistical techniques were employed in ArcMap. Using ordinary Kriging, geostatistical layers of O3 for all the studied hours were predicted and mapped at a spatial resolution of 1 kilometer. A decent level of prediction accuracy was achieved and was confirmed from cross-validation results. The mean prediction error was close to 0, the root mean-standardized-prediction error was close to 1, and the root mean square and average standard errors were small. O3 pollution map data can be further used in analysis and modeling studies. Kriging results and O3 decadal trends indicate that the populace in Houston-Sugar Land-Baytown, Dallas-Fort Worth-Arlington, Beaumont-Port Arthur, San Antonio, and Longview are repeatedly exposed to high levels of O3-related pollution, and are prone to the corresponding respiratory and cardiovascular health effects. Optimization of the monitoring network proves to be an added advantage for the accurate prediction of exposure levels. PMID:24434594

  10. Assessment of spatial distribution of fallout radionuclides through geostatistics concept.

    PubMed

    Mabit, L; Bernard, C

    2007-01-01

    After introducing geostatistics concept and its utility in environmental science and especially in Fallout Radionuclide (FRN) spatialisation, a case study for cesium-137 ((137)Cs) redistribution at the field scale using geostatistics is presented. On a Canadian agricultural field, geostatistics coupled with a Geographic Information System (GIS) was used to test three different techniques of interpolation [Ordinary Kriging (OK), Inverse Distance Weighting power one (IDW1) and two (IDW2)] to create a (137)Cs map and to establish a radioisotope budget. Following the optimization of variographic parameters, an experimental semivariogram was developed to determine the spatial dependence of (137)Cs. It was adjusted to a spherical isotropic model with a range of 30 m and a very small nugget effect. This (137)Cs semivariogram showed a good autocorrelation (R(2)=0.91) and was well structured ('nugget-to-sill' ratio of 4%). It also revealed that the sampling strategy was adequate to reveal the spatial correlation of (137)Cs. The spatial redistribution of (137)Cs was estimated by Ordinary Kriging and IDW to produce contour maps. A radioisotope budget was established for the 2.16 ha agricultural field under investigation. It was estimated that around 2 x 10(7)Bq of (137)Cs were missing (around 30% of the total initial fallout) and were exported by physical processes (runoff and erosion processes) from the area under investigation. The cross-validation analysis showed that in the case of spatially structured data, OK is a better interpolation method than IDW1 or IDW2 for the assessment of potential radioactive contamination and/or pollution.

  11. Geospatial interpolation and mapping of tropospheric ozone pollution using geostatistics.

    PubMed

    Kethireddy, Swatantra R; Tchounwou, Paul B; Ahmad, Hafiz A; Yerramilli, Anjaneyulu; Young, John H

    2014-01-10

    Tropospheric ozone (O3) pollution is a major problem worldwide, including in the United States of America (USA), particularly during the summer months. Ozone oxidative capacity and its impact on human health have attracted the attention of the scientific community. In the USA, sparse spatial observations for O3 may not provide a reliable source of data over a geo-environmental region. Geostatistical Analyst in ArcGIS has the capability to interpolate values in unmonitored geo-spaces of interest. In this study of eastern Texas O3 pollution, hourly episodes for spring and summer 2012 were selectively identified. To visualize the O3 distribution, geostatistical techniques were employed in ArcMap. Using ordinary Kriging, geostatistical layers of O3 for all the studied hours were predicted and mapped at a spatial resolution of 1 kilometer. A decent level of prediction accuracy was achieved and was confirmed from cross-validation results. The mean prediction error was close to 0, the root mean-standardized-prediction error was close to 1, and the root mean square and average standard errors were small. O3 pollution map data can be further used in analysis and modeling studies. Kriging results and O3 decadal trends indicate that the populace in Houston-Sugar Land-Baytown, Dallas-Fort Worth-Arlington, Beaumont-Port Arthur, San Antonio, and Longview are repeatedly exposed to high levels of O3-related pollution, and are prone to the corresponding respiratory and cardiovascular health effects. Optimization of the monitoring network proves to be an added advantage for the accurate prediction of exposure levels.

  12. Using geostatistics to predict the characteristics of washed coal

    SciTech Connect

    Armstrong, M.

    1984-04-01

    This paper was presented to an SME-AIME meeting in 1981. The established techniques of linear geostatistics (ordinary kriging) can be used to estimate the total tonnage and the grade of coal in situ; more sophisticated techniques are required for predicting the characteristics of washed coal in situ. Two approaches are being investigated. One involves a parametric model of the washability curves and disjunctive kriging. The other is similar to the service variable approach used for estimating recoverable uranium reserves. This latter method is described in this paper.

  13. Brain lesion detection in MRI with fuzzy and geostatistical models.

    PubMed

    Pham, Tuan D

    2010-01-01

    Automated image detection of white matter changes of the brain is essentially helpful in providing a quantitative measure for studying the association of white matter lesions with other types of biomedical data. Such study allows the possibility of several medical hypothesis validations which lead to therapeutic treatment and prevention. This paper presents a new clustering-based segmentation approach for detecting white matter changes in magnetic resonance imaging with particular reference to cognitive decline in the elderly. The proposed method is formulated using the principles of fuzzy c-means algorithm and geostatistics.

  14. Mercury emissions from coal combustion in Silesia, analysis using geostatistics

    NASA Astrophysics Data System (ADS)

    Zasina, Damian; Zawadzki, Jaroslaw

    2015-04-01

    Data provided by the UNEP's report on mercury [1] shows that solid fuel combustion in significant source of mercury emission to air. Silesia, located in southwestern Poland, is notably affected by mercury emission due to being one of the most industrialized Polish regions: the place of coal mining, production of metals, stone mining, mineral quarrying and chemical industry. Moreover, Silesia is the region with high population density. People are exposed to severe risk of mercury emitted from both: industrial and domestic sources (i.e. small household furnaces). Small sources have significant contribution to total emission of mercury. Official and statistical analysis, including prepared for international purposes [2] did not provide data about spatial distribution of the mercury emitted to air, however number of analysis on Polish public power and energy sector had been prepared so far [3; 4]. The distribution of locations exposed for mercury emission from small domestic sources is interesting matter merging information from various sources: statistical, economical and environmental. This paper presents geostatistical approach to distibution of mercury emission from coal combustion. Analysed data organized in 2 independent levels: individual, bottom-up approach derived from national emission reporting system [5; 6] and top down - regional data calculated basing on official statistics [7]. Analysis, that will be presented, will include comparison of spatial distributions of mercury emission using data derived from sources mentioned above. Investigation will include three voivodeships of Poland: Lower Silesian, Opole (voivodeship) and Silesian using selected geostatistical methodologies including ordinary kriging [8]. References [1] UNEP. Global Mercury Assessment 2013: Sources, Emissions, Releases and Environmental Transport. UNEP Chemicals Branch, Geneva, Switzerland, 2013. [2] NCEM. Poland's Informative Inventory Report 2014. NCEM at the IEP-NRI, 2014. http

  15. Classroom-based Interventions and Teachers' Perceived Job Stressors and Confidence: Evidence from a Randomized Trial in Head Start Settings.

    PubMed

    Zhai, Fuhua; Raver, C Cybele; Li-Grining, Christine

    2011-09-01

    Preschool teachers' job stressors have received increasing attention but have been understudied in the literature. We investigated the impacts of a classroom-based intervention, the Chicago School Readiness Project (CSRP), on teachers' perceived job stressors and confidence, as indexed by their perceptions of job control, job resources, job demands, and confidence in behavior management. Using a clustered randomized controlled trial (RCT) design, the CSRP provided multifaceted services to the treatment group, including teacher training and mental health consultation, which were accompanied by stress-reduction services and workshops. Overall, 90 teachers in 35 classrooms at 18 Head Start sites participated in the study. After adjusting for teacher and classroom factors and site fixed effects, we found that the CSRP had significant effects on the improvement of teachers' perceived job control and work-related resources. We also found that the CSRP decreased teachers' confidence in behavior management and had no statistically significant effects on job demands. Overall, we did not find significant moderation effects of teacher race/ethnicity, education, teaching experience, or teacher type. The implications for research and policy are discussed.

  16. Associations of high-dose melphalan pharmacokinetics and outcomes in the setting of a randomized cryotherapy trial.

    PubMed

    Cho, Yu Kyoung; Sborov, Douglas W; Lamprecht, Misty; Li, Junan; Wang, Jiang; Hade, Erinn M; Gao, Yue; Tackett, Karen; Williams, Nita; Benson, Don M; Efebera, Yvonne A; Rosko, Ashley E; Devine, Steven M; Poi, Ming; Hofmeister, Craig C; Phelps, Mitch A

    2017-02-04

    High dose melphalan followed by autologous stem cell transplantation remains standard of care for eligible patients with multiple myeloma, but disease response and toxicity, including severe mucositis, varies among patients. Our randomized trial investigated duration of cryotherapy (2 and 6 hours) for reduction of mucositis prevalence and severity and explored factors associated with variability in pharmacokinetics and outcomes from melphalan therapy. The results demonstrate 2-hour is at least as effective as 6-hour cryotherapy in decreasing severe mucositis. From a population pharmacokinetic model, we identified fat free mass, hematocrit, and creatinine clearance were significant covariates, as had been reported previously. Furthermore, we observed the rs4240803 SLC7A5 polymorphism was significantly associated with pharmacokinetic variability, and pharmacokinetics was associated with both mucositis and neutropenia. However, melphalan exposure was not associated with progression-free or overall survival in our dataset. These findings contribute to ongoing efforts to personalize melphalan dosing in transplant patients. This article is protected by copyright. All rights reserved.

  17. Weight gain prevention in the school worksite setting: Results of a multi-level cluster randomized trial

    PubMed Central

    Lemon, Stephenie C.; Wang, Monica L.; Wedick, Nicole M.; Estabrook, Barbara; Druker, Susan; Schneider, Kristin L.; Li, Wenjun; Pbert, Lori

    2014-01-01

    Objective To describe the effectiveness, reach and implementation of a weight gain prevention intervention among public school employees. Method A multi-level intervention was tested in a cluster randomized trial among 782 employees in 12 central Massachusetts public high schools from 2009 to 2012. The intervention targeted the nutrition and physical activity environment and policies, the social environment and individual knowledge, attitudes and skills. The intervention was compared to a materials only condition. The primary outcome measures were change in weight and body mass index (BMI) at 24-month follow-up. Implementation of physical environment, policy and social environment strategies at the school and interpersonal levels, and intervention participation at the individual level were assessed. Results At 24-month follow-up, there was a net change (difference of the difference) of −3.03 pounds (p=.04) and of −.48 BMI units (p=.05) between intervention and comparison conditions. The majority of intervention strategies were successfully implemented by all intervention schools, although establishing formal policies was challenging. Employee participation in programs targeting the physical and social environment was maintained over time. Conclusion This study supports that a multi-level intervention integrated within the organizational culture can be successfully implemented and prevent weight gain in public high school employees. PMID:24345602

  18. Screening and Brief Intervention for Unhealthy Drug Use in Primary Care Settings: Randomized Clinical Trials Are Needed

    PubMed Central

    Saitz, Richard; Alford, Daniel P.; Bernstein, Judith; Cheng, Debbie M.; Samet, Jeffrey; Palfai, Tibor

    2010-01-01

    The efficacy of screening and brief intervention (SBI) for drug use in primary care patients is largely unknown. Because of this lack of evidence, US professional organizations do not recommend it. Yet, a strong theoretical case can be made for drug SBI. Drug use is common and associated with numerous health consequences, patients usually do not seek help for drug abuse and dependence, and SBI has proven efficacy for unhealthy alcohol use. On the other hand, the diversity of drugs of abuse and the high prevalence of abuse and dependence among those who use them raise concerns that drug SBI may have limited or no efficacy. Federal efforts to disseminate SBI for drug use are underway, and reimbursement codes to compensate clinicians for these activities have been developed. However, the discrepancies between science and policy developments underscore the need for evidence-based research regarding the efficacy of SBI for drug use. This article discusses the rationale for drug SBI and existing research on its potential to improve drug-use outcomes and makes the argument that randomized controlled trials to determine its efficacy are urgently needed to bridge the gap between research, policy, and clinical practice. PMID:20936079

  19. Enhancing multiple-point geostatistical modeling: 1. Graph theory and pattern adjustment

    NASA Astrophysics Data System (ADS)

    Tahmasebi, Pejman; Sahimi, Muhammad

    2016-03-01

    In recent years, higher-order geostatistical methods have been used for modeling of a wide variety of large-scale porous media, such as groundwater aquifers and oil reservoirs. Their popularity stems from their ability to account for qualitative data and the great flexibility that they offer for conditioning the models to hard (quantitative) data, which endow them with the capability for generating realistic realizations of porous formations with very complex channels, as well as features that are mainly a barrier to fluid flow. One group of such models consists of pattern-based methods that use a set of data points for generating stochastic realizations by which the large-scale structure and highly-connected features are reproduced accurately. The cross correlation-based simulation (CCSIM) algorithm, proposed previously by the authors, is a member of this group that has been shown to be capable of simulating multimillion cell models in a matter of a few CPU seconds. The method is, however, sensitive to pattern's specifications, such as boundaries and the number of replicates. In this paper the original CCSIM algorithm is reconsidered and two significant improvements are proposed for accurately reproducing large-scale patterns of heterogeneities in porous media. First, an effective boundary-correction method based on the graph theory is presented by which one identifies the optimal cutting path/surface for removing the patchiness and discontinuities in the realization of a porous medium. Next, a new pattern adjustment method is proposed that automatically transfers the features in a pattern to one that seamlessly matches the surrounding patterns. The original CCSIM algorithm is then combined with the two methods and is tested using various complex two- and three-dimensional examples. It should, however, be emphasized that the methods that we propose in this paper are applicable to other pattern-based geostatistical simulation methods.

  20. Geostatistical three-dimensional modeling of oolite shoals, St. Louis Limestone, southwest Kansas

    USGS Publications Warehouse

    Qi, L.; Carr, T.R.; Goldstein, R.H.

    2007-01-01

    In the Hugoton embayment of southwestern Kansas, reservoirs composed of relatively thin (<4 m; <13.1 ft) oolitic deposits within the St. Louis Limestone have produced more than 300 million bbl of oil. The geometry and distribution of oolitic deposits control the heterogeneity of the reservoirs, resulting in exploration challenges and relatively low recovery. Geostatistical three-dimensional (3-D) models were constructed to quantify the geometry and spatial distribution of oolitic reservoirs, and the continuity of flow units within Big Bow and Sand Arroyo Creek fields. Lithofacies in uncored wells were predicted from digital logs using a neural network. The tilting effect from the Laramide orogeny was removed to construct restored structural surfaces at the time of deposition. Well data and structural maps were integrated to build 3-D models of oolitic reservoirs using stochastic simulations with geometry data. Three-dimensional models provide insights into the distribution, the external and internal geometry of oolitic deposits, and the sedimentologic processes that generated reservoir intervals. The structural highs and general structural trend had a significant impact on the distribution and orientation of the oolitic complexes. The depositional pattern and connectivity analysis suggest an overall aggradation of shallow-marine deposits during pulses of relative sea level rise followed by deepening near the top of the St. Louis Limestone. Cemented oolitic deposits were modeled as barriers and baffles and tend to concentrate at the edge of oolitic complexes. Spatial distribution of porous oolitic deposits controls the internal geometry of rock properties. Integrated geostatistical modeling methods can be applicable to other complex carbonate or siliciclastic reservoirs in shallow-marine settings. Copyright ?? 2007. The American Association of Petroleum Geologists. All rights reserved.

  1. Comparative soil CO2 flux measurements and geostatistical estimation methods on Masaya volcano, Nicaragua

    USGS Publications Warehouse

    Lewicki, J.L.; Bergfeld, D.; Cardellini, C.; Chiodini, G.; Granieri, D.; Varley, N.; Werner, C.

    2005-01-01

    We present a comparative study of soil CO2 flux (FCO2) measured by five groups (Groups 1-5) at the IAVCEI-CCVG Eighth Workshop on Volcanic Gases on Masaya volcano, Nicaragua. Groups 1-5 measured (FCO2) using the accumulation chamber method at 5-m spacing within a 900 m2 grid during a morning (AM) period. These measurements were repeated by Groups 1-3 during an afternoon (PM) period. Measured (FCO2 ranged from 218 to 14,719 g m-2 day-1. The variability of the five measurements made at each grid point ranged from ??5 to 167%. However, the arithmetic means of fluxes measured over the entire grid and associated total CO2 emission rate estimates varied between groups by only ??22%. All three groups that made PM measurements reported an 8-19% increase in total emissions over the AM results. Based on a comparison of measurements made during AM and PM times, we argue that this change is due in large part to natural temporal variability of gas flow, rather than to measurement error. In order to estimate the mean and associated CO2 emission rate of one data set and to map the spatial FCO2 distribution, we compared six geostatistical methods: Arithmetic and minimum variance unbiased estimator means of uninterpolated data, and arithmetic means of data interpolated by the multiquadric radial basis function, ordinary kriging, multi-Gaussian kriging, and sequential Gaussian simulation methods. While the total CO2 emission rates estimated using the different techniques only varied by ??4.4%, the FCO2 maps showed important differences. We suggest that the sequential Gaussian simulation method yields the most realistic representation of the spatial distribution of FCO2, but a variety of geostatistical methods are appropriate to estimate the total CO2 emission rate from a study area, which is a primary goal in volcano monitoring research. ?? Springer-Verlag 2005.

  2. Geostatistical prediction of flow-duration curves in an index-flow framework

    NASA Astrophysics Data System (ADS)

    Pugliese, A.; Castellarin, A.; Brath, A.

    2014-09-01

    An empirical period-of-record flow-duration curve (FDC) describes the percentage of time (duration) in which a given streamflow was equaled or exceeded over an historical period of time. In many practical applications one has to construct FDCs in basins that are ungauged or where very few observations are available. We present an application strategy of top-kriging, which makes the geostatistical procedure capable of predicting FDCs in ungauged catchments. Previous applications of top-kriging mainly focused on the prediction of point streamflow indices (e.g. flood quantiles, low-flow indices, etc.); here the procedure is used to predict the entire curve in ungauged sites as a weighted average of standardised empirical FDCs through the traditional linear-weighting scheme of kriging methods. In particular, we propose to standardise empirical FDCs by a reference index-flow value (i.e. mean annual flow, or mean annual precipitation × the drainage area) and to compute the overall negative deviation of the curves from this reference value. We then propose to use these values, which we term total negative deviation (TND), for expressing the hydrological similarity between catchments and for deriving the geostatistical weights. We focus on the prediction of FDCs for 18 unregulated catchments located in central Italy, and we quantify the accuracy of the proposed technique under various operational conditions through an extensive cross-validation and sensitivity analysis. The cross-validation points out that top-kriging is a reliable approach for predicting FDCs with Nash-Sutcliffe efficiency measures ranging from 0.85 to 0.96 (depending on the model settings) very low biases over the entire duration range, and an enhanced representation of the low-flow regime relative to other regionalisation models that were recently developed for the same study region.

  3. Psychosocial education improves low back pain beliefs: results from a cluster randomized clinical trial (NCT00373009) in a primary prevention setting.

    PubMed

    George, Steven Z; Teyhen, Deydre S; Wu, Samuel S; Wright, Alison C; Dugan, Jessica L; Yang, Guijun; Robinson, Michael E; Childs, John D

    2009-07-01

    The general population has a pessimistic view of low back pain (LBP), and evidence-based information has been used to positively influence LBP beliefs in previously reported mass media studies. However, there is a lack of randomized trials investigating whether LBP beliefs can be modified in primary prevention settings. This cluster randomized clinical trial investigated the effect of an evidence-based psychosocial educational program (PSEP) on LBP beliefs for soldiers completing military training. A military setting was selected for this clinical trial, because LBP is a common cause of soldier disability. Companies of soldiers (n = 3,792) were recruited, and cluster randomized to receive a PSEP or no education (control group, CG). The PSEP consisted of an interactive seminar, and soldiers were issued the Back Book for reference material. The primary outcome measure was the back beliefs questionnaire (BBQ), which assesses inevitable consequences of and ability to cope with LBP. The BBQ was administered before randomization and 12 weeks later. A linear mixed model was fitted for the BBQ at the 12-week follow-up, and a generalized linear mixed model was fitted for the dichotomous outcomes on BBQ change of greater than two points. Sensitivity analyses were performed to account for drop out. BBQ scores (potential range: 9-45) improved significantly from baseline of 25.6 +/- 5.7 (mean +/- SD) to 26.9 +/- 6.2 for those receiving the PSEP, while there was a significant decline from 26.1 +/- 5.7 to 25.6 +/- 6.0 for those in the CG. The adjusted mean BBQ score at follow-up for those receiving the PSEP was 1.49 points higher than those in the CG (P < 0.0001). The adjusted odds ratio of BBQ improvement of greater than two points for those receiving the PSEP was 1.51 (95% CI = 1.22-1.86) times that of those in the CG. BBQ improvement was also mildly associated with race and college education. Sensitivity analyses suggested minimal influence of drop out. In conclusion, soldiers

  4. Effect of mobile reminders on screening yield during opportunistic screening for type 2 diabetes mellitus in a primary health care setting: A randomized trial

    PubMed Central

    Kumar, Sathish; Shewade, Hemant Deepak; Vasudevan, Kavita; Durairaju, Kathamuthu; Santhi, V.S.; Sunderamurthy, Bhuvaneswary; Krishnakumari, Velavane; Panigrahi, Krishna Chandra

    2015-01-01

    Objective. We wanted to study whether mobile reminders increased follow-up for definitive tests resulting in higher screening yield during opportunistic screening for diabetes. Methods. This was a facility-based parallel randomized controlled trial during routine outpatient department hours in a primary health care setting in Puducherry, India (2014). We offered random blood glucose testing to non-pregnant non-diabetes adults with age >30 years (667 total, 390 consented); eligible outpatients (random blood glucose ≥ 6.1 mmol/l, n = 268) were requested to follow-up for definitive tests (fasting and postprandial blood glucose). Eligible outpatients either received (intervention arm, n = 133) or did not receive mobile reminder (control arm, n = 135) to follow-up for definitive tests. We measured capillary blood glucose using a glucometer to make epidemiological diagnosis of diabetes. The trial was registered with Clinical Trial Registry of India (CTRI/2014/10/005138). Results. 85.7% of outpatients in intervention arm returned for definitive test when compared to 53.3% in control arm [Relative Risk = 1.61, (0.95 Confidence Interval — 1.35, 1.91)]. Screening yield in intervention and control arm was 18.6% and 10.2% respectively. Etiologic fraction was 45.2% and number needed to screen was 11.9. Conclusion. In countries like India, which is emerging as the diabetes capital of the world, considering the wide prevalent use of mobile phones, and real life resource limited settings in which this study was carried out, mobile reminders during opportunistic screening in primary health care setting improve screening yield of diabetes. PMID:26844130

  5. Psychoneuroendocrine effects of cognitive-behavioral stress management in a naturalistic setting--a randomized controlled trial.

    PubMed

    Gaab, J; Sonderegger, L; Scherrer, S; Ehlert, U

    2006-05-01

    It is assumed that chronic or extensive release of cortisol due to stress has deleterious effects on somatic and psychological health, making interventions aiming to reduce and/or normalize cortisol secretion to stress of interest. Cognitive-behavioral stress management (CBSM) has repeatedly been shown to effectively reduce cortisol responses to acute psychosocial stress. However, the effects of CBSM on psychoneuroendocrine responses during "real-life" stress have yet not been examined in healthy subjects. Eight weeks before all subjects took an important academic exam, 28 healthy economics students were randomly assigned to four weekly sessions of cognitive behavioral stress management (CBSM) training or a waiting control condition. Psychological and somatic symptoms were repeatedly assessed throughout the preparation period. Salivary cortisol (cortisol awakening response and short circadian cortisol profile) was repeatedly measured at baseline and on the day of the exam. In addition, cognitive appraisal was assessed on the day of the exam. Subjects in the CBSM group showed significantly lower anxiety and somatic symptom levels throughout the period prior to the exam. On the day of the exam, groups differed in their cortisol awakening stress responses, with significantly attenuated cortisol levels in controls. Short circadian cortisol levels did not differ between groups. Interestingly, groups differed in their associations between cortisol responses before the exam and cognitive stress appraisal, with dissociation in controls but not in the CBSM group. The results show that CBSM reduces psychological and somatic symptoms and influences the ability to show a cortisol response corresponding to subjectively perceived stress. In line with current psychoneuroendocrine models, the inability to mount a cortisol response corresponding to the cognitive appraisal in controls could be a result of a dysregulated HPA axis, probably as a consequence of longlasting stress.

  6. A multiple-point geostatistical approach to quantifying uncertainty for flow and transport simulation in geologically complex environments

    NASA Astrophysics Data System (ADS)

    Cronkite-Ratcliff, C.; Phelps, G. A.; Boucher, A.

    2011-12-01

    In many geologic settings, the pathways of groundwater flow are controlled by geologic heterogeneities which have complex geometries. Models of these geologic heterogeneities, and consequently, their effects on the simulated pathways of groundwater flow, are characterized by uncertainty. Multiple-point geostatistics, which uses a training image to represent complex geometric descriptions of geologic heterogeneity, provides a stochastic approach to the analysis of geologic uncertainty. Incorporating multiple-point geostatistics into numerical models provides a way to extend this analysis to the effects of geologic uncertainty on the results of flow simulations. We present two case studies to demonstrate the application of multiple-point geostatistics to numerical flow simulation in complex geologic settings with both static and dynamic conditioning data. Both cases involve the development of a training image from a complex geometric description of the geologic environment. Geologic heterogeneity is modeled stochastically by generating multiple equally-probable realizations, all consistent with the training image. Numerical flow simulation for each stochastic realization provides the basis for analyzing the effects of geologic uncertainty on simulated hydraulic response. The first case study is a hypothetical geologic scenario developed using data from the alluvial deposits in Yucca Flat, Nevada. The SNESIM algorithm is used to stochastically model geologic heterogeneity conditioned to the mapped surface geology as well as vertical drill-hole data. Numerical simulation of groundwater flow and contaminant transport through geologic models produces a distribution of hydraulic responses and contaminant concentration results. From this distribution of results, the probability of exceeding a given contaminant concentration threshold can be used as an indicator of uncertainty about the location of the contaminant plume boundary. The second case study considers a

  7. Random-phase approximation correlation energies from Lanczos chains and an optimal basis set: theory and applications to the benzene dimer.

    PubMed

    Rocca, Dario

    2014-05-14

    A new ab initio approach is introduced to compute the correlation energy within the adiabatic connection fluctuation dissipation theorem in the random phase approximation. First, an optimally small basis set to represent the response functions is obtained by diagonalizing an approximate dielectric matrix containing the kinetic energy contribution only. Then, the Lanczos algorithm is used to compute the full dynamical dielectric matrix and the correlation energy. The convergence issues with respect to the number of empty states or the dimension of the basis set are avoided and the dynamical effects are easily kept into account. To demonstrate the accuracy and efficiency of this approach the binding curves for three different configurations of the benzene dimer are computed: T-shaped, sandwich, and slipped parallel.

  8. A Bayesian geostatistical transfer function approach to tracer test analysis

    NASA Astrophysics Data System (ADS)

    Fienen, Michael N.; Luo, Jian; Kitanidis, Peter K.

    2006-07-01

    Reactive transport modeling is often used in support of bioremediation and chemical treatment planning and design. There remains a pressing need for practical and efficient models that do not require (or assume attainable) the high level of characterization needed by complex numerical models. We focus on a linear systems or transfer function approach to the problem of reactive tracer transport in a heterogeneous saprolite aquifer. Transfer functions are obtained through the Bayesian geostatistical inverse method applied to tracer injection histories and breakthrough curves. We employ nonparametric transfer functions, which require minimal assumptions about shape and structure. The resulting flexibility empowers the data to determine the nature of the transfer function with minimal prior assumptions. Nonnegativity is enforced through a reflected Brownian motion stochastic model. The inverse method enables us to quantify uncertainty and to generate conditional realizations of the transfer function. Complex information about a hydrogeologic system is distilled into a relatively simple but rigorously obtained function that describes the transport behavior of the system between two wells. The resulting transfer functions are valuable in reactive transport models based on traveltime and streamline methods. The information contained in the data, particularly in the case of strong heterogeneity, is not overextended but is fully used. This is the first application of Bayesian geostatistical inversion to transfer functions in hydrogeology but the methodology can be extended to any linear system.

  9. Validating spatial structure in canopy water content using geostatistics

    NASA Technical Reports Server (NTRS)

    Sanderson, E. W.; Zhang, M. H.; Ustin, S. L.; Rejmankova, E.; Haxo, R. S.

    1995-01-01

    Heterogeneity in ecological phenomena are scale dependent and affect the hierarchical structure of image data. AVIRIS pixels average reflectance produced by complex absorption and scattering interactions between biogeochemical composition, canopy architecture, view and illumination angles, species distributions, and plant cover as well as other factors. These scales affect validation of pixel reflectance, typically performed by relating pixel spectra to ground measurements acquired at scales of 1m(exp 2) or less (e.g., field spectra, foilage and soil samples, etc.). As image analysis becomes more sophisticated, such as those for detection of canopy chemistry, better validation becomes a critical problem. This paper presents a methodology for bridging between point measurements and pixels using geostatistics. Geostatistics have been extensively used in geological or hydrogeolocial studies but have received little application in ecological studies. The key criteria for kriging estimation is that the phenomena varies in space and that an underlying controlling process produces spatial correlation between the measured data points. Ecological variation meets this requirement because communities vary along environmental gradients like soil moisture, nutrient availability, or topography.

  10. Spatial analysis of hazardous waste data using geostatistics

    SciTech Connect

    Zirschky, J.H.

    1984-01-01

    The objective of this investigation was to determine if geostatistics could be a useful tool for evaluating hazardous waste sites. Three sites contaminated by dioxin (2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)) were investigated. The first site evaluated was a creek into which TCDD-contaminated soil had eroded. The second site was a town in which TCDD-contaminated wastes had been sprayed onto the streets. Finally, the third site was a highway of which the shoulders were contaminated by dust deposition from a nearby hazardous waste site. The distribution of TCDD at the first and third sites were investigated using kriging, an optimal estimation technique. By using kriging, the areas of both sites requiring cleanup were successfully identified. At the second site, the town, satisfactory results were not obtained. The distribution of contamination in this town is believed to be very heterogeneous; thus, reasonable estimates could not be obtained. Additional sampling was therefore recommended at this site. Based upon this research, geostatistics appears to be a very useful tool for evaluating a hazardous waste site if the distribution of contaminants at the site is homogeneous, or can be divided into homogeneous areas.

  11. Regional flow duration curves: Geostatistical techniques versus multivariate regression

    NASA Astrophysics Data System (ADS)

    Pugliese, Alessio; Farmer, William H.; Castellarin, Attilio; Archfield, Stacey A.; Vogel, Richard M.

    2016-10-01

    A period-of-record flow duration curve (FDC) represents the relationship between the magnitude and frequency of daily streamflows. Prediction of FDCs is of great importance for locations characterized by sparse or missing streamflow observations. We present a detailed comparison of two methods which are capable of predicting an FDC at ungauged basins: (1) an adaptation of the geostatistical method, Top-kriging, employing a linear weighted average of dimensionless empirical FDCs, standardised with a reference streamflow value; and (2) regional multiple linear regression of streamflow quantiles, perhaps the most common method for the prediction of FDCs at ungauged sites. In particular, Top-kriging relies on a metric for expressing the similarity between catchments computed as the negative deviation of the FDC from a reference streamflow value, which we termed total negative deviation (TND). Comparisons of these two methods are made in 182 largely unregulated river catchments in the southeastern U.S. using a three-fold cross-validation algorithm. Our results reveal that the two methods perform similarly throughout flow-regimes, with average Nash-Sutcliffe Efficiencies 0.566 and 0.662, (0.883 and 0.829 on log-transformed quantiles) for the geostatistical and the linear regression models, respectively. The differences between the reproduction of FDC's occurred mostly for low flows with exceedance probability (i.e. duration) above 0.98.

  12. Regional flow duration curves: Geostatistical techniques versus multivariate regression

    USGS Publications Warehouse

    Pugliese, Alessio; Farmer, William H.; Castellarin, Attilio; Archfield, Stacey A.; Vogel, Richard M.

    2016-01-01

    A period-of-record flow duration curve (FDC) represents the relationship between the magnitude and frequency of daily streamflows. Prediction of FDCs is of great importance for locations characterized by sparse or missing streamflow observations. We present a detailed comparison of two methods which are capable of predicting an FDC at ungauged basins: (1) an adaptation of the geostatistical method, Top-kriging, employing a linear weighted average of dimensionless empirical FDCs, standardised with a reference streamflow value; and (2) regional multiple linear regression of streamflow quantiles, perhaps the most common method for the prediction of FDCs at ungauged sites. In particular, Top-kriging relies on a metric for expressing the similarity between catchments computed as the negative deviation of the FDC from a reference streamflow value, which we termed total negative deviation (TND). Comparisons of these two methods are made in 182 largely unregulated river catchments in the southeastern U.S. using a three-fold cross-validation algorithm. Our results reveal that the two methods perform similarly throughout flow-regimes, with average Nash-Sutcliffe Efficiencies 0.566 and 0.662, (0.883 and 0.829 on log-transformed quantiles) for the geostatistical and the linear regression models, respectively. The differences between the reproduction of FDC's occurred mostly for low flows with exceedance probability (i.e. duration) above 0.98.

  13. A Controlled Design of Aligned and Random Nanofibers for 3D Bi-functionalized Nerve Conduits Fabricated via a Novel Electrospinning Set-up

    PubMed Central

    Kim, Jeong In; Hwang, Tae In; Aguilar, Ludwig Erik; Park, Chan Hee; Kim, Cheol Sang

    2016-01-01

    Scaffolds made of aligned nanofibers are favorable for nerve regeneration due to their superior nerve cell attachment and proliferation. However, it is challenging not only to produce a neat mat or a conduit form with aligned nanofibers but also to use these for surgical applications as a nerve guide conduit due to their insufficient mechanical strength. Furthermore, no studies have been reported on the fabrication of aligned nanofibers and randomly-oriented nanofibers on the same mat. In this study, we have successfully produced a mat with both aligned and randomly-oriented nanofibers by using a novel electrospinning set up. A new conduit with a highly-aligned electrospun mat is produced with this modified electrospinning method, and this proposed conduit with favorable features, such as selective permeability, hydrophilicity and nerve growth directional steering, were fabricated as nerve guide conduits (NGCs). The inner surface of the nerve conduit is covered with highly aligned electrospun nanofibers and is able to enhance the proliferation of neural cells. The central part of the tube is double-coated with randomly-oriented nanofibers over the aligned nanofibers, strengthening the weak mechanical strength of the aligned nanofibers. PMID:27021221

  14. Estimating the Burden of Malaria in Senegal: Bayesian Zero-Inflated Binomial Geostatistical Modeling of the MIS 2008 Data

    PubMed Central

    Giardina, Federica; Gosoniu, Laura; Konate, Lassana; Diouf, Mame Birame; Perry, Robert; Gaye, Oumar; Faye, Ousmane; Vounatsou, Penelope

    2012-01-01

    The Research Center for Human Development in Dakar (CRDH) with the technical assistance of ICF Macro and the National Malaria Control Programme (NMCP) conducted in 2008/2009 the Senegal Malaria Indicator Survey (SMIS), the first nationally representative household survey collecting parasitological data and malaria-related indicators. In this paper, we present spatially explicit parasitaemia risk estimates and number of infected children below 5 years. Geostatistical Zero-Inflated Binomial models (ZIB) were developed to take into account the large number of zero-prevalence survey locations (70%) in the data. Bayesian variable selection methods were incorporated within a geostatistical framework in order to choose the best set of environmental and climatic covariates associated with the parasitaemia risk. Model validation confirmed that the ZIB model had a better predictive ability than the standard Binomial analogue. Markov chain Monte Carlo (MCMC) methods were used for inference. Several insecticide treated nets (ITN) coverage indicators were calculated to assess the effectiveness of interventions. After adjusting for climatic and socio-economic factors, the presence of at least one ITN per every two household members and living in urban areas reduced the odds of parasitaemia by 86% and 81% respectively. Posterior estimates of the ORs related to the wealth index show a decreasing trend with the quintiles. Infection odds appear to be increasing with age. The population-adjusted prevalence ranges from 0.12% in Thillé-Boubacar to 13.1% in Dabo. Tambacounda has the highest population-adjusted predicted prevalence (8.08%) whereas the region with the highest estimated number of infected children under the age of 5 years is Kolda (13940). The contemporary map and estimates of malaria burden identify the priority areas for future control interventions and provide baseline information for monitoring and evaluation. Zero-Inflated formulations are more appropriate in

  15. Effectiveness of intensive group and individual interventions for smoking cessation in primary health care settings: a randomized trial

    PubMed Central

    2010-01-01

    Objectives Primary: To compare the effectiveness of intensive group and individual interventions for smoking cessation in a primary health care setting; secondary: to identify the variables associated with smoking cessation. Methods Three-pronged clinical trial with randomisation at the individual level. We performed the following: an intensive individual intervention (III), an intensive group intervention (IGI) and a minimal intervention (MI). Included in the study were smokers who were prepared to quit smoking. Excluded from the study were individuals aged less than 18 years or with severe mental conditions or terminal illnesses. The outcome measure was continued abstinence at 12 months confirmed through CO-oximetry (CO). The analysis was based on intention to treat. Results In total, 287 smokers were recruited: 81 in the III, 111 in the IGI, and 95 in the MI. Continued abstinence at 12 months confirmed through CO was 7.4% in the III, 5.4% in the IGI, and 1% in the MI. No significant differences were noted between III and MI on the one hand, and between IGI and MI on the other [RR 7.04 (0.9-7.2) and RR 5.1 (0.6-41.9), respectively]. No differences were noted between IGI and III [RR 0.7 (0.2-2.2)]. In multivariate analysis, only overall visit length showed a statistically significant association with smoking cessation. Conclusions The effectiveness of intensive smoking interventions in this study was lower than expected. No statistically significant differences were found between the results of individual and group interventions. Trial registration number ISRCTN32323770 PMID:20178617

  16. Effects of Prereactivation Propranolol on Cocaine Craving Elicited by Imagery Script/Cue Sets in Opioid-dependent Polydrug Users: A Randomized Study

    PubMed Central

    Jobes, Michelle L.; Aharonovich, Efrat; Epstein, David H.; Phillips, Karran A.; Reamer, David; Anderson, Micheline; Preston, Kenzie L.

    2015-01-01

    Objectives Relapse to drug misuse may follow exposure to drug cues that elicit craving. The learned associations, or “emotional memories,” that underlie responses to cues may be attenuated or erased by the beta-adrenergic antagonist propranolol during a “reconsolidation window” shortly after the memories are reactivated by cues. Methods We evaluated the effects of propranolol on cue-induced drug cravings in healthy opioid-dependent individuals who used cocaine while receiving methadone maintenance (n = 33). Participants were asked to recall specific cocaine-use and neutral events in an interview; these events were used to develop personalized auditory script/cue sets. Approximately one week later, propranolol (40 mg) or placebo (random assignment, double-blind) was administered orally before presentation of the script/cue sets; the presentation of the script/cue sets were tested 1 and 5 weeks after the propranolol/placebo session. Ongoing drug use was monitored via urine screens and self-report in twice-weekly visits. Results Cue reactivity, as assessed by craving scales and physiological responses, was unexpectedly greater in the propranolol group than in the placebo group. This counter-hypothesized group difference was present acutely during propranolol administration and appeared to persist (without reaching statistical significance) during the subsequent test sessions. Conclusions Our results do not support the use of propranolol for cue-induced cocaine craving in opioid-maintained patients. PMID:26501788

  17. The effectiveness of physical activity monitoring and distance counseling in an occupational setting – Results from a randomized controlled trial (CoAct)

    PubMed Central

    2012-01-01

    Background Lack of physical activity (PA) is a known risk factor for many health conditions. The workplace is a setting often used to promote activity and health. We investigated the effectiveness of an intervention on PA and productivity-related outcomes in an occupational setting. Methods We conducted a randomized controlled trial of 12 months duration with two 1:1 allocated parallel groups of insurance company employees. Eligibility criteria included permanent employment and absence of any condition that risked the participant’s health during PA. Subjects in the intervention group monitored their daily PA with an accelerometer, set goals, had access to an online service to help them track their activity levels, and received counseling via telephone or web messages for 12 months. The control group received the results of a fitness test and an information leaflet on PA at the beginning of the study. The intervention’s aim was to increase PA, improve work productivity, and decrease sickness absence. Primary outcomes were PA (measured as MET minutes per week), work productivity (quantity and quality of work; QQ index), and sickness absence (SA) days at 12 months. Participants were assigned to groups using block randomization with a computer-generated scheme. The study was not blinded. Results There were 544 randomized participants, of which 521 were included in the analysis (64% female, mean age 43 years). At 12 months, there was no significant difference in physical activity levels between the intervention group (n = 264) and the control group (n = 257). The adjusted mean difference was −206 MET min/week [95% Bayesian credible interval −540 to 128; negative values favor control group]. There was also no significant difference in the QQ index (−0.5 [−4.4 to 3.3]) or SA days (0.0 [−1.2 to 0.9]). Of secondary outcomes, body weight (0.5 kg [0.0 to 1.0]) and percentage of body fat (0.6% [0.2% to 1.1%]) were slightly higher in the

  18. Predictive and Prognostic Role of Tumor-Infiltrating Lymphocytes for Early Breast Cancer According to Disease Subtypes: Sensitivity Analysis of Randomized Trials in Adjuvant and Neoadjuvant Setting

    PubMed Central

    Carbognin, Luisa; Pilotto, Sara; Nortilli, Rolando; Brunelli, Matteo; Nottegar, Alessia; Sperduti, Isabella; Giannarelli, Diana; Tortora, Giampaolo

    2016-01-01

    Background. The role of tumor-infiltrating lymphocytes (TILs) in breast cancer (BC) is still an issue for clinical research. Toward this end, a sensitivity analysis of neoadjuvant and adjuvant randomized clinical trials was performed according to disease subtypes. Methods. Pathological complete responses (pCRs) after neoadjuvant treatment according to the presence or absence of lymphocyte-predominant BC (LPBC) were extracted and cumulated as odds ratios (ORs) by adopting a random-effects model by subtype. Overall survival hazard ratios as a function of 10% incremental values of stromal TILs (sTILs) in adjuvant trials were extracted. The interaction test was adopted to determine the differential effect according to the subtype. Results. Eight trials (5,514 patients) were identified. With regard to neoadjuvant setting (4 studies), a significant interaction (p < .0001) according to LPBC was found. The presence of LPBC was associated with a 29.5% increase in pCR rate compared with non-LPBC (p < .0001). The pCR rate was significantly higher in patients with LPBC in triple-negative BC (TNBC) and HER2-positive BC settings, with an absolute difference of 15.7% (95% confidence interval [CI], 4.9%–26.2%) and 33.3% (95% CI, 23.6%–42.7%), respectively. With respect to the adjuvant setting (4 studies), a significant interaction (p < .0001) according to sTILs was found. A survival benefit was more likely to be determined for HER2-positive BC (p = .025) and TNBC (p < .0001), with no statistically significant difference for estrogen receptor-positive/HER2-negative disease. Conclusion. Despite the retrospective nature of this analysis, the presence of TILs may represent a robust predictive and prognostic marker for BC, particularly for TNBC and HER2-positive disease. Implications for Practice: This sensitivity analysis of neoadjuvant and adjuvant randomized clinical trials in breast cancer explores the potential predictive and prognostic role of tumor-infiltrating lymphocytes

  19. [Spatial distribution of soil animals: a geostatistical approach].

    PubMed

    Gongal'skiĭ, K B; Zaĭtsev, A S; Savin, F A

    2009-01-01

    Spatial distribution is one of the main parameters of populations of soil animals. Spatial soil ecology having been developing during last decades bases animal distribution estimates on the geostatistic approach. A simple principle underlying the latter's methodology is that samples placed close to each other have more similarity than those distantly placed, it is usually called autocorrelation. The principles of basic statistics cannot be applied to autocorrelated data. Apiplying variograms, Mantel test, Moran index, and SADIE statistics enables to reveal the size of clusters of both soil parameters and soil animal aggregations. This direction of investigations quite popular in the western literature is just rarely employed by Russian soil ecologists. Statistically correct procedures allow developing field sampling methodology that is vital in applied studies of soil ecology, namely, in bioindication and ecotoxicology of soils, in the assessment of biological resources in terms of abundance and biomass of soil animals. This methodology has a decisive importance in the development of soil biogeography.

  20. Bayesian geostatistics in health cartography: the perspective of malaria

    PubMed Central

    Patil, Anand P.; Gething, Peter W.; Piel, Frédéric B.; Hay, Simon I.

    2011-01-01

    Maps of parasite prevalences and other aspects of infectious diseases that vary in space are widely used in parasitology. However, spatial parasitological datasets rarely, if ever, have sufficient coverage to allow exact determination of such maps. Bayesian geostatistics (BG) is a method for finding a large sample of maps that can explain a dataset, in which maps that do a better job of explaining the data are more likely to be represented. This sample represents the knowledge that the analyst has gained from the data about the unknown true map. BG provides a conceptually simple way to convert these samples to predictions of features of the unknown map, for example regional averages. These predictions account for each map in the sample, yielding an appropriate level of predictive precision. PMID:21420361

  1. Interactive Declustering of Spatial Environmental Data for Geostatistical Analyses

    SciTech Connect

    Christopher P. Oertel; John R. Giles; Stanley M. Miller

    2009-01-01

    Clustered sampling often results during environmental site investigations when localized areas are over-sampled due to specific concerns in those areas or due to monitoring programs focused on specific zones. An interactive, nearest-neighbor method for efficient spatial declustering has been developed as part of the ongoing monitoring and assessment of Cs-137 concentrations in soils at the Idaho National Laboratory (INL) site. Insitu field measurements of Cs-137 have been obtained with a field gamma-ray spectrometer using a sampling layout with points concentrated around several former nuclear processing facilities. The spatial declustering process allows for more useful and productive geostatistical studies focused on characterization of spatial dependence and subsequent spatial estimation to generate maps that depict Cs-137 concentrations across the site.

  2. Bayesian geostatistics in health cartography: the perspective of malaria.

    PubMed

    Patil, Anand P; Gething, Peter W; Piel, Frédéric B; Hay, Simon I

    2011-06-01

    Maps of parasite prevalences and other aspects of infectious diseases that vary in space are widely used in parasitology. However, spatial parasitological datasets rarely, if ever, have sufficient coverage to allow exact determination of such maps. Bayesian geostatistics (BG) is a method for finding a large sample of maps that can explain a dataset, in which maps that do a better job of explaining the data are more likely to be represented. This sample represents the knowledge that the analyst has gained from the data about the unknown true map. BG provides a conceptually simple way to convert these samples to predictions of features of the unknown map, for example regional averages. These predictions account for each map in the sample, yielding an appropriate level of predictive precision.

  3. Using rank-order geostatistics for spatial interpolation of highly skewed data in a heavy-metal contaminated site.

    PubMed

    Juang, K W; Lee, D Y; Ellsworth, T R

    2001-01-01

    The spatial distribution of a pollutant in contaminated soils is usually highly skewed. As a result, the sample variogram often differs considerably from its regional counterpart and the geostatistical interpolation is hindered. In this study, rank-order geostatistics with standardized rank transformation was used for the spatial interpolation of pollutants with a highly skewed distribution in contaminated soils when commonly used nonlinear methods, such as logarithmic and normal-scored transformations, are not suitable. A real data set of soil Cd concentrations with great variation and high skewness in a contaminated site of Taiwan was used for illustration. The spatial dependence of ranks transformed from Cd concentrations was identified and kriging estimation was readily performed in the standardized-rank space. The estimated standardized rank was back-transformed into the concentration space using the middle point model within a standardized-rank interval of the empirical distribution function (EDF). The spatial distribution of Cd concentrations was then obtained. The probability of Cd concentration being higher than a given cutoff value also can be estimated by using the estimated distribution of standardized ranks. The contour maps of Cd concentrations and the probabilities of Cd concentrations being higher than the cutoff value can be simultaneously used for delineation of hazardous areas of contaminated soils.

  4. Spatial evaluation of the risk of groundwater quality degradation. A comparison between disjunctive kriging and geostatistical simulation.

    PubMed

    Barca, E; Passarella, G

    2008-02-01

    In some previous papers a probabilistic methodology was introduced to estimate a spatial index of risk of groundwater quality degradation, defined as the conditional probability of exceeding assigned thresholds of concentration of a generic chemical sampled in the studied water system. A crucial stage of this methodology was the use of geostatistical techniques to provide an estimation of the above-mentioned probability in a number of selected points by crossing spatial and temporal information. In this work, spatial risk values were obtained using alternatively stochastic conditional simulation and disjunctive kriging. A comparison between the resulting two sets of spatial risks, based on global and local statistical tests, showed that they do not come from the same statistical population and, consequently, they cannot be viewed as equivalent in a statistical sense. At a first glance, geostatistical conditional simulation may appear to represent the spatial variability of the phenomenon more effectively, as the latter tends to be smoothed by DK. However, a close examination of real case study results suggests that disjunctive kriging is more effective than simulation in estimating the spatial risk of groundwater quality degradation. In the study case, the potentially 'harmful event' considered, threatening a natural 'vulnerable groundwater system,' is fertilizer and manure application.

  5. Medical Geography: a Promising Field of Application for Geostatistics.

    PubMed

    Goovaerts, P

    2009-01-01

    The analysis of health data and putative covariates, such as environmental, socio-economic, behavioral or demographic factors, is a promising application for geostatistics. It presents, however, several methodological challenges that arise from the fact that data are typically aggregated over irregular spatial supports and consist of a numerator and a denominator (i.e. population size). This paper presents an overview of recent developments in the field of health geostatistics, with an emphasis on three main steps in the analysis of areal health data: estimation of the underlying disease risk, detection of areas with significantly higher risk, and analysis of relationships with putative risk factors. The analysis is illustrated using age-adjusted cervix cancer mortality rates recorded over the 1970-1994 period for 118 counties of four states in the Western USA. Poisson kriging allows the filtering of noisy mortality rates computed from small population sizes, enhancing the correlation with two putative explanatory variables: percentage of habitants living below the federally defined poverty line, and percentage of Hispanic females. Area-to-point kriging formulation creates continuous maps of mortality risk, reducing the visual bias associated with the interpretation of choropleth maps. Stochastic simulation is used to generate realizations of cancer mortality maps, which allows one to quantify numerically how the uncertainty about the spatial distribution of health outcomes translates into uncertainty about the location of clusters of high values or the correlation with covariates. Last, geographically-weighted regression highlights the non-stationarity in the explanatory power of covariates: the higher mortality values along the coast are better explained by the two covariates than the lower risk recorded in Utah.

  6. Performance prediction using geostatistics and window reservoir simulation

    SciTech Connect

    Fontanilla, J.P.; Al-Khalawi, A.A.; Johnson, S.G.

    1995-11-01

    This paper is the first window model study in the northern area of a large carbonate reservoir in Saudi Arabia. It describes window reservoir simulation with geostatistics to model uneven water encroachment in the southwest producing area of the northern portion of the reservoir. In addition, this paper describes performance predictions that investigate the sweep efficiency of the current peripheral waterflood. A 50 x 50 x 549 (240 m. x 260 m. x 0.15 m. average grid block size) geological model was constructed with geostatistics software. Conditional simulation was used to obtain spatial distributions of porosity and volume of dolomite. Core data transforms were used to obtain horizontal and vertical permeability distributions. Simple averaging techniques were used to convert the 549-layer geological model to a 50 x 50 x 10 (240 m. x 260 m. x 8 m. average grid block size) window reservoir simulation model. Flux injectors and flux producers were assigned to the outermost grid blocks. Historical boundary flux rates were obtained from a coarsely-ridded full-field model. Pressure distribution, water cuts, GORs, and recent flowmeter data were history matched. Permeability correction factors and numerous parameter adjustments were required to obtain the final history match. The permeability correction factors were based on pressure transient permeability-thickness analyses. The prediction phase of the study evaluated the effects of infill drilling, the use of artificial lifts, workovers, horizontal wells, producing rate constraints, and tight zone development to formulate depletion strategies for the development of this area. The window model will also be used to investigate day-to-day reservoir management problems in this area.

  7. Revised Geostatistical Analysis of the Inventory of Carbon Tetrachloride in the Unconfined Aquifer in the 200 West Area of the Hanford Site

    SciTech Connect

    Murray, Christopher J.; Bott, Yi-Ju

    2008-12-30

    This report provides an updated estimate of the inventory of carbon tetrachloride (CTET) in the unconfined aquifer in the 200 West Area of the Hanford Site. The contaminant plumes of interest extend within the 200-ZP-1 and 200-UP-1 operable units. CH2M HILL Plateau Remediation Company (CHPRC) currently is preparing a plan identifying locations for groundwater extraction wells, injection wells, transfer stations, and one or more treatment facilities to address contaminants of concern identified in the 200-ZP-1 CERCLA Record of Decision. To accomplish this, a current understanding of the inventory of CTET is needed throughout the unconfined aquifer in the 200 West Area. Pacific Northwest National Laboratory (PNNL) previously developed an estimate of the CTET inventory in the area using a Monte Carlo approach based on geostatistical simulation of the three-dimensional (3D) distribution of CTET and chloroform in the aquifer. Fluor Hanford, Inc. (FH) (the previous site contractor) requested PNNL to update that inventory estimate using as input a set of geostatistical realizations of CTET and chloroform recently created for a related but separate project, referred to as the mapping project. The scope of work for the inventory revision complemented the scope of work for the mapping project, performed for FH by PNNL. This report briefly describes the spatial and univariate distribution of the CTET and chloroform data, along with the results of the geostatistical analysis and simulation performed for the mapping project.

  8. Focused breastfeeding counselling improves short- and long-term success in an early-discharge setting: A cluster-randomized study.

    PubMed

    Nilsson, Ingrid M S; Strandberg-Larsen, Katrine; Knight, Christopher H; Hansen, Anne Vinkel; Kronborg, Hanne

    2017-02-14

    Length of postnatal hospitalization has decreased and has been shown to be associated with infant nutritional problems and increase in readmissions. We aimed to evaluate if guidelines for breastfeeding counselling in an early discharge hospital setting had an effect on maternal breastfeeding self-efficacy, infant readmission and breastfeeding duration. A cluster randomized trial was conducted and assigned nine maternity settings in Denmark to intervention or usual care. Women were eligible if they expected a single infant, intended to breastfeed, were able to read Danish, and expected to be discharged within 50 hr postnatally. Between April 2013 and August 2014, 2,065 mothers were recruited at intervention and 1,476 at reference settings. Results show that the intervention did not affect maternal breastfeeding self-efficacy (primary outcome). However, less infants were readmitted 1 week postnatally in the intervention compared to the reference group (adjusted OR 0.55, 95% CI 0.37, -0.81), and 6 months following birth, more infants were exclusively breastfed in the intervention group (adjusted OR 1.36, 95% CI 1.02, -1.81). Moreover, mothers in the intervention compared to the reference group were breastfeeding more frequently (p < .001), and spend more hours skin to skin with their infants (p < .001). The infants were less often treated for jaundice (p = 0.003) and there was more paternal involvement (p = .037). In an early discharge hospital setting, a focused breastfeeding programme concentrating on increased skin to skin contact, frequent breastfeeding, good positioning of the mother infant dyad, and enhanced involvement of the father improved short-term and long-term breastfeeding success.

  9. [Geostatistics analyzing to cause of formation of circle distribution of plant communities in Horqin Sandy Land].

    PubMed

    He, Xingdong; Gao, Yubao; Zhao, Wenzhi; Cong, Zili

    2004-09-01

    Investigation results in the present study showed that plant communities took typical concentric circles distribution patterns along habitat gradient from top, slope to interdune on a few large fixed dunes in middle part of Korqin Sandy Land. In order to explain this phenomenon, analysis of water content and its spatial heterogeneity in sand layers on different locations of dunes was conducted. In these dunes, water contents in sand layers of the tops were lower than those of the slopes; both of them were lower than those of the interdunes. According to the results of geostatistics analysis, whether shifting dune or fixed dune, spatial heterogeneity of water contents in sand layers took on regular changes, such as ratios between nugget and sill and ranges reduced gradually, fractal dimension increased gradually, the regular changes of these parameters indicated that random spatial heterogeneity reduced gradually, and autocorrelation spatial heterogeneity increased gradually from the top, the slope to the interdune. The regular changes of water contents in sand layers and their spatial heterogeneity of different locations of the dunes, thus, might be an important cause resulted in the formation of the concentric circles patterns of the plant communities on these fixed dunes.

  10. Analysis of alluvial hydrostratigraphy using indicator geostatistics, with examples from Santa Clara Valley, California

    SciTech Connect

    1995-03-01

    Current trends in hydrogeology seek to enlist sedimentary concepts in the interpretation of permeability structures. However, existing conceptual models of alluvial deposition tend to inadequately account for the heterogeneity caused by complex sedimentological and external factors. This dissertation presents three analyses of alluvial hydrostratigraphy using indicator geostatistics. This approach empirically acknowledges both the random and structured qualities of alluvial structures at scales relevant to site investigations. The first analysis introduces the indicator approach, whereby binary values are assigned to borehole-log intervals on the basis of inferred relative permeability; it presents a case study of indicator variography at a well-documented ground-water contamination site, and uses indicator kriging to interpolate an aquifer-aquitard sequence in three dimensions. The second analysis develops an alluvial-architecture context for interpreting semivariograms, and performs comparative variography for a suite of alluvial sites in Santa Clara Valley, California. The third analysis investigates the use of a water well perforation indicator for assessing large-scale hydrostratigraphic structures within relatively deep production zones.

  11. Geostatistical analysis of allele presence patterns among American black bears in eastern North Carolina

    USGS Publications Warehouse

    Thompson, L.M.; Van Manen, F.T.; King, T.L.

    2005-01-01

    Highways are one of the leading causes of wildlife habitat fragmentation and may particularly affect wide-ranging species, such as American black bears (Ursus americanus). We initiated a research project in 2000 to determine potential effects of a 4-lane highway on black bear ecology in Washington County, North Carolina. The research design included a treatment area (highway construction) and a control area and a pre- and post-construction phase. We used data from the pre-construction phase to determine whether we could detect scale dependency or directionality among allele occurrence patterns using geostatistics. Detection of such patterns could provide a powerful tool to measure the effects of landscape fragmentation on gene flow. We sampled DNA from roots of black bear hair at 70 hair-sampling sites on each study area for 7 weeks during fall of 2000. We used microsatellite analysis based on 10 loci to determine unique multi-locus genotypes. We examined all alleles sampled at ???25 sites on each study area and mapped their presence or absence at each hair-sample site. We calculated semivariograms, which measure the strength of statistical correlation as a function of distance, and adjusted them for anisotropy to determine the maximum direction of spatial continuity. We then calculated the mean direction of spatial continuity for all examined alleles. The mean direction of allele frequency variation was 118.3?? (SE = 8.5) on the treatment area and 172.3?? (SE = 6.0) on the control area. Rayleigh's tests showed that these directions differed from random distributions (P = 0.028 and P < 0.001, respectively), indicating consistent directional patterns for the alleles we examined in each area. Despite the small spatial scale of our study (approximately 11,000 ha for each study area), we observed distinct and consistent patterns of allele occurrence, suggesting different directions of gene flow between the study areas. These directions seemed to coincide with the

  12. Radon risk mapping in southern Belgium: an application of geostatistical and GIS techniques.

    PubMed

    Zh, H C; Charlet, J M; Poffijn, A

    2001-05-14

    A data set of long-term radon measurements in approximately 2200 houses in southern Belgium has been collected in an on-going national radon survey. The spatial variation of indoor Rn concentrations is modelled by variograms. A radon distribution map is produced using the log-normal kriging technique. A GIS is used to digitise, process and integrate a variety of data, including geological maps, Rn concentrations associated with house locations and an administrative map, etc. It also allows evaluation of the relationships between various spatial data sets with the goal of producing radon risk maps. Based on geostatistical mapping and spatial analysis, we define three categories of risk areas: high risk, medium risk and low risk area. The correlation between radon concentrations and geological features is proved in this study. High and medium Rn risk zones are dominantly situated in bedrock from the Cambrian to Lower Devonian, although a few medium risk zones are within the Jurassic. It is evident that high-risk zones are related to a strongly folded and fractured context.

  13. Combining geostatistics with Moran's I analysis for mapping soil heavy metals in Beijing, China.

    PubMed

    Huo, Xiao-Ni; Li, Hong; Sun, Dan-Feng; Zhou, Lian-Di; Li, Bao-Guo

    2012-03-01

    Production of high quality interpolation maps of heavy metals is important for risk assessment of environmental pollution. In this paper, the spatial correlation characteristics information obtained from Moran's I analysis was used to supplement the traditional geostatistics. According to Moran's I analysis, four characteristics distances were obtained and used as the active lag distance to calculate the semivariance. Validation of the optimality of semivariance demonstrated that using the two distances where the Moran's I and the standardized Moran's I, Z(I) reached a maximum as the active lag distance can improve the fitting accuracy of semivariance. Then, spatial interpolation was produced based on the two distances and their nested model. The comparative analysis of estimation accuracy and the measured and predicted pollution status showed that the method combining geostatistics with Moran's I analysis was better than traditional geostatistics. Thus, Moran's I analysis is a useful complement for geostatistics to improve the spatial interpolation accuracy of heavy metals.

  14. Limited efficacy of a nonrestricted intervention on antimicrobial prescription of commonly used antibiotics in the hospital setting: results of a randomized controlled trial.

    PubMed

    Masiá, M; Matoses, C; Padilla, S; Murcia, A; Sánchez, V; Romero, I; Navarro, A; Hernández, I; Gutiérrez, F

    2008-07-01

    Most interventions aimed at diminishing the use of antimicrobials in hospitals have focussed on newly introduced antibiotics and very few have been randomly controlled. We evaluated the impact on antibiotic consumption of an intervention without restrictions in antibiotic use, focussed on commonly used antibiotics with a controlled randomized trial. All new prescriptions of levofloxacin, carbapenems, or vancomycin in hospitalized patients were randomized to an intervention or a control group. Intervention consisted of an antibiotic regimen counselling targeted to match local antibiotic guidelines, performed using only patients' charts. Clinical charts of patients assigned to the control group were reviewed daily by a pharmacist. The primary endpoint was a reduction in consumption of the targeted antibiotics. Two hundred seventy-eight prescriptions corresponding to 253 patients were included: 146 were assigned to the intervention and 132 to the control group. Total consumption of the targeted antibiotics (median [IQR]) was slightly lower in the intervention (8 [4-12] defined daily doses [DDDs] per patient) than in the control group (10 [6-16] DDDs per patient; p = 0.04). No differences in number of DDDs were observed when antibiotics of substitution were included (11.05 [6-18.2] vs 10 [6-16.5] in the intervention and control groups, respectively, p = 0.13). The total number of days on treatment with the targeted antibiotics was lower in the intervention (4 [3-7] days per patient) than in the control group (6 [4-10] days per patient; p = 0.002). Differences in number of days on treatment only reached statistical significance in the prescriptions of carbapenems. There were no differences between intervention and control groups in terms of number of deaths, hospital readmissions, length of hospital stay, or antibiotic costs. In this trial, an intervention without restrictions focussed on antimicrobial prescriptions of commonly used antibiotics in the hospital setting

  15. Efficient Geostatistical Inversion under Transient Flow Conditions in Heterogeneous Porous Media

    NASA Astrophysics Data System (ADS)

    Klein, Ole; Cirpka, Olaf A.; Bastian, Peter; Ippisch, Olaf

    2014-05-01

    a reasonable range. Transient inversion, however, requires time series of measurements and therefore typically leads to a large number of observations, and under these circumstances the existing methods become unfeasible. We present an extension of the existing inversion methods to instationary flow regimes. Our approach uses a Conjugate Gradients scheme preconditioned with the prior covariance matrix QY Y to avoid both multiplications with QY Y -1 and the explicit assembly of Hz. Instead, one combined adjoint model run is used for all observations at once. As the computing time of our approach is largely independent of the number of measurements used for inversion, the presented method can be applied to large data sets. This facilitates the treatment of applications with variable boundary conditions (nearby rivers, precipitation). We integrate the geostatistical inversion method into the software framework DUNE, enabling the use of high-performance-computing techniques and full parallelization. Feasibility of our approach is demonstrated through the joint inversion of several synthetic data sets in two and three dimensions, e.g. estimation of hydraulic conductivity using hydraulic head values and tracer concentrations, and scalability of the new method is analyzed. A comparison of the new method with existing geostatistical inversion approaches highlights its advantages and drawbacks and demonstrates scenarios in which our scheme can be beneficial.

  16. Geostatistical noise filtering of geophysical images : application to unexploded ordnance (UXO) sites.

    SciTech Connect

    Saito, Hirotaka; McKenna, Sean Andrew; Coburn, Timothy C.

    2004-07-01

    Geostatistical and non-geostatistical noise filtering methodologies, factorial kriging and a low-pass filter, and a region growing method are applied to analytic signal magnetometer images at two UXO contaminated sites to delineate UXO target areas. Overall delineation performance is improved by removing background noise. Factorial kriging slightly outperforms the low-pass filter but there is no distinct difference between them in terms of finding anomalies of interest.

  17. Development of a Core Set of Outcomes for Randomized Controlled Trials with Multiple Outcomes – Example of Pulp Treatments of Primary Teeth for Extensive Decay in Children

    PubMed Central

    Smaïl-Faugeron, Violaine; Fron Chabouis, Hélène; Durieux, Pierre; Attal, Jean-Pierre; Muller-Bolla, Michèle; Courson, Frédéric

    2013-01-01

    Objectives Evidence-based comparisons of interventions can be challenging because of the diversity of outcomes in randomized controlled trials (RCTs). We aimed to describe outcomes in RCTs assessing pulp treatments for primary teeth and to develop a core set of component outcomes to be part of composite outcome defining the failure of a pulp treatment. Methods We systematically reviewed articles of RCTs comparing pulp treatments for primary molars published up to February 2012. We abstracted all outcomes assessed in each trial, then used a small-group consensus process to group similar outcomes, which were reduced to a composite outcome of failure of a pulp treatment by a 3-round Delphi process involving expert authors and dentists. Results We included 47 reports of RCTs in the review, for 83 reported outcomes (median 11 outcomes per RCT). These outcomes were grouped into 24 overarching outcome categories. We contacted 210 experts for the Delphi process and 25% to 30% participated. The process identified the following 5 component outcomes as part of a composite outcome of failure of a pulp treatment: soft-tissue pathology, pain, pathologic mobility, pathologic radiolucency and pathologic root resorption. Conclusions RCTs of pulp treatments for primary teeth investigate diverse outcomes. Our consensus process, involving clinicians but no patient, allowed for compiling a core set of component outcomes to define the composite outcome failure of a pulp treatment for primary teeth. PMID:23300955

  18. Feasibility intervention trial of two types of improved cookstoves in three resource-limited settings: study protocol for a randomized controlled trial

    PubMed Central

    2013-01-01

    Background Exposure to biomass fuel smoke is one of the leading risk factors for disease burden worldwide. International campaigns are currently promoting the widespread adoption of improved cookstoves in resource-limited settings, yet little is known about the cultural and social barriers to successful improved cookstove adoption and how these barriers affect environmental exposures and health outcomes. Design We plan to conduct a one-year crossover, feasibility intervention trial in three resource-limited settings (Kenya, Nepal and Peru). We will enroll 40 to 46 female primary cooks aged 20 to 49 years in each site (total 120 to 138). Methods At baseline, we will collect information on sociodemographic characteristics and cooking practices, and measure respiratory health and blood pressure for all participating women. An initial observational period of four months while households use their traditional, open-fire design cookstoves will take place prior to randomization. All participants will then be randomized to receive one of two types of improved, ventilated cookstoves with a chimney: a commercially-constructed cookstove (Envirofit G3300/G3355) or a locally-constructed cookstove. After four months of observation, participants will crossover and receive the other improved cookstove design and be followed for another four months. During each of the three four-month study periods, we will collect monthly information on self-reported respiratory symptoms, cooking practices, compliance with cookstove use (intervention periods only), and measure peak expiratory flow, forced expiratory volume at 1 second, exhaled carbon monoxide and blood pressure. We will also measure pulmonary function testing in the women participants and 24-hour kitchen particulate matter and carbon monoxide levels at least once per period. Discussion Findings from this study will help us better understand the behavioral, biological, and environmental changes that occur with a cookstove

  19. Comparing different approaches - data mining, geostatistic, and deterministic pedology - to assess the frequency of WRB Reference Soil Groups in the Italian soil regions

    NASA Astrophysics Data System (ADS)

    Lorenzetti, Romina; Barbetti, Roberto; L'Abate, Giovanni; Fantappiè, Maria; Costantini, Edoardo A. C.

    2013-04-01

    Estimating frequency of soil classes in map unit is always affected by some degree of uncertainty, especially at small scales, with a larger generalization. The aim of this study was to compare different possible approaches - data mining, geostatistic, deterministic pedology - to assess the frequency of WRB Reference Soil Groups (RSG) in the major Italian soil regions. In the soil map of Italy (Costantini et al., 2012), a list of the first five RSG was reported in each major 10 soil regions. The soil map was produced using the national soil geodatabase, which stored 22,015 analyzed and classified pedons, 1,413 soil typological unit (STU) and a set of auxiliary variables (lithology, land-use, DEM). Other variables were added, to better consider the influence of soil forming factors (slope, soil aridity index, carbon stock, soil inorganic carbon content, clay, sand, geography of soil regions and soil systems) and a grid at 1 km mesh was set up. The traditional deterministic pedology assessed the STU frequency according to the expert judgment presence in every elementary landscape which formed the mapping unit. Different data mining techniques were firstly compared in their ability to predict RSG through auxiliary variables (neural networks, random forests, boosted tree, supported vector machine (SVM)). We selected SVM according to the result of a testing set. A SVM model is a representation of the examples as points in space, mapped so that examples of separate categories are divided by a clear gap that is as wide as possible. The geostatistic algorithm we used was an indicator collocated cokriging. The class values of the auxiliary variables, available at all the points of the grid, were transformed in indicator variables (values 0, 1). A principal component analysis allowed us to select the variables that were able to explain the largest variability, and to correlate each RSG with the first principal component, which explained the 51% of the total variability. The

  20. Building on crossvalidation for increasing the quality of geostatistical modeling

    USGS Publications Warehouse

    Olea, R.A.

    2012-01-01

    The random function is a mathematical model commonly used in the assessment of uncertainty associated with a spatially correlated attribute that has been partially sampled. There are multiple algorithms for modeling such random functions, all sharing the requirement of specifying various parameters that have critical influence on the results. The importance of finding ways to compare the methods and setting parameters to obtain results that better model uncertainty has increased as these algorithms have grown in number and complexity. Crossvalidation has been used in spatial statistics, mostly in kriging, for the analysis of mean square errors. An appeal of this approach is its ability to work with the same empirical sample available for running the algorithms. This paper goes beyond checking estimates by formulating a function sensitive to conditional bias. Under ideal conditions, such function turns into a straight line, which can be used as a reference for preparing measures of performance. Applied to kriging, deviations from the ideal line provide sensitivity to the semivariogram lacking in crossvalidation of kriging errors and are more sensitive to conditional bias than analyses of errors. In terms of stochastic simulation, in addition to finding better parameters, the deviations allow comparison of the realizations resulting from the applications of different methods. Examples show improvements of about 30% in the deviations and approximately 10% in the square root of mean square errors between reasonable starting modelling and the solutions according to the new criteria. ?? 2011 US Government.

  1. Porous media flux sensitivity to pore-scale geostatistics: A bottom-up approach

    NASA Astrophysics Data System (ADS)

    Di Palma, P. R.; Guyennon, N.; Heße, F.; Romano, E.

    2017-04-01

    Macroscopic properties of flow through porous media can be directly computed by solving the Navier-Stokes equations at the scales related to the actual flow processes, while considering the porous structures in an explicit way. The aim of this paper is to investigate the effects of the pore-scale spatial distribution on seepage velocity through numerical simulations of 3D fluid flow performed by the lattice Boltzmann method. To this end, we generate multiple random Gaussian fields whose spatial correlation follows an assigned semi-variogram function. The Exponential and Gaussian semi-variograms are chosen as extreme-cases of correlation for short distances and statistical properties of the resulting porous media (indicator field) are described using the Matèrn covariance model, with characteristic lengths of spatial autocorrelation (pore size) varying from 2% to 13% of the linear domain. To consider the sensitivity of the modeling results to the geostatistical representativeness of the domain as well as to the adopted resolution, porous media have been generated repetitively with re-initialized random seeds and three different resolutions have been tested for each resulting realization. The main difference among results is observed between the two adopted semi-variograms, indicating that the roughness (short distances autocorrelation) is the property mainly affecting the flux. However, computed seepage velocities show additionally a wide variability (about three orders of magnitude) for each semi-variogram model in relation to the assigned correlation length, corresponding to pore sizes. The spatial resolution affects more the results for short correlation lengths (i.e., small pore sizes), resulting in an increasing underestimation of the seepage velocity with the decreasing correlation length. On the other hand, results show an increasing uncertainty as the correlation length approaches the domain size.

  2. A randomized trial of computer-based reminders and audit and feedback to improve HIV screening in a primary care setting.

    PubMed

    Sundaram, V; Lazzeroni, L C; Douglass, L R; Sanders, G D; Tempio, P; Owens, D K

    2009-08-01

    Despite recommendations for voluntary HIV screening, few medical centres have implemented screening programmes. The objective of the study was to determine whether an intervention with computer-based reminders and feedback would increase screening for HIV in a Department of Veterans Affairs (VA) health-care system. The design of the study was a randomized controlled trial at five primary care clinics at the VA Palo Alto Health Care System. All primary care providers were eligible to participate in the study. The study intervention was computer-based reminders to either assess HIV risk behaviours or to offer HIV testing; feedback on adherence to reminders was provided. The main outcome measure was the difference in HIV testing rates between intervention and control group providers. The control group providers tested 1.0% (n = 67) and 1.4% (n = 106) of patients in the preintervention and intervention period, respectively; intervention providers tested 1.8% (n = 98) and 1.9% (n = 114), respectively (P = 0.75). In our random sample of 753 untested patients, 204 (27%) had documented risk behaviours. Providers were more likely to adhere to reminders to test rather than with reminders to perform risk assessment (11% versus 5%, P < 0.01). Sixty-one percent of providers felt that lack of time prevented risk assessment. In conclusion, in primary care clinics in our setting, HIV testing rates were low. Providers were unaware of the high rates of risky behaviour in their patient population and perceived important barriers to testing. Low-intensity clinical reminders and feedback did not increase rates of screening.

  3. Geostatistical prediction of flow-duration curves in an index-flow framework

    NASA Astrophysics Data System (ADS)

    Pugliese, Alessio; Castellarin, Attilio; Brath, Armando

    2014-05-01

    An empirical period-of-record Flow-Duration Curve (FDC) describes the percentage of time (duration) in which a given streamflow was equaled or exceeded over an historical period of time. FDCs have always attracted a great deal of interest in engineering applications because of their ability to provide a simple yet comprehensive graphical view of the overall historical variability of streamflows in a river basin, from floods to low-flows. Nevertheless, in many practical applications one has to construct FDC in basins that are ungauged or where very few observations are available. We present in this study an application strategy of Topological kriging (or Top-kriging), which makes the geostatistical procedure capable of predicting flow-duration curves (FDCs) in ungauged catchments. Previous applications of Top-kriging mainly focused on the prediction of point streamflow indices (e.g. flood quantiles, low-flow indices, etc.). In this study Top-kriging is used to predict FDCs in ungauged sites as a weighted average of standardised empirical FDCs through the traditional linear-weighting scheme of kriging methods. Our study focuses on the prediction of FDCs for 18 unregulated catchments located in Central Italy, for which daily streamflow series with length from 5 to 40 years are available, together with information on climate referring to the same time-span of each daily streamflow sequence. Empirical FDCs are standardised by a reference index-flow value (i.e. mean annual flow, or mean annual precipitation times the catchment drainage area) and the overall deviation of the curves from this reference value is then used for expressing the hydrological similarity between catchments and for deriving the geostatistical weights. We performed an extensive leave-one-out cross-validation to quantify the accuracy of the proposed technique, and to compare it to traditional regionalisation models that were recently developed for the same study region. The cross-validation points

  4. Optimized Field Sampling and Monitoring of Airborne Hazardous Transport Plumes; A Geostatistical Simulation Approach

    SciTech Connect

    Chen, DI-WEN

    2001-11-21

    Airborne hazardous plumes inadvertently released during nuclear/chemical/biological incidents are mostly of unknown composition and concentration until measurements are taken of post-accident ground concentrations from plume-ground deposition of constituents. Unfortunately, measurements often are days post-incident and rely on hazardous manned air-vehicle measurements. Before this happens, computational plume migration models are the only source of information on the plume characteristics, constituents, concentrations, directions of travel, ground deposition, etc. A mobile ''lighter than air'' (LTA) system is being developed at Oak Ridge National Laboratory that will be part of the first response in emergency conditions. These interactive and remote unmanned air vehicles will carry light-weight detectors and weather instrumentation to measure the conditions during and after plume release. This requires a cooperative computationally organized, GPS-controlled set of LTA's that self-coordinate around the objectives in an emergency situation in restricted time frames. A critical step before an optimum and cost-effective field sampling and monitoring program proceeds is the collection of data that provides statistically significant information, collected in a reliable and expeditious manner. Efficient aerial arrangements of the detectors taking the data (for active airborne release conditions) are necessary for plume identification, computational 3-dimensional reconstruction, and source distribution functions. This report describes the application of stochastic or geostatistical simulations to delineate the plume for guiding subsequent sampling and monitoring designs. A case study is presented of building digital plume images, based on existing ''hard'' experimental data and ''soft'' preliminary transport modeling results of Prairie Grass Trials Site. Markov Bayes Simulation, a coupled Bayesian/geostatistical methodology, quantitatively combines soft information

  5. Estimating transmissivity in the Edwards Aquifer using upscaling, geostatistics, and Bayesian updating

    NASA Astrophysics Data System (ADS)

    Painter, S. L.; Jiang, Y.; Woodbury, A. D.

    2002-12-01

    The Edwards Aquifer, a highly heterogeneous karst aquifer located in south central Texas, is the sole source of drinking water for more than one million people. Hydraulic conductivity (K) measurements in the Edwards Aquifer are sparse, highly variable (log-K variance of 6.4), and are mostly from single-well drawdown tests that are appropriate for the spatial scale of a few meters. To support ongoing efforts to develop a groundwater management (MODFLOW) model of the San Antonio segment of the Edwards Aquifer, a multistep procedure was developed to assign hydraulic parameters to the 402 m x 402 m computational cells intended for the management model. The approach used a combination of nonparametric geostatistical analysis, stochastic simulation, numerical upscaling, and automatic model calibration based on Bayesian updating [1,2]. Indicator correlograms reveal a nested spatial structure in the well-test K of the confined zone, with practical correlation ranges of 3,600 and 15,000 meters and a large nugget effect. The fitted geostatistical model was used in unconditional stochastic simulations by the sequential indicator simulation method. The resulting realizations of K, defined at the scale of the well tests, were then numerically upscaled to the block scale. A new geostatistical model was fitted to the upscaled values. The upscaled model was then used to cokrige the block-scale K based on the well-test K. The resulting K map was then converted to transmissivity (T) using deterministically mapped aquifer thickness. When tested in a forward groundwater model, the upscaled T reproduced hydraulic heads better than a simple kriging of the well-test values (mean error of -3.9 meter and mean-absolute-error of 12 meters, as compared with -13 and 17 meters for the simple kriging). As the final step in the study, the upscaled T map was used as the prior distribution in an inverse procedure based on Bayesian updating [1,2]. When input to the forward groundwater model, the

  6. High Intensity Interval Training in a Real World Setting: A Randomized Controlled Feasibility Study in Overweight Inactive Adults, Measuring Change in Maximal Oxygen Uptake

    PubMed Central

    Lunt, Helen; Draper, Nick; Marshall, Helen C.; Logan, Florence J.; Hamlin, Michael J.; Shearman, Jeremy P.; Cotter, James D.; Kimber, Nicholas E.; Blackwell, Gavin; Frampton, Christopher M. A.

    2014-01-01

    Background In research clinic settings, overweight adults undertaking HIIT (high intensity interval training) improve their fitness as effectively as those undertaking conventional walking programs but can do so within a shorter time spent exercising. We undertook a randomized controlled feasibility (pilot) study aimed at extending HIIT into a real world setting by recruiting overweight/obese, inactive adults into a group based activity program, held in a community park. Methods Participants were allocated into one of three groups. The two interventions, aerobic interval training and maximal volitional interval training, were compared with an active control group undertaking walking based exercise. Supervised group sessions (36 per intervention) were held outdoors. Cardiorespiratory fitness was measured using VO2max (maximal oxygen uptake, results expressed in ml/min/kg), before and after the 12 week interventions. Results On ITT (intention to treat) analyses, baseline (N = 49) and exit (N = 39) O2 was 25.3±4.5 and 25.3±3.9, respectively. Participant allocation and baseline/exit VO2max by group was as follows: Aerobic interval training N =  16, 24.2±4.8/25.6±4.8; maximal volitional interval training N = 16, 25.0±2.8/25.2±3.4; walking N = 17, 26.5±5.3/25.2±3.6. The post intervention change in VO2max was +1.01 in the aerobic interval training, −0.06 in the maximal volitional interval training and −1.03 in the walking subgroups. The aerobic interval training subgroup increased VO2max compared to walking (p = 0.03). The actual (observed, rather than prescribed) time spent exercising (minutes per week, ITT analysis) was 74 for aerobic interval training, 45 for maximal volitional interval training and 116 for walking (p =  0.001). On descriptive analysis, the walking subgroup had the fewest adverse events. Conclusions In contrast to earlier studies, the improvement in cardiorespiratory fitness in a cohort of overweight

  7. Obtaining parsimonious hydraulic conductivity fields using head and transport observations: A bayesian geostatistical parameter estimation approach

    USGS Publications Warehouse

    Fienen, M.; Hunt, R.; Krabbenhoft, D.; Clemo, T.

    2009-01-01

    Flow path delineation is a valuable tool for interpreting the subsurface hydrogeochemical environment. Different types of data, such as groundwater flow and transport, inform different aspects of hydrogeologie parameter values (hydraulic conductivity in this case) which, in turn, determine flow paths. This work combines flow and transport information to estimate a unified set of hydrogeologic parameters using the Bayesian geostatistical inverse approach. Parameter flexibility is allowed by using a highly parameterized approach with the level of complexity informed by the data. Despite the effort to adhere to the ideal of minimal a priori structure imposed on the problem, extreme contrasts in parameters can result in the need to censor correlation across hydrostratigraphic bounding surfaces. These partitions segregate parameters into faci??s associations. With an iterative approach in which partitions are based on inspection of initial estimates, flow path interpretation is progressively refined through the inclusion of more types of data. Head observations, stable oxygen isotopes (18O/16O) ratios), and tritium are all used to progressively refine flow path delineation on an isthmus between two lakes in the Trout Lake watershed, northern Wisconsin, United States. Despite allowing significant parameter freedom by estimating many distributed parameter values, a smooth field is obtained. Copyright 2009 by the American Geophysical Union.

  8. Geostatistical investigations for suitable mapping of the water table: the Bordeaux case (France)

    NASA Astrophysics Data System (ADS)

    Guekie simo, Aubin Thibaut; Marache, Antoine; Lastennet, Roland; Breysse, Denys

    2016-02-01

    Methodologies have been developed to establish realistic water-table maps using geostatistical methods: ordinary kriging (OK), cokriging (CoK), collocated cokriging (CoCoK), and kriging with external drift (KED). In fact, in a hilly terrain, when piezometric data are sparsely distributed over large areas, the water-table maps obtained by these methods provide exact water levels at monitoring wells but fail to represent the groundwater flow system, manifested through an interpolated water table above the topography. A methodology is developed in order to rebuild water-table maps for urban areas at the city scale. The interpolation methodology is presented and applied in a case study where water levels are monitored at a set of 47 points for a part urban domain covering 25.6 km2 close to Bordeaux city, France. To select the best method, a geographic information system was used to visualize surfaces reconstructed with each method. A cross-validation was carried out to evaluate the predictive performances of each kriging method. KED proves to be the most accurate and yields a better description of the local fluctuations induced by the topography (natural occurrence of ridges and valleys).

  9. Use of a Transition Probability/Markov Approach to Improve Geostatistical of Facies Architecture

    SciTech Connect

    Carle, S.F.

    2000-11-01

    Facies may account for the largest permeability contrasts within the reservoir model at the scale relevant to production. Conditional simulation of the spatial distribution of facies is one of the most important components of building a reservoir model. Geostatistical techniques are widely used to produce realistic and geologically plausible realizations of facies architecture. However, there are two stumbling blocks to the traditional indicator variogram-based approaches: (1) intensive data sets are needed to develop models of spatial variability by empirical curve-fitting to sample indicator (cross-) variograms and to implement ''post-processing'' simulation algorithms; and (2) the prevalent ''sequential indicator simulation'' (SIS) methods do not accurately produce patterns of spatial variability for systems with three or more facies (Seifert and Jensen, 1999). This paper demonstrates an alternative transition probability/Markov approach that emphasizes: (1) Conceptual understanding of the parameters of the spatial variability model, so that geologic insight can support and enhance model development when data are sparse. (2) Mathematical rigor, so that the ''coregionalization'' model (including the spatial cross-correlations) obeys probability law. (3) Consideration of spatial cross-correlation, so that juxtapositional tendencies--how frequently one facies tends to occur adjacent to another facies--are honored.

  10. Delineation of estuarine management areas using multivariate geostatistics: the case of Sado Estuary.

    PubMed

    Caeiro, Sandra; Goovaerts, Pierre; Painho, Marco; Costa, M Helena

    2003-09-15

    The Sado Estuary is a coastal zone located in the south of Portugal where conflicts between conservation and development exist because of its location near industrialized urban zones and its designation as a natural reserve. The aim of this paper is to evaluate a set of multivariate geostatistical approaches to delineate spatially contiguous regions of sediment structure for Sado Estuary. These areas will be the supporting infrastructure of an environmental management system for this estuary. The boundaries of each homogeneous area were derived from three sediment characterization attributes through three different approaches: (1) cluster analysis of dissimilarity matrix function of geographical separation followed by indicator kriging of the cluster data, (2) discriminant analysis of kriged values of the three sediment attributes, and (3) a combination of methods 1 and 2. Final maximum likelihood classification was integrated into a geographical information system. All methods generated fairly spatially contiguous management areas that reproduce well the environment of the estuary. Map comparison techniques based on kappa statistics showed thatthe resultant three maps are similar, supporting the choice of any of the methods as appropriate for management of the Sado Estuary. However, the results of method 1 seem to be in better agreement with estuary behavior, assessment of contamination sources, and previous work conducted at this site.

  11. Geostatistical analysis of tritium, groundwater age and other noble gas derived parameters in California.

    PubMed

    Visser, A; Moran, J E; Hillegonds, Darren; Singleton, M J; Kulongoski, Justin T; Belitz, Kenneth; Esser, B K

    2016-03-15

    Key characteristics of California groundwater systems related to aquifer vulnerability, sustainability, recharge locations and mechanisms, and anthropogenic impact on recharge are revealed in a spatial geostatistical analysis of a unique data set of tritium, noble gases and other isotopic analyses unprecedented in size at nearly 4000 samples. The correlation length of key groundwater residence time parameters varies between tens of kilometers ((3)H; age) to the order of a hundred kilometers ((4)Heter; (14)C; (3)Hetrit). The correlation length of parameters related to climate, topography and atmospheric processes is on the order of several hundred kilometers (recharge temperature; δ(18)O). Young groundwater ages that highlight regional recharge areas are located in the eastern San Joaquin Valley, in the southern Santa Clara Valley Basin, in the upper LA basin and along unlined canals carrying Colorado River water, showing that much of the recent recharge in central and southern California is dominated by river recharge and managed aquifer recharge. Modern groundwater is found in wells with the top open intervals below 60 m depth in the southeastern San Joaquin Valley, Santa Clara Valley and Los Angeles basin, as the result of intensive pumping and/or managed aquifer recharge operations.

  12. Characterizing the spatial structure of endangered species habitat using geostatistical analysis of IKONOS imagery

    USGS Publications Warehouse

    Wallace, C.S.A.; Marsh, S.E.

    2005-01-01

    Our study used geostatistics to extract measures that characterize the spatial structure of vegetated landscapes from satellite imagery for mapping endangered Sonoran pronghorn habitat. Fine spatial resolution IKONOS data provided information at the scale of individual trees or shrubs that permitted analysis of vegetation structure and pattern. We derived images of landscape structure by calculating local estimates of the nugget, sill, and range variogram parameters within 25 ?? 25-m image windows. These variogram parameters, which describe the spatial autocorrelation of the 1-m image pixels, are shown in previous studies to discriminate between different species-specific vegetation associations. We constructed two independent models of pronghorn landscape preference by coupling the derived measures with Sonoran pronghorn sighting data: a distribution-based model and a cluster-based model. The distribution-based model used the descriptive statistics for variogram measures at pronghorn sightings, whereas the cluster-based model used the distribution of pronghorn sightings within clusters of an unsupervised classification of derived images. Both models define similar landscapes, and validation results confirm they effectively predict the locations of an independent set of pronghorn sightings. Such information, although not a substitute for field-based knowledge of the landscape and associated ecological processes, can provide valuable reconnaissance information to guide natural resource management efforts. ?? 2005 Taylor & Francis Group Ltd.

  13. Mapping soil gas radon concentration: a comparative study of geostatistical methods.

    PubMed

    Buttafuoco, Gabriele; Tallarico, Adalisa; Falcone, Giovanni

    2007-08-01

    Understanding soil gas radon spatial variations can allow the constructor of a new house to prevent radon gas flowing from the ground. Indoor radon concentration distribution depends on many parameters and it is difficult to use its spatial variation to assess radon potential. Many scientists use to measure outdoor soil gas radon concentrations to assess the radon potential. Geostatistical methods provide us a valuable tool to study spatial structure of radon concentration and mapping. To explore the structure of soil gas radon concentration within an area in south Italy and choice a kriging algorithm, we compared the prediction performances of four different kriging algorithms: ordinary kriging, lognormal kriging, ordinary multi-Gaussian kriging, and ordinary indicator cokriging. Their results were compared using an independent validation data set. The comparison of predictions was based on three measures of accuracy: (1) the mean absolute error, (2) the mean-squared error of prediction; (3) the mean relative error, and a measure of effectiveness: the goodness-of-prediction estimate. The results obtained in this case study showed that the multi-Gaussian kriging was the most accurate approach among those considered. Comparing radon anomalies with lithology and fault locations, no evidence of a strict correlation between type of outcropping terrain and radon anomalies was found, except in the western sector where there were granitic and gneissic terrain. Moreover, there was a clear correlation between radon anomalies and fault systems.

  14. Integrating Ensemble Data Assimilation and Indicator Geostatistics to Delineate Hydrofacies Spatial Distribution

    NASA Astrophysics Data System (ADS)

    Song, X.; Chen, X.; Ye, M.; Dai, Z.; Hammond, G. E.

    2015-12-01

    We present a new framework for delineating spatial distributions of hydrofacies from indirect data by linking ensemble-based data assimilation method (e.g., Ensemble Kalman filter, EnKF) with indicator geostatistics based on transition probability. The nature of ensemble data assimilation makes the framework efficient and flexible to integrate various types of observation data. We leveraged the level set concept to establish transformations between discrete hydrofacies and continuous variables, which is a critical element to implement ensemble data assimilation methods for hydrofacies delineation. T-PROGS is used to generate realizations of hydrofacies fields given conditioning points. An additional quenching step of T-PROGS is taken to preserve spatial structure of updated hydrofacies after each data assimilation step. This new method is illustrated by a two-dimensional (2-D) synthetic study in which transient hydraulic head data resulting from pumping is assimilated to delineate hydrofacies distribution. Our results showed that the proposed framework was able to characterize hydrofacies distribution and their associated permeability with adequate accuracy even with limited direct hydrofacies data. This method may find broader applications in facies delineation using other types of indirect measurements, such as tracer tests and geophysical surveys.

  15. Geostatistical Analyses of the Persistence and Inventory of Carbon Tetrachloride in the 200 West Area of the Hanford Site

    SciTech Connect

    Murray, Christopher J.; Bott, Yi-Ju; Truex, Michael J.

    2007-04-30

    This report documents two separate geostatistical studies performed by researchers from Pacific Northwest National Laboratory to evaluate the carbon tetrachloride plume in the groundwater on the Hanford Site.

  16. Text Messaging to Improve Hypertension Medication Adherence in African Americans From Primary Care and Emergency Department Settings: Results From Two Randomized Feasibility Studies

    PubMed Central

    Hirzel, Lindsey; Dawood, Rachelle M; Dawood, Katee L; Nichols, Lauren P; Artinian, Nancy T; Schwiebert, Loren; Yarandi, Hossein N; Roberson, Dana N; Plegue, Melissa A; Mango, LynnMarie C; Levy, Phillip D

    2017-01-01

    Background Hypertension (HTN) is an important problem in the United States, with an estimated 78 million Americans aged 20 years and older suffering from this condition. Health disparities related to HTN are common in the United States, with African Americans suffering from greater prevalence of the condition than whites, as well as greater severity, earlier onset, and more complications. Medication adherence is an important component of HTN management, but adherence is often poor, and simply forgetting to take medications is often cited as a reason. Mobile health (mHealth) strategies have the potential to be a low-cost and effective method for improving medication adherence that also has broad reach. Objective Our goal was to determine the feasibility, acceptability, and preliminary clinical effectiveness of BPMED, an intervention designed to improve medication adherence among African Americans with uncontrolled HTN, through fully automated text messaging support. Methods We conducted two parallel, unblinded randomized controlled pilot trials with African-American patients who had uncontrolled HTN, recruited from primary care and emergency department (ED) settings. In each trial, participants were randomized to receive either usual care or the BPMED intervention for one month. Data were collected in-person at baseline and one-month follow-up, assessing the effect on medication adherence, systolic and diastolic blood pressure (SBP and DBP), medication adherence self-efficacy, and participant satisfaction. Data for both randomized controlled pilot trials were analyzed separately and combined. Results A total of 58 primary care and 65 ED participants were recruited with retention rates of 91% (53/58) and 88% (57/65), respectively. BPMED participants consistently showed numerically greater, yet nonsignificant, improvements in measures of medication adherence (mean change 0.9, SD 2.0 vs mean change 0.5, SD 1.5, P=.26), SBP (mean change –12.6, SD 24.0 vs mean change

  17. An in vitro systematic spectroscopic examination of the photostabilities of a random set of commercial sunscreen lotions and their chemical UVB/UVA active agents.

    PubMed

    Serpone, Nick; Salinaro, Angela; Emeline, Alexei V; Horikoshi, Satoshi; Hidaka, Hisao; Zhao, Jincai

    2002-12-01

    The photostabilities of a random set of commercially available sunscreen lotions and their active ingredients are examined spectroscopically subsequent to simulated sunlight UV exposure. Loss of filtering efficacy can occur because of possible photochemical modifications of the sunscreen active agents. Changes in absorption of UVA/ UVB sunlight by agents in sunscreen lotions also leads to a reduction of the expected photoprotection of human skin and DNA against the harmful UV radiation. The active ingredients were investigated in aqueous media and in organic solvents of various polarities (methanol, acetonitrile, and n-hexane) under aerobic and anaerobic conditions The UV absorption features are affected by the nature of the solvents with properties closely related to oil-in-water (o/w) or water-in-oil (w/o) emulsions actually used in sunscreen formulations, and by the presence of molecular oxygen. The photostabilities of two combined chemical ingredients (oxybenzone and octyl methoxycinnamate) and the combination oxybenzone/titanium dioxide were also explored. In the latter case, oxybenzone undergoes significant photodegradation in the presence of the physical filter TiO2.

  18. Use of geostatistics in planning optimum drilling program

    SciTech Connect

    Ghose S. )

    1989-08-01

    Application of geostatistics in the natural resources industry is well established. In a typical process of estimation, the statistically dependent geological data are used to predict the characteristics of a deposit. The estimator used is the best linear unbiased estimator (or BLUE), and a numerical factor of confidence is also provided. The natural inhomogeneity and anisotropy of a deposit are also quantified with preciseness. Drilling is the most reliable way of obtaining data for mining and related industries. However, it is often difficult to decide what is the optimum number of drill holes necessary for evaluation. In this paper, sequential measures of percent variation at 95% confidence level of a geological variable have been used to decipher economically optimum drilling density. A coal reserve model has been used to illustrate the method and findings. Fictitious drilling data were added (within the domain of population characteristics) in stages, to obtain a point of stability, beyond which the gain was significant (diminishing marginal benefit). The final relations are established by graphically projecting and comparing two variables - cost and precision. By mapping the percent variation at each stage, the localized areas of discrepancies can be identified. These are the locations where additional drilling is needed. The system can be controlled if performed at progressive stages and the preciseness toward stability is monitored.

  19. Kriging in the Shadows: Geostatistical Interpolation for Remote Sensing

    NASA Technical Reports Server (NTRS)

    Rossi, Richard E.; Dungan, Jennifer L.; Beck, Louisa R.

    1994-01-01

    It is often useful to estimate obscured or missing remotely sensed data. Traditional interpolation methods, such as nearest-neighbor or bilinear resampling, do not take full advantage of the spatial information in the image. An alternative method, a geostatistical technique known as indicator kriging, is described and demonstrated using a Landsat Thematic Mapper image in southern Chiapas, Mexico. The image was first classified into pasture and nonpasture land cover. For each pixel that was obscured by cloud or cloud shadow, the probability that it was pasture was assigned by the algorithm. An exponential omnidirectional variogram model was used to characterize the spatial continuity of the image for use in the kriging algorithm. Assuming a cutoff probability level of 50%, the error was shown to be 17% with no obvious spatial bias but with some tendency to categorize nonpasture as pasture (overestimation). While this is a promising result, the method's practical application in other missing data problems for remotely sensed images will depend on the amount and spatial pattern of the unobscured pixels and missing pixels and the success of the spatial continuity model used.

  20. Spatiotemporal analysis of olive flowering using geostatistical techniques.

    PubMed

    Rojo, Jesús; Pérez-Badia, Rosa

    2015-02-01

    Analysis of flowering patterns in the olive (Olea europaea L.) are of considerable agricultural and ecological interest, and also provide valuable information for allergy-sufferers, enabling identification of the major sources of airborne pollen at any given moment by interpreting the aerobiological data recorded in pollen traps. The present spatiotemporal analysis of olive flowering in central Spain combined geostatistical techniques with the application of a Geographic Information Systems, and compared results for flowering intensity with airborne pollen records. The results were used to obtain continuous phenological maps which determined the pattern of the succession of the olive flowering. The results show also that, although the highest airborne olive-pollen counts were recorded during the greatest flowering intensity of the groves closest to the pollen trap, the counts recorded at the start of the pollen season were not linked to local olive groves, which had not yet begin to flower. To detect the remote sources of olive pollen several episodes of pollen recorded before the local flowering season were analysed using a HYSPLIT trajectory model and the findings showed that western, southern and southwestern winds transported pollen grains into the study area from earlier-flowering groves located outside the territory.

  1. An interactive Bayesian geostatistical inverse protocol for hydraulic tomography

    USGS Publications Warehouse

    Fienen, Michael N.; Clemo, Tom; Kitanidis, Peter K.

    2008-01-01

    Hydraulic tomography is a powerful technique for characterizing heterogeneous hydrogeologic parameters. An explicit trade-off between characterization based on measurement misfit and subjective characterization using prior information is presented. We apply a Bayesian geostatistical inverse approach that is well suited to accommodate a flexible model with the level of complexity driven by the data and explicitly considering uncertainty. Prior information is incorporated through the selection of a parameter covariance model characterizing continuity and providing stability. Often, discontinuities in the parameter field, typically caused by geologic contacts between contrasting lithologic units, necessitate subdivision into zones across which there is no correlation among hydraulic parameters. We propose an interactive protocol in which zonation candidates are implied from the data and are evaluated using cross validation and expert knowledge. Uncertainty introduced by limited knowledge of dynamic regional conditions is mitigated by using drawdown rather than native head values. An adjoint state formulation of MODFLOW-2000 is used to calculate sensitivities which are used both for the solution to the inverse problem and to guide protocol decisions. The protocol is tested using synthetic two-dimensional steady state examples in which the wells are located at the edge of the region of interest.

  2. A multiple-point geostatistical method for characterizing uncertainty of subsurface alluvial units and its effects on flow and transport

    USGS Publications Warehouse

    Cronkite-Ratcliff, C.; Phelps, G.A.; Boucher, A.

    2012-01-01

    This report provides a proof-of-concept to demonstrate the potential application of multiple-point geostatistics for characterizing geologic heterogeneity and its effect on flow and transport simulation. The study presented in this report is the result of collaboration between the U.S. Geological Survey (USGS) and Stanford University. This collaboration focused on improving the characterization of alluvial deposits by incorporating prior knowledge of geologic structure and estimating the uncertainty of the modeled geologic units. In this study, geologic heterogeneity of alluvial units is characterized as a set of stochastic realizations, and uncertainty is indicated by variability in the results of flow and transport simulations for this set of realizations. This approach is tested on a hypothetical geologic scenario developed using data from the alluvial deposits in Yucca Flat, Nevada. Yucca Flat was chosen as a data source for this test case because it includes both complex geologic and hydrologic characteristics and also contains a substantial amount of both surface and subsurface geologic data. Multiple-point geostatistics is used to model geologic heterogeneity in the subsurface. A three-dimensional (3D) model of spatial variability is developed by integrating alluvial units mapped at the surface with vertical drill-hole data. The SNESIM (Single Normal Equation Simulation) algorithm is used to represent geologic heterogeneity stochastically by generating 20 realizations, each of which represents an equally probable geologic scenario. A 3D numerical model is used to simulate groundwater flow and contaminant transport for each realization, producing a distribution of flow and transport responses to the geologic heterogeneity. From this distribution of flow and transport responses, the frequency of exceeding a given contaminant concentration threshold can be used as an indicator of uncertainty about the location of the contaminant plume boundary.

  3. Geostatistical discrimination between different sources of soil pollutants using a magneto-geochemical data set.

    PubMed

    Zawadzki, Jarosław; Szuszkiewicz, Marcin; Fabijańczyk, Piotr; Magiera, Tadeusz

    2016-12-01

    The primary goal of this work was to distinguish between soil pollution from long-range and local transport of atmospheric pollutants using soil magnetometry supported by geochemical analyses. The study area was located in the Izery region of Poland (within the "Black Triangle" region, which is the nickname for one of Europe's most polluted areas, where Germany, Poland and the Czech Republic meet). One site of the study area was situated in the Forest Glade and was exposed to anthropogenic pollution from a former glasswork. The second site of the study area was located on a neighboring hill (Granicznik) of which the western, northwestern and southwestern parts of the slope were exposed to the long-range transport of atmospheric pollutants from the Czech Republic, Germany and Poland. Magnetic susceptibility was measured on the soil surface and in the soil samples using a MS2 Bartington meter equipped with MS2D and MS2C sensors, respectively. Using soil magnetometry, it was possible to discriminate between long-range transport of atmospheric pollutants and anthropogenic pollution related to the former glasswork located in the Forest Glade. Additionally, using MS2C measurements and geochemical analyses of sixteen trace elements, it was possible to discriminate between natural and anthropogenic origins of a soil magnetic susceptibility signal. Our results indicate that the Forest Glade site is characterized by relatively significant anthropogenic translocation of topsoil horizons, presence of artefacts, more hot spots, very high spatial variability, and higher nugget effect than on the Granicznik Hill.

  4. GIS, geostatistics, metadata banking, and tree-based models for data analysis and mapping in environmental monitoring and epidemiology.

    PubMed

    Schröder, Winfried

    2006-05-01

    By the example of environmental monitoring, some applications of geographic information systems (GIS), geostatistics, metadata banking, and Classification and Regression Trees (CART) are presented. These tools are recommended for mapping statistically estimated hot spots of vectors and pathogens. GIS were introduced as tools for spatially modelling the real world. The modelling can be done by mapping objects according to the spatial information content of data. Additionally, this can be supported by geostatistical and multivariate statistical modelling. This is demonstrated by the example of modelling marine habitats of benthic communities and of terrestrial ecoregions. Such ecoregionalisations may be used to predict phenomena based on the statistical relation between measurements of an interesting phenomenon such as, e.g., the incidence of medically relevant species and correlated characteristics of the ecoregions. The combination of meteorological data and data on plant phenology can enhance the spatial resolution of the information on climate change. To this end, meteorological and phenological data have to be correlated. To enable this, both data sets which are from disparate monitoring networks have to be spatially connected by means of geostatistical estimation. This is demonstrated by the example of transformation of site-specific data on plant phenology into surface data. The analysis allows for spatial comparison of the phenology during the two periods 1961-1990 and 1991-2002 covering whole Germany. The changes in both plant phenology and air temperature were proved to be statistically significant. Thus, they can be combined by GIS overlay technique to enhance the spatial resolution of the information on the climate change and use them for the prediction of vector incidences at the regional scale. The localisation of such risk hot spots can be done by geometrically merging surface data on promoting factors. This is demonstrated by the example of the

  5. Working with men to prevent intimate partner violence in a conflict-affected setting: a pilot cluster randomized controlled trial in rural Côte d’Ivoire

    PubMed Central

    2014-01-01

    Background Evidence from armed conflict settings points to high levels of intimate partner violence (IPV) against women. Current knowledge on how to prevent IPV is limited—especially within war-affected settings. To inform prevention programming on gender-based violence in settings affected by conflict, we evaluated the impact of adding a targeted men’s intervention to a community-based prevention programme in Côte d’Ivoire. Methods We conducted a two-armed, non-blinded cluster randomized trial in Côte d’Ivoire among 12 pair-matched communities spanning government-controlled, UN buffer, and rebel–controlled zones. The intervention communities received a 16-week IPV prevention intervention using a men’s discussion group format. All communities received community-based prevention programmes. Baseline data were collected from couples in September 2010 (pre-intervention) and follow-up in March 2012 (one year post-intervention). The primary trial outcome was women’s reported experiences of physical and/or sexual IPV in the last 12 months. We also assessed men’s reported intention to use physical IPV, attitudes towards sexual IPV, use of hostility and conflict management skills, and participation in gendered household tasks. An adjusted cluster-level intention to treat analysis was used to compare outcomes between intervention and control communities at follow-up. Results At follow-up, reported levels of physical and/or sexual IPV in the intervention arm had decreased compared to the control arm (ARR 0.52, 95% CI 0.18-1.51, not significant). Men participating in the intervention reported decreased intentions to use physical IPV (ARR 0.83, 95% CI 0.66-1.06) and improved attitudes toward sexual IPV (ARR 1.21, 95% CI 0.77-1.91). Significant differences were found between men in the intervention and control arms’ reported ability to control their hostility and manage conflict (ARR 1.3, 95% CI 1.06-1.58), and participation in gendered household tasks (ARR

  6. A new approach to upscaling fracture network models while preserving geostatistical and geomechanical characteristics

    NASA Astrophysics Data System (ADS)

    Lei, Qinghua; Latham, John-Paul; Tsang, Chin-Fu; Xiang, Jiansheng; Lang, Philipp

    2015-07-01

    A new approach to upscaling two-dimensional fracture network models is proposed for preserving geostatistical and geomechanical characteristics of a smaller-scale "source" fracture pattern. First, the scaling properties of an outcrop system are examined in terms of spatial organization, lengths, connectivity, and normal/shear displacements using fractal geometry and power law relations. The fracture pattern is observed to be nonfractal with the fractal dimension D ≈ 2, while its length distribution tends to follow a power law with the exponent 2 < a < 3. To introduce a realistic distribution of fracture aperture and shear displacement, a geomechanical model using the combined finite-discrete element method captures the response of a fractured rock sample with a domain size L = 2 m under in situ stresses. Next, a novel scheme accommodating discrete-time random walks in recursive self-referencing lattices is developed to nucleate and propagate fractures together with their stress- and scale-dependent attributes into larger domains of up to 54 m × 54 m. The advantages of this approach include preserving the nonplanarity of natural cracks, capturing the existence of long fractures, retaining the realism of variable apertures, and respecting the stress dependency of displacement-length correlations. Hydraulic behavior of multiscale growth realizations is modeled by single-phase flow simulation, where distinct permeability scaling trends are observed for different geomechanical scenarios. A transition zone is identified where flow structure shifts from extremely channeled to distributed as the network scale increases. The results of this paper have implications for upscaling network characteristics for reservoir simulation.

  7. A Novel Approach of Understanding and Incorporating Error of Chemical Transport Models into a Geostatistical Framework

    NASA Astrophysics Data System (ADS)

    Reyes, J.; Vizuete, W.; Serre, M. L.; Xu, Y.

    2015-12-01

    The EPA employs a vast monitoring network to measure ambient PM2.5 concentrations across the United States with one of its goals being to quantify exposure within the population. However, there are several areas of the country with sparse monitoring spatially and temporally. One means to fill in these monitoring gaps is to use PM2.5 modeled estimates from Chemical Transport Models (CTMs) specifically the Community Multi-scale Air Quality (CMAQ) model. CMAQ is able to provide complete spatial coverage but is subject to systematic and random error due to model uncertainty. Due to the deterministic nature of CMAQ, often these uncertainties are not quantified. Much effort is employed to quantify the efficacy of these models through different metrics of model performance. Currently evaluation is specific to only locations with observed data. Multiyear studies across the United States are challenging because the error and model performance of CMAQ are not uniform over such large space/time domains. Error changes regionally and temporally. Because of the complex mix of species that constitute PM2.5, CMAQ error is also a function of increasing PM2.5 concentration. To address this issue we introduce a model performance evaluation for PM2.5 CMAQ that is regionalized and non-linear. This model performance evaluation leads to error quantification for each CMAQ grid. Areas and time periods of error being better qualified. The regionalized error correction approach is non-linear and is therefore more flexible at characterizing model performance than approaches that rely on linearity assumptions and assume homoscedasticity of CMAQ predictions errors. Corrected CMAQ data are then incorporated into the modern geostatistical framework of Bayesian Maximum Entropy (BME). Through cross validation it is shown that incorporating error-corrected CMAQ data leads to more accurate estimates than just using observed data by themselves.

  8. Massively Parallel Geostatistical Inversion of Coupled Processes in Heterogeneous Porous Media

    NASA Astrophysics Data System (ADS)

    Ngo, A.; Schwede, R. L.; Li, W.; Bastian, P.; Ippisch, O.; Cirpka, O. A.

    2012-04-01

    The quasi-linear geostatistical approach is an inversion scheme that can be used to estimate the spatial distribution of a heterogeneous hydraulic conductivity field. The estimated parameter field is considered to be a random variable that varies continuously in space, meets the measurements of dependent quantities (such as the hydraulic head, the concentration of a transported solute or its arrival time) and shows the required spatial correlation (described by certain variogram models). This is a method of conditioning a parameter field to observations. Upon discretization, this results in as many parameters as elements of the computational grid. For a full three dimensional representation of the heterogeneous subsurface it is hardly sufficient to work with resolutions (up to one million parameters) of the model domain that can be achieved on a serial computer. The forward problems to be solved within the inversion procedure consists of the elliptic steady-state groundwater flow equation and the formally elliptic but nearly hyperbolic steady-state advection-dominated solute transport equation in a heterogeneous porous medium. Both equations are discretized by Finite Element Methods (FEM) using fully scalable domain decomposition techniques. Whereas standard conforming FEM is sufficient for the flow equation, for the advection dominated transport equation, which rises well known numerical difficulties at sharp fronts or boundary layers, we use the streamline diffusion approach. The arising linear systems are solved using efficient iterative solvers with an AMG (algebraic multigrid) pre-conditioner. During each iteration step of the inversion scheme one needs to solve a multitude of forward and adjoint problems in order to calculate the sensitivities of each measurement and the related cross-covariance matrix of the unknown parameters and the observations. In order to reduce interprocess communications and to improve the scalability of the code on larger clusters

  9. Examining the spatial distribution of flower thrips in southern highbush blueberries by utilizing geostatistical methods.

    PubMed

    Rhodes, Elena M; Liburd, Oscar E; Grunwald, Sabine

    2011-08-01

    Flower thrips (Frankliniella spp.) are one of the key pests of southern highbush blueberries (Vaccinium corymbosum L. x V. darrowii Camp), a high-value crop in Florida. Thrips' feeding and oviposition injury to flowers can result in fruit scarring that renders the fruit unmarketable. Flower thrips often form areas of high population, termed "hot spots", in blueberry plantings. The objective of this study was to model thrips spatial distribution patterns with geostatistical techniques. Semivariogram models were used to determine optimum trap spacing and two commonly used interpolation methods, inverse distance weighting (IDW) and ordinary kriging (OK), were compared for their ability to model thrips spatial patterns. The experimental design consisted of a grid of 100 white sticky traps spaced at 15.24-m and 7.61-m intervals in 2008 and 2009, respectively. Thirty additional traps were placed randomly throughout the sampling area to collect information on distances shorter than the grid spacing. The semivariogram analysis indicated that, in most cases, spacing traps at least 28.8 m apart would result in spatially independent samples. Also, the 7.61-m grid spacing captured more of the thrips spatial variability than the 15.24-m grid spacing. IDW and OK produced maps with similar accuracy in both years, which indicates that thrips spatial distribution patterns, including "hot spots," can be modeled using either interpolation method. Future studies can use this information to determine if the formation of "hot spots" can be predicted using flower density, temperature, and other environmental factors. If so, this development would allow growers to spot treat the "hot spots" rather than their entire field.

  10. Prospective Randomized Double-Blind Pilot Study of Site-Specific Consensus Atlas Implementation for Rectal Cancer Target Volume Delineation in the Cooperative Group Setting

    SciTech Connect

    Fuller, Clifton D.; Nijkamp, Jasper; Duppen, Joop C.; Rasch, Coen R.N.; Thomas, Charles R.; Wang, Samuel J.; Okunieff, Paul; Jones, William E.; Baseman, Daniel; Patel, Shilpen; Demandante, Carlo G.N.; Harris, Anna M.; Smith, Benjamin D.; Katz, Alan W.; McGann, Camille

    2011-02-01

    Purpose: Variations in target volume delineation represent a significant hurdle in clinical trials involving conformal radiotherapy. We sought to determine the effect of a consensus guideline-based visual atlas on contouring the target volumes. Methods and Materials: A representative case was contoured (Scan 1) by 14 physician observers and a reference expert with and without target volume delineation instructions derived from a proposed rectal cancer clinical trial involving conformal radiotherapy. The gross tumor volume (GTV), and two clinical target volumes (CTVA, including the internal iliac, presacral, and perirectal nodes, and CTVB, which included the external iliac nodes) were contoured. The observers were randomly assigned to receipt (Group A) or nonreceipt (Group B) of a consensus guideline and atlas for anorectal cancers and then instructed to recontour the same case/images (Scan 2). Observer variation was analyzed volumetrically using the conformation number (CN, where CN = 1 equals total agreement). Results: Of 14 evaluable contour sets (1 expert and 7 Group A and 6 Group B observers), greater agreement was found for the GTV (mean CN, 0.75) than for the CTVs (mean CN, 0.46-0.65). Atlas exposure for Group A led to significantly increased interobserver agreement for CTVA (mean initial CN, 0.68, after atlas use, 0.76; p = .03) and increased agreement with the expert reference (initial mean CN, 0.58; after atlas use, 0.69; p = .02). For the GTV and CTVB, neither the interobserver nor the expert agreement was altered after atlas exposure. Conclusion: Consensus guideline atlas implementation resulted in a detectable difference in interobserver agreement and a greater approximation of expert volumes for the CTVA but not for the GTV or CTVB in the specified case. Visual atlas inclusion should be considered as a feature in future clinical trials incorporating conformal RT.

  11. Hand-held echocardiography in the setting of pre-operative cardiac evaluation of patients undergoing non-cardiac surgery: results from a randomized pilot study.

    PubMed

    Cavallari, Ilaria; Mega, Simona; Goffredo, Costanza; Patti, Giuseppe; Chello, Massimo; Di Sciascio, Germano

    2015-06-01

    Transthoracic echocardiography is not a routine test in the pre-operative cardiac evaluation of patients undergoing non-cardiac surgery but may be considered in those with known heart failure and valvular heart disease or complaining cardiac symptoms. In this setting, hand-held echocardiography (HHE) could find a potential application as an alternative to standard echocardiography in selected patients; however, its utility in this context has not been investigated. The aim of this pilot study was to evaluate the conclusiveness of HHE compared to standard echocardiography in this subset of patients. 100 patients scheduled for non-cardiac surgery were randomized to receive a standard exam with a Philips Ie33 or a bedside evaluation with a pocket-size imaging device (Opti-Go, Philips Medical System). The primary endpoint was the percentage of satisfactory diagnosis at the end of the examination referred as conclusiveness. Secondary endpoints were the mean duration time and the mean waiting time to perform the exams. No significant difference in terms of conclusiveness between HHE and standard echo was found (86 vs 96%; P = 0.08). Mean duration time of the examinations was 6.1 ± 1.2 min with HHE and 13.1 ± 2.6 min with standard echocardiography (P < 0.001). HHE resulted in a consistent save of waiting time because it was performed the same day of clinical evaluation whereas patients waited 10.1 ± 6.1 days for a standard echocardiography (P < 0.001). This study suggests the potential role of HHE for pre-operative evaluation of selected patients undergoing non-cardiac surgery, since it provided similar information but it was faster and earlier performed compared to standard echocardiography.

  12. Optimization of ventilator setting by flow and pressure waveforms analysis during noninvasive ventilation for acute exacerbations of COPD: a multicentric randomized controlled trial

    PubMed Central

    2011-01-01

    Introduction The analysis of flow and pressure waveforms generated by ventilators can be useful in the optimization of patient-ventilator interactions, notably in chronic obstructive pulmonary disease (COPD) patients. To date, however, a real clinical benefit of this approach has not been proven. Methods The aim of the present randomized, multi-centric, controlled study was to compare optimized ventilation, driven by the analysis of flow and pressure waveforms, to standard ventilation (same physician, same initial ventilator setting, same time spent at the bedside while the ventilator screen was obscured with numerical data always available). The primary aim was the rate of pH normalization at two hours, while secondary aims were changes in PaCO2, respiratory rate and the patient's tolerance to ventilation (all parameters evaluated at baseline, 30, 120, 360 minutes and 24 hours after the beginning of ventilation). Seventy patients (35 for each group) with acute exacerbation of COPD were enrolled. Results Optimized ventilation led to a more rapid normalization of pH at two hours (51 vs. 26% of patients), to a significant improvement of the patient's tolerance to ventilation at two hours, and to a higher decrease of PaCO2 at two and six hours. Optimized ventilation induced physicians to use higher levels of external positive end-expiratory pressure, more sensitive inspiratory triggers and a faster speed of pressurization. Conclusions The analysis of the waveforms generated by ventilators has a significant positive effect on physiological and patient-centered outcomes during acute exacerbation of COPD. The acquisition of specific skills in this field should be encouraged. Trial registration ClinicalTrials.gov NCT01291303. PMID:22115190

  13. Bootstrapped models for intrinsic random functions

    SciTech Connect

    Campbell, K.

    1988-08-01

    Use of intrinsic random function stochastic models as a basis for estimation in geostatistical work requires the identification of the generalized covariance function of the underlying process. The fact that this function has to be estimated from data introduces an additional source of error into predictions based on the model. This paper develops the sample reuse procedure called the bootstrap in the context of intrinsic random functions to obtain realistic estimates of these errors. Simulation results support the conclusion that bootstrap distributions of functionals of the process, as well as their kriging variance, provide a reasonable picture of variability introduced by imperfect estimation of the generalized covariance function.

  14. Bootstrapped models for intrinsic random functions

    SciTech Connect

    Campbell, K.

    1987-01-01

    The use of intrinsic random function stochastic models as a basis for estimation in geostatistical work requires the identification of the generalized covariance function of the underlying process, and the fact that this function has to be estimated from the data introduces an additional source of error into predictions based on the model. This paper develops the sample reuse procedure called the ''bootstrap'' in the context of intrinsic random functions to obtain realistic estimates of these errors. Simulation results support the conclusion that bootstrap distributions of functionals of the process, as well as of their ''kriging variance,'' provide a reasonable picture of the variability introduced by imperfect estimation of the generalized covariance function.

  15. A geostatistical approach to mapping site response spectral amplifications

    USGS Publications Warehouse

    Thompson, E.M.; Baise, L.G.; Kayen, R.E.; Tanaka, Y.; Tanaka, H.

    2010-01-01

    If quantitative estimates of the seismic properties do not exist at a location of interest then the site response spectral amplifications must be estimated from data collected at other locations. Currently, the most common approach employs correlations of site class with maps of surficial geology. Analogously, correlations of site class with topographic slope can be employed where the surficial geology is unknown. Our goal is to identify and validate a method to estimate site response with greater spatial resolution and accuracy for regions where additional effort is warranted. This method consists of three components: region-specific data collection, a spatial model for interpolating seismic properties, and a theoretical method for computing spectral amplifications from the interpolated seismic properties. We consider three spatial interpolation schemes: correlations with surficial geology, termed the geologic trend (GT), ordinary kriging (OK), and kriging with a trend (KT). We estimate the spectral amplifications from seismic properties using the square root of impedance method, thereby linking the frequency-dependent spectral amplifications to the depth-dependent seismic properties. Thus, the range of periods for which this method is applicable is limited by the depth of exploration. A dense survey of near-surface S-wave slowness (Ss) throughout Kobe, Japan shows that the geostatistical methods give more accurate estimates of Ss than the topographic slope and GT methods, and the OK and KT methods perform equally well. We prefer the KT model because it can be seamlessly integrated with geologic maps that cover larger regions. Empirical spectral amplifications show that the region-specific data achieve more accurate estimates of observed median short-period amplifications than the topographic slope method. ?? 2010 Elsevier B.V.

  16. Geostatistic applied to seismic noise measurements for hydrothermal basin characterization

    NASA Astrophysics Data System (ADS)

    Boaga, Jacopo; Trevisani, Sebastiano; Agostini, Laura; Galgaro, Antonio

    2016-04-01

    We present a geo-statistical analysis applied to seismic noise measurements in the framework of a thermal basin characterization. The site test is located in the N-E part of Italy (Caldiero, Verona Province) where more than 100 passive single station seismic noise measurements were conducted. The final aim was the characterization of an important hydrothermal basin, which is exploited since the Roman Period. The huge amount of measurements offers high density cover, since the measurements point has average spacing of 100 m for a total area investigated of ca 100ha. The HVSR (Horizontal to Vertical Spectral Ratio) is a geophysical passive technique used to retrieve fundamental resonance frequency of the subsoil. The measurement consists in passive recording of seismic noise with 3 components broadband receivers. From the spectral analysis of the recorded data, we can retrieve the resonance frequency of soil and hence information about depth and mechanical properties of soil covers. Since HVSR is a punctual measurement, 2d map of the results are usually extracted with interpolation procedure, as common kriging or natural neighbor techniques. Despite this accurate statistical procedure are rarely adopted for HVSR analysis, limiting the real significance of the dataset. As a matter of fact, rigorous statistical approach of the spatial distribution is neglected in common HVSR geophysical prospecting. Here we present the use of advanced spatial-statistic technique (e.g. cross-validation, residual distribution etc.) applied to HVSR data. Our results show as critic data scrubbing, joined to rigorous statistical approach for data interpolation, are mandatory to assure meaningful structural interpretation of microtremor HVSR survey. The maps obtained are compared with boreholes data, reflection seismic prospecting, and geological information. The proposed procedure highlighted the potential of these quick passive measurements, if correctly treated from the statistical point

  17. Patch-based iterative conditional geostatistical simulation using graph cuts

    NASA Astrophysics Data System (ADS)

    Li, Xue; Mariethoz, Gregoire; Lu, DeTang; Linde, Niklas

    2016-08-01

    Training image-based geostatistical methods are increasingly popular in groundwater hydrology even if existing algorithms present limitations that often make real-world applications difficult. These limitations include a computational cost that can be prohibitive for high-resolution 3-D applications, the presence of visual artifacts in the model realizations, and a low variability between model realizations due to the limited pool of patterns available in a finite-size training image. In this paper, we address these issues by proposing an iterative patch-based algorithm which adapts a graph cuts methodology that is widely used in computer graphics. Our adapted graph cuts method optimally cuts patches of pixel values borrowed from the training image and assembles them successively, each time accounting for the information of previously stitched patches. The initial simulation result might display artifacts, which are identified as regions of high cost. These artifacts are reduced by iteratively placing new patches in high-cost regions. In contrast to most patch-based algorithms, the proposed scheme can also efficiently address point conditioning. An advantage of the method is that the cut process results in the creation of new patterns that are not present in the training image, thereby increasing pattern variability. To quantify this effect, a new measure of variability is developed, the merging index, quantifies the pattern variability in the realizations with respect to the training image. A series of sensitivity analyses demonstrates the stability of the proposed graph cuts approach, which produces satisfying simulations for a wide range of parameters values. Applications to 2-D and 3-D cases are compared to state-of-the-art multiple-point methods. The results show that the proposed approach obtains significant speedups and increases variability between realizations. Connectivity functions applied to 2-D models transport simulations in 3-D models are used to

  18. Geostatistical applications in petroleum geology and sedimentary geology

    SciTech Connect

    Murray, C.J.

    1992-01-01

    Statistical tools can aid the geologist in understanding geologic data, and in the solution of real-world problems. This thesis examines several tools, concentrating on techniques with wide applicability in sedimentary and petroleum geology. A case study presents the use of geostatistics for mapping hydrocarbon pore volume and risk assessment in an area surrounding Amos Draw field in the northern Powder River basin of Wyoming. The study used sequential Gaussian and indicator simulation techniques, and documents the ability of indicator simulation to incorporate correlated secondary data. Reservoir characterization requires the generation of numerical grids of geologic properties. Because those properties differ for each rock type, one should first simulate the distribution of rock types, and then the distribution of the reservoir properties. This thesis proposes two multivariate statistical techniques, discriminant function analysis and cluster analysis, for the identification of petrophysical rock types in the Muddy Formation at Amos Draw. The geology of those rock types is discussed using core descriptions, thin-sections, and well log data. The rock types were simulated in three dimensions using indicator principal component simulation. The study also used simulated annealing for post-processing of the simulations, incorporating information from the wells on the transition frequencies between the rock types. The third case study used runs analysis for the identification of patterns in bed thickness and grain size in turbidites. Upward-thickening and thinning patterns have been used to assign turbidite sequences to depositional environments, although there has been disagreement on their identification. Runs analysis was applied to a turbidite section in the Sites Formation at Cache Creek, in northern California.

  19. Bayesian Geostatistical Modeling of Malaria Indicator Survey Data in Angola

    PubMed Central

    Gosoniu, Laura; Veta, Andre Mia; Vounatsou, Penelope

    2010-01-01

    The 2006–2007 Angola Malaria Indicator Survey (AMIS) is the first nationally representative household survey in the country assessing coverage of the key malaria control interventions and measuring malaria-related burden among children under 5 years of age. In this paper, the Angolan MIS data were analyzed to produce the first smooth map of parasitaemia prevalence based on contemporary nationwide empirical data in the country. Bayesian geostatistical models were fitted to assess the effect of interventions after adjusting for environmental, climatic and socio-economic factors. Non-linear relationships between parasitaemia risk and environmental predictors were modeled by categorizing the covariates and by employing two non-parametric approaches, the B-splines and the P-splines. The results of the model validation showed that the categorical model was able to better capture the relationship between parasitaemia prevalence and the environmental factors. Model fit and prediction were handled within a Bayesian framework using Markov chain Monte Carlo (MCMC) simulations. Combining estimates of parasitaemia prevalence with the number of children under we obtained estimates of the number of infected children in the country. The population-adjusted prevalence ranges from in Namibe province to in Malanje province. The odds of parasitaemia in children living in a household with at least ITNs per person was by 41% lower (CI: 14%, 60%) than in those with fewer ITNs. The estimates of the number of parasitaemic children produced in this paper are important for planning and implementing malaria control interventions and for monitoring the impact of prevention and control activities. PMID:20351775

  20. TiConverter: A training image converting tool for multiple-point geostatistics

    NASA Astrophysics Data System (ADS)

    Fadlelmula F., Mohamed M.; Killough, John; Fraim, Michael

    2016-11-01

    TiConverter is a tool developed to ease the application of multiple-point geostatistics whether by the open source Stanford Geostatistical Modeling Software (SGeMS) or other available commercial software. TiConverter has a user-friendly interface and it allows the conversion of 2D training images into numerical representations in four different file formats without the need for additional code writing. These are the ASCII (.txt), the geostatistical software library (GSLIB) (.txt), the Isatis (.dat), and the VTK formats. It performs the conversion based on the RGB color system. In addition, TiConverter offers several useful tools including image resizing, smoothing, and segmenting tools. The purpose of this study is to introduce the TiConverter, and to demonstrate its application and advantages with several examples from the literature.

  1. Use of geostatistics for remediation planning to transcend urban political boundaries.

    PubMed

    Milillo, Tammy M; Sinha, Gaurav; Gardella, Joseph A

    2012-11-01

    Soil remediation plans are often dictated by areas of jurisdiction or property lines instead of scientific information. This study exemplifies how geostatistically interpolated surfaces can substantially improve remediation planning. Ordinary kriging, ordinary co-kriging, and inverse distance weighting spatial interpolation methods were compared for analyzing surface and sub-surface soil sample data originally collected by the US EPA and researchers at the University at Buffalo in Hickory Woods, an industrial-residential neighborhood in Buffalo, NY, where both lead and arsenic contamination is present. Past clean-up efforts estimated contamination levels from point samples, but parcel and agency jurisdiction boundaries were used to define remediation sites, rather than geostatistical models estimating the spatial behavior of the contaminants in the soil. Residents were understandably dissatisfied with the arbitrariness of the remediation plan. In this study we show how geostatistical mapping and participatory assessment can make soil remediation scientifically defensible, socially acceptable, and economically feasible.

  2. Geostatistical analysis of groundwater level using Euclidean and non-Euclidean distance metrics and variable variogram fitting criteria

    NASA Astrophysics Data System (ADS)

    Theodoridou, Panagiota G.; Karatzas, George P.; Varouchakis, Emmanouil A.; Corzo Perez, Gerald A.

    2015-04-01

    Groundwater level is an important information in hydrological modelling. Geostatistical methods are often employed to map the free surface of an aquifer. In geostatistical analysis using Kriging techniques the selection of the optimal variogram model is very important for the optimal method performance. This work compares three different criteria, the least squares sum method, the Akaike Information Criterion and the Cressie's Indicator, to assess the theoretical variogram that fits to the experimental one and investigates the impact on the prediction results. Moreover, five different distance functions (Euclidean, Minkowski, Manhattan, Canberra, and Bray-Curtis) are applied to calculate the distance between observations that affects both the variogram calculation and the Kriging estimator. Cross validation analysis in terms of Ordinary Kriging is applied by using sequentially a different distance metric and the above three variogram fitting criteria. The spatial dependence of the observations in the tested dataset is studied by fitting classical variogram models and the Matérn model. The proposed comparison analysis performed for a data set of two hundred fifty hydraulic head measurements distributed over an alluvial aquifer that covers an area of 210 km2. The study area is located in the Prefecture of Drama, which belongs to the Water District of East Macedonia (Greece). This area was selected in terms of hydro-geological data availability and geological homogeneity. The analysis showed that a combination of the Akaike information Criterion for the variogram fitting assessment and the Brays-Curtis distance metric provided the most accurate cross-validation results. The Power-law variogram model provided the best fit to the experimental data. The aforementioned approach for the specific dataset in terms of the Ordinary Kriging method improves the prediction efficiency in comparison to the classical Euclidean distance metric. Therefore, maps of the spatial

  3. Evaluation of Yoga for Preventing Adolescent Substance Use Risk Factors in a Middle School Setting: A Preliminary Group-Randomized Controlled Trial.

    PubMed

    Butzer, Bethany; LoRusso, Amanda; Shin, Sunny H; Khalsa, Sat Bir S

    2017-03-01

    Adolescence is a key developmental period for preventing substance use initiation, however prevention programs solely providing educational information about the dangers of substance use rarely change adolescent substance use behaviors. Recent research suggests that mind-body practices such as yoga may have beneficial effects on several substance use risk factors, and that these practices may serve as promising interventions for preventing adolescent substance use. The primary aim of the present study was to test the efficacy of yoga for reducing substance use risk factors during early adolescence. Seventh-grade students in a public school were randomly assigned by classroom to receive either a 32-session yoga intervention (n = 117) in place of their regular physical education classes or to continue with physical-education-as-usual (n = 94). Participants (63.2 % female; 53.6 % White) completed pre- and post-intervention questionnaires assessing emotional self-regulation, perceived stress, mood impairment, impulsivity, substance use willingness, and actual substance use. Participants also completed questionnaires at 6-months and 1-year post-intervention. Results revealed that participants in the control condition were significantly more willing to try smoking cigarettes immediately post-intervention than participants in the yoga condition. Immediate pre- to post-intervention differences did not emerge for the remaining outcomes. However, long-term follow-up analyses revealed a pattern of delayed effects in which females in the yoga condition, and males in the control condition, demonstrated improvements in emotional self-control. The findings suggest that school-based yoga may have beneficial effects with regard to preventing males' and females' willingness to smoke cigarettes, as well as improving emotional self-control in females. However additional research is required, particularly with regard to the potential long-term effects of mind-body interventions

  4. Mapping seabed sediments: Comparison of manual, geostatistical, object-based image analysis and machine learning approaches

    NASA Astrophysics Data System (ADS)

    Diesing, Markus; Green, Sophie L.; Stephens, David; Lark, R. Murray; Stewart, Heather A.; Dove, Dayton

    2014-08-01

    Marine spatial planning and conservation need underpinning with sufficiently detailed and accurate seabed substrate and habitat maps. Although multibeam echosounders enable us to map the seabed with high resolution and spatial accuracy, there is still a lack of fit-for-purpose seabed maps. This is due to the high costs involved in carrying out systematic seabed mapping programmes and the fact that the development of validated, repeatable, quantitative and objective methods of swath acoustic data interpretation is still in its infancy. We compared a wide spectrum of approaches including manual interpretation, geostatistics, object-based image analysis and machine-learning to gain further insights into the accuracy and comparability of acoustic data interpretation approaches based on multibeam echosounder data (bathymetry, backscatter and derivatives) and seabed samples with the aim to derive seabed substrate maps. Sample data were split into a training and validation data set to allow us to carry out an accuracy assessment. Overall thematic classification accuracy ranged from 67% to 76% and Cohen's kappa varied between 0.34 and 0.52. However, these differences were not statistically significant at the 5% level. Misclassifications were mainly associated with uncommon classes, which were rarely sampled. Map outputs were between 68% and 87% identical. To improve classification accuracy in seabed mapping, we suggest that more studies on the effects of factors affecting the classification performance as well as comparative studies testing the performance of different approaches need to be carried out with a view to developing guidelines for selecting an appropriate method for a given dataset. In the meantime, classification accuracy might be improved by combining different techniques to hybrid approaches and multi-method ensembles.

  5. Quantifying the Relationship between Dynamical Cores and Physical Parameterizations by Geostatistical Methods

    NASA Astrophysics Data System (ADS)

    Yorgun, M. S.; Rood, R. B.

    2010-12-01

    The behavior of atmospheric models is sensitive to the algorithms that are used to represent the equations of motion. Typically, comprehensive models are conceived in terms of the resolved fluid dynamics (i.e. the dynamical core) and subgrid, unresolved physics represented by parameterizations. Deterministic weather predictions are often validated with feature-by-feature comparison. Probabilistic weather forecasts and climate projects are evaluated with statistical methods. We seek to develop model evaluation strategies that identify like “objects” - coherent systems with an associated set of measurable parameters. This makes it possible to evaluate processes in models without needing to reproduce the time and location of, for example, a particular observed cloud system. Process- and object-based evaluation preserves information in the observations by avoiding the need for extensive spatial and temporal averaging. As a concrete example, we focus on analyzing how the choice of dynamical core impacts the representation of precipitation in the Pacific Northwest of the United States, Western Canada, and Alaska; this brings attention to the interaction of the resolved and the parameterized components of the model. Two dynamical cores are considered within the Community Atmosphere Model. These are the Spectral (Eulerian), which relies on global basis functions and the Finite Volume (FV), which uses only local information. We introduce the concept of "meteorological realism" that is, do local representations of large-scale phenomena, for example, fronts and orographic precipitation, look like the observations? A follow on question is, does the representation of these phenomena improve with resolution? Our approach to quantify meteorological realism starts with methods of geospatial statistics. Specifically, we employ variography, which is a geostatistical method which is used to measure the spatial continuity of a regionalized variable, and principle component

  6. Geostatistical analysis of soil geochemical data from an industrial area (Puertollano, South-Central Spain).

    NASA Astrophysics Data System (ADS)

    Esbrí, José M.; Higueras, Pablo; López-Berdonces, Miguel A.; García-Noguero, Eva M.; González-Corrochano, Beatriz; Fernández-Calderón, Sergio; Martínez-Coronado, Alba

    2015-04-01

    Puertollano is the biggest industrial city of Castilla-La Mancha, with 48,086 inhabitants. It is located 250 km South of Madrid in the North border of the Ojailén River valley. The industrial area includes a big coal open pit (ENCASUR), two power plants (EON and ELCOGAS), a petrochemical complex (REPSOL) and a fertiliser factory (ENFERSA), all located in the proximities of the town. These industries suppose a complex scenario in terms of metals and metalloids emissions. For instance, mercury emissions declared to PRTR inventory during 2010 were 210 kg year-1 (REPSOL), 130 kg year-1 (ELCOGAS) and 11,9 kg year-1 (EON). Besides it still remains an unaccounted possibly of diffuse sources of other potentially toxic elements coming from the different industrial sites. Multielemental analyses of soils from two different depths covering the whole valley were carried out by means of XRF with a portable Oxford Instruments device. Geostatistical data treatment was performed using SURFER software, applying block kriging to obtain interpolation maps for the study area. Semivariograms of elemental concentrations make a clear distinction between volatile (Hg, Se) and non-volatile elements (Cu, Ni), with differences in scales and variances between the two soil horizons considered. Semivariograms also show different models for elements emitted by combustion processes (Ni) and for anomalous elements from geological substrate (Pb, Zn). In addition to differences in anisotropy of data, these models reflect different forms of elemental dispersion; despite this, identification of particular sources for the different elements is not possible for this geochemical data set.

  7. Error modeling based on geostatistics for uncertainty analysis in crop mapping using Gaofen-1 multispectral imagery

    NASA Astrophysics Data System (ADS)

    You, Jiong; Pei, Zhiyuan

    2015-01-01

    With the development of remote sensing technology, its applications in agriculture monitoring systems, crop mapping accuracy, and spatial distribution are more and more being explored by administrators and users. Uncertainty in crop mapping is profoundly affected by the spatial pattern of spectral reflectance values obtained from the applied remote sensing data. Errors in remotely sensed crop cover information and the propagation in derivative products need to be quantified and handled correctly. Therefore, this study discusses the methods of error modeling for uncertainty characterization in crop mapping using GF-1 multispectral imagery. An error modeling framework based on geostatistics is proposed, which introduced the sequential Gaussian simulation algorithm to explore the relationship between classification errors and the spectral signature from remote sensing data source. On this basis, a misclassification probability model to produce a spatially explicit classification error probability surface for the map of a crop is developed, which realizes the uncertainty characterization for crop mapping. In this process, trend surface analysis was carried out to generate a spatially varying mean response and the corresponding residual response with spatial variation for the spectral bands of GF-1 multispectral imagery. Variogram models were employed to measure the spatial dependence in the spectral bands and the derived misclassification probability surfaces. Simulated spectral data and classification results were quantitatively analyzed. Through experiments using data sets from a region in the low rolling country located at the Yangtze River valley, it was found that GF-1 multispectral imagery can be used for crop mapping with a good overall performance, the proposal error modeling framework can be used to quantify the uncertainty in crop mapping, and the misclassification probability model can summarize the spatial variation in map accuracy and is helpful for

  8. UNCERT: geostatistics, uncertainty analysis and visualization software applied to groundwater flow and contaminant transport modeling

    NASA Astrophysics Data System (ADS)

    Wingle, William L.; Poeter, Eileen P.; McKenna, Sean A.

    1999-05-01

    UNCERT is a 2D and 3D geostatistics, uncertainty analysis and visualization software package applied to ground water flow and contaminant transport modeling. It is a collection of modules that provides tools for linear regression, univariate statistics, semivariogram analysis, inverse-distance gridding, trend-surface analysis, simple and ordinary kriging and discrete conditional indicator simulation. Graphical user interfaces for MODFLOW and MT3D, ground water flow and contaminant transport models, are provided for streamlined data input and result analysis. Visualization tools are included for displaying data input and output. These include, but are not limited to, 2D and 3D scatter plots, histograms, box and whisker plots, 2D contour maps, surface renderings of 2D gridded data and 3D views of gridded data. By design, UNCERT's graphical user interface and visualization tools facilitate model design and analysis. There are few built in restrictions on data set sizes and each module (with two exceptions) can be run in either graphical or batch mode. UNCERT is in the public domain and is available from the World Wide Web with complete on-line and printable (PDF) documentation. UNCERT is written in ANSI-C with a small amount of FORTRAN77, for UNIX workstations running X-Windows and Motif (or Lesstif). This article discusses the features of each module and demonstrates how they can be used individually and in combination. The tools are applicable to a wide range of fields and are currently used by researchers in the ground water, mining, mathematics, chemistry and geophysics, to name a few disciplines.

  9. Spatial analysis of the distribution of Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae) and losses in maize crop productivity using geostatistics.

    PubMed

    Farias, Paulo R S; Barbosa, José C; Busoli, Antonio C; Overal, William L; Miranda, Vicente S; Ribeiro, Susane M

    2008-01-01

    The fall armyworm, Spodoptera frugiperda (J.E. Smith), is one of the chief pests of maize in the Americas. The study of its spatial distribution is fundamental for designing correct control strategies, improving sampling methods, determining actual and potential crop losses, and adopting precise agricultural techniques. In São Paulo state, Brazil, a maize field was sampled at weekly intervals, from germination through harvest, for caterpillar densities, using quadrates. In each of 200 quadrates, 10 plants were sampled per week. Harvest weights were obtained in the field for each quadrate, and ear diameters and lengths were also sampled (15 ears per quadrate) and used to estimate potential productivity of the quadrate. Geostatistical analyses of caterpillar densities showed greatest ranges for small caterpillars when semivariograms were adjusted for a spherical model that showed greatest fit. As the caterpillars developed in the field, their spatial distribution became increasingly random, as shown by a model adjusted to a straight line, indicating a lack of spatial dependence among samples. Harvest weight and ear length followed the spherical model, indicating the existence of spatial variability of the production parameters in the maize field. Geostatistics shows promise for the application of precise methods in the integrated control of pests.

  10. The Use of Geostatistics in the Study of Floral Phenology of Vulpia geniculata (L.) Link

    PubMed Central

    León Ruiz, Eduardo J.; García Mozo, Herminia; Domínguez Vilches, Eugenio; Galán, Carmen

    2012-01-01

    Traditionally phenology studies have been focused on changes through time, but there exist many instances in ecological research where it is necessary to interpolate among spatially stratified samples. The combined use of Geographical Information Systems (GIS) and Geostatistics can be an essential tool for spatial analysis in phenological studies. Geostatistics are a family of statistics that describe correlations through space/time and they can be used for both quantifying spatial correlation and interpolating unsampled points. In the present work, estimations based upon Geostatistics and GIS mapping have enabled the construction of spatial models that reflect phenological evolution of Vulpia geniculata (L.) Link throughout the study area during sampling season. Ten sampling points, scattered troughout the city and low mountains in the “Sierra de Córdoba” were chosen to carry out the weekly phenological monitoring during flowering season. The phenological data were interpolated by applying the traditional geostatitical method of Kriging, which was used to ellaborate weekly estimations of V. geniculata phenology in unsampled areas. Finally, the application of Geostatistics and GIS to create phenological maps could be an essential complement in pollen aerobiological studies, given the increased interest in obtaining automatic aerobiological forecasting maps. PMID:22629169

  11. Combined assimilation of streamflow and satellite soil moisture with the particle filter and geostatistical modeling

    NASA Astrophysics Data System (ADS)

    Yan, Hongxiang; Moradkhani, Hamid

    2016-08-01

    Assimilation of satellite soil moisture and streamflow data into a distributed hydrologic model has received increasing attention over the past few years. This study provides a detailed analysis of the joint and separate assimilation of streamflow and Advanced Scatterometer (ASCAT) surface soil moisture into a distributed Sacramento Soil Moisture Accounting (SAC-SMA) model, with the use of recently developed particle filter-Markov chain Monte Carlo (PF-MCMC) method. Performance is assessed over the Salt River Watershed in Arizona, which is one of the watersheds without anthropogenic effects in Model Parameter Estimation Experiment (MOPEX). A total of five data assimilation (DA) scenarios are designed and the effects of the locations of streamflow gauges and the ASCAT soil moisture on the predictions of soil moisture and streamflow are assessed. In addition, a geostatistical model is introduced to overcome the significantly biased satellite soil moisture and also discontinuity issue. The results indicate that: (1) solely assimilating outlet streamflow can lead to biased soil moisture estimation; (2) when the study area can only be partially covered by the satellite data, the geostatistical approach can estimate the soil moisture for those uncovered grid cells; (3) joint assimilation of streamflow and soil moisture from geostatistical modeling can further improve the surface soil moisture prediction. This study recommends that the geostatistical model is a helpful tool to aid the remote sensing technique and the hydrologic DA study.

  12. Estimating Solar PV Output Using Modern Space/Time Geostatistics (Presentation)

    SciTech Connect

    Lee, S. J.; George, R.; Bush, B.

    2009-04-29

    This presentation describes a project that uses mapping techniques to predict solar output at subhourly resolution at any spatial point, develop a methodology that is applicable to natural resources in general, and demonstrate capability of geostatistical techniques to predict the output of a potential solar plant.

  13. The use of geostatistics in the study of floral phenology of Vulpia geniculata (L.) link.

    PubMed

    León Ruiz, Eduardo J; García Mozo, Herminia; Domínguez Vilches, Eugenio; Galán, Carmen

    2012-01-01

    Traditionally phenology studies have been focused on changes through time, but there exist many instances in ecological research where it is necessary to interpolate among spatially stratified samples. The combined use of Geographical Information Systems (GIS) and Geostatistics can be an essential tool for spatial analysis in phenological studies. Geostatistics are a family of statistics that describe correlations through space/time and they can be used for both quantifying spatial correlation and interpolating unsampled points. In the present work, estimations based upon Geostatistics and GIS mapping have enabled the construction of spatial models that reflect phenological evolution of Vulpia geniculata (L.) Link throughout the study area during sampling season. Ten sampling points, scattered throughout the city and low mountains in the "Sierra de Córdoba" were chosen to carry out the weekly phenological monitoring during flowering season. The phenological data were interpolated by applying the traditional geostatitical method of Kriging, which was used to elaborate weekly estimations of V. geniculata phenology in unsampled areas. Finally, the application of Geostatistics and GIS to create phenological maps could be an essential complement in pollen aerobiological studies, given the increased interest in obtaining automatic aerobiological forecasting maps.

  14. Integrated geostatistics for modeling fluid contacts and shales in Prudhoe Bay

    SciTech Connect

    Perez, G.; Chopra, A.K.; Severson, C.D.

    1997-12-01

    Geostatistics techniques are being used increasingly to model reservoir heterogeneity at a wide range of scales. A variety of techniques is now available with differing underlying assumptions, complexity, and applications. This paper introduces a novel method of geostatistics to model dynamic gas-oil contacts and shales in the Prudhoe Bay reservoir. The method integrates reservoir description and surveillance data within the same geostatistical framework. Surveillance logs and shale data are transformed to indicator variables. These variables are used to evaluate vertical and horizontal spatial correlation and cross-correlation of gas and shale at different times and to develop variogram models. Conditional simulation techniques are used to generate multiple three-dimensional (3D) descriptions of gas and shales that provide a measure of uncertainty. These techniques capture the complex 3D distribution of gas-oil contacts through time. The authors compare results of the geostatistical method with conventional techniques as well as with infill wells drilled after the study. Predicted gas-oil contacts and shale distributions are in close agreement with gas-oil contacts observed at infill wells.

  15. Adapting geostatistics to analyze spatial and temporal trends in weed populations

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Geostatistics were originally developed in mining to estimate the location, abundance and quality of ore over large areas from soil samples to optimize future mining efforts. Here, some of these methods were adapted to weeds to account for a limited distribution area (i.e., inside a field), variatio...

  16. Geostatistical prediction of stream-flow regime in southeastern United States

    NASA Astrophysics Data System (ADS)

    Pugliese, Alessio; Castellarin, Attilio; Archfield, Stacey; Farmer, William

    2015-04-01

    similar performances independently of the interpretation of the curves (i.e. period-of-record/annual, or complete/seasonal) or Q* (MAF or MAP*); at -site performances are satisfactory or good (i.e. Nash-Sutcliffe Efficiency NSE ranges from 0.60 to 0.90 for cross-validated FDCs, depending on the model setting), while the overall performance at regional scale indicates that OK and TK are associated with smaller BIAS and RMSE relative to the six benchmark procedures. Acknowledgements: We thankfully acknowledge Alessia Bononi and Antonio Liguori for their preliminary analyses and Jon O. Skøien and Edzer Pebesma for their helpful assistance with R-packages rtop and gstat. The study is part of the research activities carried out by the working group: Anthropogenic and Climatic Controls on WateR AvailabilitY (ACCuRAcY) of Panta Rhei - Everything Flows Change in Hydrology and Society (IAHS Scientific Decade 2013-2022). References Pugliese, A., A. Castellarin, A., Brath (2014): Geostatistical prediction of flow-duration curves in an index-flow framework, Hydrol. Earth Syst. Sci., 18, 3801-3816,doi:10.5194/hess-18-3801-2014. Castiglioni, S., A. Castellarin, A. Montanari (2009): Prediction of low-flow indices in ungauged basins through physiographical space-based interpolation, Journal of Hydrology, 378, 272-280.

  17. Advancing Interprofessional Primary Health Care Services in Rural Settings for People with Chronic Low Back Disorders: Protocol of a Community-Based Randomized Controlled Trial

    PubMed Central

    Lovo Grona, Stacey; Milosavljevic, Stephan; Sari, Nazmi; Imeah, Biaka; O’Connell, Megan E

    2016-01-01

    Background Chronic low back disorders (CLBDs) are a substantial burden on individuals and societies, and impact up to 20% of Canadians. Rural and remote residents are approximately 30% more likely to have CLBDs. Reduced access to appropriate team-based health services, including physical therapy, is a key factor that may magnify the impact of CLBD on pain, physical function, overall quality of life, health-related system costs, and individual costs. Objective The purpose of this project is to evaluate the validity, comparative effectiveness, costs, barriers, and facilitators of an interprofessional management approach for people with CLBDs, delivered via telehealth. Methods This project will examine 3 different health care delivery options: (1) in-person nurse practitioner (NP); (2) in-person physical therapist (PT); and (3) a team approach utilizing an NP (in-person) and a PT joining via telehealth. Validity of the telehealth team care model will be explored by comparing the diagnostic categorization and management recommendations arising from participants with CLBD who undergo a team telehealth, in-person NP, and in-person PT assessment. Comparative effectiveness and costs will be examined using a community-based randomized controlled trial in a rural Saskatchewan community with limited PT services. The 3 arms of the trial are: (1) usual care delivered by a local rural NP; (2) a local NP and an urban-based PT joining via telehealth; and (3) face-to-face services by a PT traveling to the community. Patient-reported outcomes of pain, physical function, quality of life, satisfaction, and CLBD care-related costs will be evaluated up to 6 months after the intervention. Patient and provider experiences with the team telehealth approach will be explored through qualitative interviews. Results The study was funded in July 2013 and the University of Saskatchewan Biomedical Research Ethics Board approved the study in November 2013. Participant recruitment began in

  18. Introduction to This Special Issue on Geostatistics and Geospatial Techniques in Remote Sensing

    NASA Technical Reports Server (NTRS)

    Atkinson, Peter; Quattrochi, Dale A.; Goodman, H. Michael (Technical Monitor)

    2000-01-01

    The germination of this special Computers & Geosciences (C&G) issue began at the Royal Geographical Society (with the Institute of British Geographers) (RGS-IBG) annual meeting in January 1997 held at the University of Exeter, UK. The snow and cold of the English winter were tempered greatly by warm and cordial discussion of how to stimulate and enhance cooperation on geostatistical and geospatial research in remote sensing 'across the big pond' between UK and US researchers. It was decided that one way forward would be to hold parallel sessions in 1998 on geostatistical and geospatial research in remote sensing at appropriate venues in both the UK and the US. Selected papers given at these sessions would be published as special issues of C&G on the UK side and Photogrammetric Engineering and Remote Sensing (PE&RS) on the US side. These issues would highlight the commonality in research on geostatistical and geospatial research in remote sensing on both sides of the Atlantic Ocean. As a consequence, a session on "Geostatistics and Geospatial Techniques for Remote Sensing of Land Surface Processes" was held at the RGS-IBG annual meeting in Guildford, Surrey, UK in January 1998, organized by the Modeling and Advanced Techniques Special Interest Group (MAT SIG) of the Remote Sensing Society (RSS). A similar session was held at the Association of American Geographers (AAG) annual meeting in Boston, Massachusetts in March 1998, sponsored by the AAG's Remote Sensing Specialty Group (RSSG). The 10 papers that make up this issue of C&G, comprise 7 papers from the UK and 3 papers from the LIS. We are both co-editors of each of the journal special issues, with the lead editor of each journal issue being from their respective side of the Atlantic. The special issue of PE&RS (vol. 65) that constitutes the other half of this co-edited journal series was published in early 1999, comprising 6 papers by US authors. We are indebted to the International Association for Mathematical

  19. Adaptive Centering with Random Effects: An Alternative to the Fixed Effects Model for Studying Time-Varying Treatments in School Settings

    ERIC Educational Resources Information Center

    Raudenbush, Stephen W.

    2009-01-01

    Fixed effects models are often useful in longitudinal studies when the goal is to assess the impact of teacher or school characteristics on student learning. In this article, I introduce an alternative procedure: adaptive centering with random effects. I show that this procedure can replicate the fixed effects analysis while offering several…

  20. Spectral turning bands for efficient Gaussian random fields generation on GPUs and accelerators

    NASA Astrophysics Data System (ADS)

    Hunger, L.; Cosenza, B.; Kimeswenger, S.; Fahringer, T.

    2015-11-01

    A random field (RF) is a set of correlated random variables associated with different spatial locations. RF generation algorithms are of crucial importance for many scientific areas, such as astrophysics, geostatistics, computer graphics, and many others. Current approaches commonly make use of 3D fast Fourier transform (FFT), which does not scale well for RF bigger than the available memory; they are also limited to regular rectilinear meshes. We introduce random field generation with the turning band method (RAFT), an RF generation algorithm based on the turning band method that is optimized for massively parallel hardware such as GPUs and accelerators. Our algorithm replaces the 3D FFT with a lower-order, one-dimensional FFT followed by a projection step and is further optimized with loop unrolling and blocking. RAFT can easily generate RF on non-regular (non-uniform) meshes and efficiently produce fields with mesh sizes bigger than the available device memory by using a streaming, out-of-core approach. Our algorithm generates RF with the correct statistical behavior and is tested on a variety of modern hardware, such as NVIDIA Tesla, AMD FirePro and Intel Phi. RAFT is faster than the traditional methods on regular meshes and has been successfully applied to two real case scenarios: planetary nebulae and cosmological simulations.

  1. CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Simulation of SET Operation in Phase-Change Random Access Memories with Heater Addition and Ring-Type Contactor for Low-Power Consumption by Finite Element Modeling

    NASA Astrophysics Data System (ADS)

    Gong, Yue-Feng; Song, Zhi-Tang; Ling, Yun; Liu, Yan; Feng, Song-Lin

    2009-11-01

    A three-dimensional finite element model for phase change random access memory (PCRAM) is established for comprehensive electrical and thermal analysis during SET operation. The SET behaviours of the heater addition structure (HS) and the ring-type contact in bottom electrode (RIB) structure are compared with each other. There are two ways to reduce the RESET current, applying a high resistivity interfacial layer and building a new device structure. The simulation results indicate that the variation of SET current with different power reduction ways is little. This study takes the RESET and SET operation current into consideration, showing that the RIB structure PCRAM cell is suitable for future devices with high heat efficiency and high-density, due to its high heat efficiency in RESET operation.

  2. A geostatistical methodology for the optimal design of space-time hydraulic head monitoring networks and its application to the Valle de Querétaro aquifer

    NASA Astrophysics Data System (ADS)

    Herrera, G. S.; Júnez-Ferreira, H. E.

    2012-12-01

    This work introduces a new methodology for the optimal design of space-time hydraulic head monitoring networks and its application to the Valle de Querétaro aquifer in Mexico. The selection of the space-time monitoring points is done using a static Kalman filter combined with a sequential optimization method. The Kalman filter requires as input a space-time covariance matrix, which is derived from a geostatistical analysis. A sequential optimization method that selects the space-time point that minimizes a function of the variance, in each step, is used. We demonstrate the methodology applying it to the redesign of the hydraulic head monitoring network of the Valle de Querétaro aquifer with the objective of selecting from a set of monitoring positions and times, those that minimize the spatiotemporal redundancy. The database for the geostatistical space-time analysis corresponds to information of 273 wells located within the aquifer for the period 1970-2007. A total of 1,435 hydraulic head data were used to construct the experimental space-time variogram. The results show that from the existing monitoring program that consists of 418 space-time monitoring points, only 178 are not redundant. The implied reduction of monitoring costs was possible because the proposed method is successful in propagating information in space and time.

  3. A geostatistical methodology for the optimal design of space-time hydraulic head monitoring networks and its application to the Valle de Querétaro aquifer.

    PubMed

    Júnez-Ferreira, H E; Herrera, G S

    2013-04-01

    This paper presents a new methodology for the optimal design of space-time hydraulic head monitoring networks and its application to the Valle de Querétaro aquifer in Mexico. The selection of the space-time monitoring points is done using a static Kalman filter combined with a sequential optimization method. The Kalman filter requires as input a space-time covariance matrix, which is derived from a geostatistical analysis. A sequential optimization method that selects the space-time point that minimizes a function of the variance, in each step, is used. We demonstrate the methodology applying it to the redesign of the hydraulic head monitoring network of the Valle de Querétaro aquifer with the objective of selecting from a set of monitoring positions and times, those that minimize the spatiotemporal redundancy. The database for the geostatistical space-time analysis corresponds to information of 273 wells located within the aquifer for the period 1970-2007. A total of 1,435 hydraulic head data were used to construct the experimental space-time variogram. The results show that from the existing monitoring program that consists of 418 space-time monitoring points, only 178 are not redundant. The implied reduction of monitoring costs was possible because the proposed method is successful in propagating information in space and time.

  4. Geostatistical applications in ground-water modeling in south-central Kansas

    USGS Publications Warehouse

    Ma, T.-S.; Sophocleous, M.; Yu, Y.-S.

    1999-01-01

    This paper emphasizes the supportive role of geostatistics in applying ground-water models. Field data of 1994 ground-water level, bedrock, and saltwater-freshwater interface elevations in south-central Kansas were collected and analyzed using the geostatistical approach. Ordinary kriging was adopted to estimate initial conditions for ground-water levels and topography of the Permian bedrock at the nodes of a finite difference grid used in a three-dimensional numerical model. Cokriging was used to estimate initial conditions for the saltwater-freshwater interface. An assessment of uncertainties in the estimated data is presented. The kriged and cokriged estimation variances were analyzed to evaluate the adequacy of data employed in the modeling. Although water levels and bedrock elevations are well described by spherical semivariogram models, additional data are required for better cokriging estimation of the interface data. The geostatistically analyzed data were employed in a numerical model of the Siefkes site in the project area. Results indicate that the computed chloride concentrations and ground-water drawdowns reproduced the observed data satisfactorily.This paper emphasizes the supportive role of geostatistics in applying ground-water models. Field data of 1994 ground-water level, bedrock, and saltwater-freshwater interface elevations in south-central Kansas were collected and analyzed using the geostatistical approach. Ordinary kriging was adopted to estimate initial conditions for ground-water levels and topography of the Permian bedrock at the nodes of a finite difference grid used in a three-dimensional numerical model. Cokriging was used to estimate initial conditions for the saltwater-freshwater interface. An assessment of uncertainties in the estimated data is presented. The kriged and cokriged estimation variances were analyzed to evaluate the adequacy of data employed in the modeling. Although water levels and bedrock elevations are well described

  5. Delivering Prevention Interventions to People Living with HIV in Clinical Care Settings: Results of a Cluster Randomized Trial in Kenya, Namibia, and Tanzania

    PubMed Central

    Kidder, Daniel; Medley, Amy; Pals, Sherri L.; Carpenter, Deborah; Howard, Andrea; Antelman, Gretchen; DeLuca, Nicolas; Muhenje, Odylia; Sheriff, Muhsin; Somi, Geoffrey; Katuta, Frieda; Cherutich, Peter; Moore, Janet

    2016-01-01

    We conducted a group randomized trial to assess the feasibility and effectiveness of a multi-component, clinic-based HIV prevention intervention for HIV-positive patients attending clinical care in Namibia, Kenya, and Tanzania. Eighteen HIV care and treatment clinics (six per country) were randomly assigned to intervention or control arms. Approximately 200 sexually active clients from each clinic were enrolled and interviewed at baseline and 6- and 12-months post-intervention. Mixed model logistic regression with random effects for clinic and participant was used to assess the effectiveness of the intervention. Of 3522 HIV-positive patients enrolled, 3034 (86 %) completed a 12-month follow-up interview. Intervention participants were significantly more likely to report receiving provider-delivered messages on disclosure, partner testing, family planning, alcohol reduction, and consistent condom use compared to participants in comparison clinics. Participants in intervention clinics were less likely to report unprotected sex in the past 2 weeks (OR = 0.56, 95 % CI 0.32, 0.99) compared to participants in comparison clinics. In Tanzania, a higher percentage of participants in intervention clinics (17 %) reported using a highly effective method of contraception compared to participants in comparison clinics (10 %, OR = 2.25, 95 % CI 1.24, 4.10). This effect was not observed in Kenya or Namibia. HIV prevention services are feasible to implement as part of routine care and are associated with a self-reported decrease in unprotected sex. Further operational research is needed to identify strategies to address common operational challenges including staff turnover and large patient volumes. PMID:26995678

  6. SRS 2010 Vegetation Inventory GeoStatistical Mapping Results for Custom Reaction Intensity and Total Dead Fuels.

    SciTech Connect

    Edwards, Lloyd A.; Paresol, Bernard

    2014-09-01

    This report of the geostatistical analysis results of the fire fuels response variables, custom reaction intensity and total dead fuels is but a part of an SRS 2010 vegetation inventory project. For detailed description of project, theory and background including sample design, methods, and results please refer to USDA Forest Service Savannah River Site internal report “SRS 2010 Vegetation Inventory GeoStatistical Mapping Report”, (Edwards & Parresol 2013).

  7. What do You Need to Get Male Partners of Pregnant Women Tested for HIV in Resource Limited Settings? The Baby Shower Cluster Randomized Trial.

    PubMed

    Ezeanolue, Echezona E; Obiefune, Michael C; Yang, Wei; Ezeanolue, Chinenye O; Pharr, Jennifer; Osuji, Alice; Ogidi, Amaka G; Hunt, Aaron T; Patel, Dina; Ogedegbe, Gbenga; Ehiri, John E

    2017-02-01

    Male partner involvement has the potential to increase uptake of interventions to prevent mother-to-child transmission of HIV (PMTCT). Finding cultural appropriate strategies to promote male partner involvement in PMTCT programs remains an abiding public health challenge. We assessed whether a congregation-based intervention, the Healthy Beginning Initiative (HBI), would lead to increased uptake of HIV testing among male partners of pregnant women during pregnancy. A cluster-randomized controlled trial of forty churches in Southeastern Nigeria randomly assigned to either the HBI (intervention group; IG) or standard of care referral to a health facility (control group; CG) was conducted. Participants in the IG received education and were offered onsite HIV testing. Overall, 2498 male partners enrolled and participated, a participation rate of 88.9%. Results showed that male partners in the IG were 12 times more likely to have had an HIV test compared to male partners of pregnant women in the CG (CG = 37.71% vs. IG = 84.00%; adjusted odds ratio = 11.9; p < .01). Culturally appropriate and community-based interventions can be effective in increasing HIV testing and counseling among male partners of pregnant women.

  8. Geostatistical simulations for radon indoor with a nested model including the housing factor.

    PubMed

    Cafaro, C; Giovani, C; Garavaglia, M

    2016-01-01

    The radon prone areas definition is matter of many researches in radioecology, since radon is considered a leading cause of lung tumours, therefore the authorities ask for support to develop an appropriate sanitary prevention strategy. In this paper, we use geostatistical tools to elaborate a definition accounting for some of the available information about the dwellings. Co-kriging is the proper interpolator used in geostatistics to refine the predictions by using external covariates. In advance, co-kriging is not guaranteed to improve significantly the results obtained by applying the common lognormal kriging. Here, instead, such multivariate approach leads to reduce the cross-validation residual variance to an extent which is deemed as satisfying. Furthermore, with the application of Monte Carlo simulations, the paradigm provides a more conservative radon prone areas definition than the one previously made by lognormal kriging.

  9. Geostatistics for spatial genetic structures: study of wild populations of perennial ryegrass.

    PubMed

    Monestiez, P; Goulard, M; Charmet, G

    1994-04-01

    Methods based on geostatistics were applied to quantitative traits of agricultural interest measured on a collection of 547 wild populations of perennial ryegrass in France. The mathematical background of these methods, which resembles spatial autocorrelation analysis, is briefly described. When a single variable is studied, the spatial structure analysis is similar to spatial autocorrelation analysis, and a spatial prediction method, called "kriging", gives a filtered map of the spatial pattern over all the sampled area. When complex interactions of agronomic traits with different evaluation sites define a multivariate structure for the spatial analysis, geostatistical methods allow the spatial variations to be broken down into two main spatial structures with ranges of 120 km and 300 km, respectively. The predicted maps that corresponded to each range were interpreted as a result of the isolation-by-distance model and as a consequence of selection by environmental factors. Practical collecting methodology for breeders may be derived from such spatial structures.

  10. Three-dimensional geostatistical inversion of flowmeter and pumping test data.

    PubMed

    Li, Wei; Englert, Andreas; Cirpka, Olaf A; Vereecken, Harry

    2008-01-01

    We jointly invert field data of flowmeter and multiple pumping tests in fully screened wells to estimate hydraulic conductivity using a geostatistical method. We use the steady-state drawdowns of pumping tests and the discharge profiles of flowmeter tests as our data in the inference. The discharge profiles need not be converted to absolute hydraulic conductivities. Consequently, we do not need measurements of depth-averaged hydraulic conductivity at well locations. The flowmeter profiles contain information about relative vertical distributions of hydraulic conductivity, while drawdown measurements of pumping tests provide information about horizontal fluctuation of the depth-averaged hydraulic conductivity. We apply the method to data obtained at the Krauthausen test site of the Forschungszentrum Jülich, Germany. The resulting estimate of our joint three-dimensional (3D) geostatistical inversion shows an improved 3D structure in comparison to the inversion of pumping test data only.

  11. Geochemical and geostatistical evaluation, Arkansas Canyon Planning Unit, Fremont and Custer Counties, Colorado

    USGS Publications Warehouse

    Weiland, E.F.; Connors, R.A.; Robinson, M.L.; Lindemann, J.W.; Meyer, W.T.

    1982-01-01

    A mineral assessment of the Arkansas Canyon Planning Unit was undertaken by Barringer Resources Inc., under the terms of contract YA-553-CTO-100 with the Bureau of Land Management, Colorado State Office. The study was based on a geochemical-geostatistical survey in which 700 stream sediment samples were collected and analyzed for 25 elements. Geochemical results were interpreted by statistical processing which included factor, discriminant, multiple regression and characteristic analysis. The major deposit types evaluated were massive sulfide-base metal, sedimentary and magmatic uranium, thorium vein, magmatic segregation, and carbonatite related deposits. Results of the single element data and multivariate geostatistical analysis indicate that limited potential exists for base metal mineralization near the Horseshoe, El Plomo, and Green Mountain Mines. Thirty areas are considered to be anomalous with regard to one or more of the geochemical parameters evaluated during this study. The evaluation of carbonatite related mineralization was restricted due to the lack of geochemical data specific to this environment.

  12. The effect of implementing the Outcome Questionnaire-45.2 feedback system in Norway: A multisite randomized clinical trial in a naturalistic setting.

    PubMed

    Amble, Ingunn; Gude, Tore; Stubdal, Sven; Andersen, Bror Just; Wampold, Bruce E

    2015-01-01

    It has been claimed that the monitoring of ongoing psychotherapy is of crucial importance for improving the quality of mental health care. This study investigated the effect of using the Norwegian version of the patient feedback system OQ-Analyst using the Outcome Questionnaire-45.2. Patients from six psychiatric clinics in Southern Norway (N = 259) were randomized to feedback (FB) or no feedback (NFB). The main effect of feedback was statistical significant (p = .027), corroborating the hypothesis that feedback would improve the quality of services, although the size of the effect was small to moderate (d = 0.32). The benefits of feedback have to be considered against the costs of implementation.

  13. Application of a computationally efficient geostatistical approach to characterizing variably spaced water-table data

    SciTech Connect

    Quinn, J.J.

    1996-02-01

    Geostatistical analysis of hydraulic head data is useful in producing unbiased contour plots of head estimates and relative errors. However, at most sites being characterized, monitoring wells are generally present at different densities, with clusters of wells in some areas and few wells elsewhere. The problem that arises when kriging data at different densities is in achieving adequate resolution of the grid while maintaining computational efficiency and working within software limitations. For the site considered, 113 data points were available over a 14-mi{sup 2} study area, including 57 monitoring wells within an area of concern of 1.5 mi{sup 2}. Variogram analyses of the data indicate a linear model with a negligible nugget effect. The geostatistical package used in the study allows a maximum grid of 100 by 100 cells. Two-dimensional kriging was performed for the entire study area with a 500-ft grid spacing, while the smaller zone was modeled separately with a 100-ft spacing. In this manner, grid cells for the dense area and the sparse area remained small relative to the well separation distances, and the maximum dimensions of the program were not exceeded. The spatial head results for the detailed zone were then nested into the regional output by use of a graphical, object-oriented database that performed the contouring of the geostatistical output. This study benefitted from the two-scale approach and from very fine geostatistical grid spacings relative to typical data separation distances. The combining of the sparse, regional results with those from the finer-resolution area of concern yielded contours that honored the actual data at every measurement location. The method applied in this study can also be used to generate reproducible, unbiased representations of other types of spatial data.

  14. Geostatistical mapping of effluent-affected sediment distribution on the Palos Verdes shelf

    USGS Publications Warehouse

    Murray, C.J.; Lee, H.J.; Hampton, M.A.

    2002-01-01

    Geostatistical techniques were used to study the spatial continuity of the thickness of effluent-affected sediment in the offshore Palos Verdes Margin area. The thickness data were measured directly from cores and indirectly from high-frequency subbottom profiles collected over the Palos Verdes Margin. Strong spatial continuity of the sediment thickness data was identified, with a maximum range of correlation in excess of 1.4 km. The spatial correlation showed a marked anisotropy, and was more than twice as continuous in the alongshore direction as in the cross-shelf direction. Sequential indicator simulation employing models fit to the thickness data variograms was used to map the distribution of the sediment, and to quantify the uncertainty in those estimates. A strong correlation between sediment thickness data and measurements of the mass of the contaminant p,p???-DDE per unit area was identified. A calibration based on the bivariate distribution of the thickness and p,p???-DDE data was applied using Markov-Bayes indicator simulation to extend the geostatistical study and map the contamination levels in the sediment. Integrating the map grids produced by the geostatistical study of the two variables indicated that 7.8 million m3 of effluent-affected sediment exist in the map area, containing approximately 61-72 Mg (metric tons) of p,p???-DDE. Most of the contaminated sediment (about 85% of the sediment and 89% of the p,p???-DDE) occurs in water depths < 100 m. The geostatistical study also indicated that the samples available for mapping are well distributed and the uncertainty of the estimates of the thickness and contamination level of the sediments is lowest in areas where the contaminated sediment is most prevalent. ?? 2002 Elsevier Science Ltd. All rights reserved.

  15. A geostatistical approach to predicting sulfur content in the Pittsburgh coal bed

    USGS Publications Warehouse

    Watson, W.D.; Ruppert, L.F.; Bragg, L.J.; Tewalt, S.J.

    2001-01-01

    The US Geological Survey (USGS) is completing a national assessment of coal resources in the five top coal-producing regions in the US. Point-located data provide measurements on coal thickness and sulfur content. The sample data and their geologic interpretation represent the most regionally complete and up-to-date assessment of what is known about top-producing US coal beds. The sample data are analyzed using a combination of geologic and Geographic Information System (GIS) models to estimate tonnages and qualities of the coal beds. Traditionally, GIS practitioners use contouring to represent geographical patterns of "similar" data values. The tonnage and grade of coal resources are then assessed by using the contour lines as references for interpolation. An assessment taken to this point is only indicative of resource quantity and quality. Data users may benefit from a statistical approach that would allow them to better understand the uncertainty and limitations of the sample data. To develop a quantitative approach, geostatistics were applied to the data on coal sulfur content from samples taken in the Pittsburgh coal bed (located in the eastern US, in the southwestern part of the state of Pennsylvania, and in adjoining areas in the states of Ohio and West Virginia). Geostatistical methods that account for regional and local trends were applied to blocks 2.7 mi (4.3 km) on a side. The data and geostatistics support conclusions concerning the average sulfur content and its degree of reliability at regional- and economic-block scale over the large, contiguous part of the Pittsburgh outcrop, but not to a mine scale. To validate the method, a comparison was made with the sulfur contents in sample data taken from 53 coal mines located in the study area. The comparison showed a high degree of similarity between the sulfur content in the mine samples and the sulfur content represented by the geostatistically derived contours. Published by Elsevier Science B.V.

  16. Analysis of Large Scale Spatial Variability of Soil Moisture Using a Geostatistical Method

    DTIC Science & Technology

    2010-01-25

    consists of a heating element and thermocouple emplaced in epoxy in a hypodermic needle , which is encased in a porous ceramic matrix. This sensor is...Sensors 2010, 10, 913-932; doi:10.3390/s100100913 sensors ISSN 1424-8220 www.mdpi.com/journal/sensors Article Analysis of Large Scale Spatial...in calibration and validation of large-scale satellite based data assimilation systems. Spatial analysis using geostatistical approaches was used to

  17. Geostatistical mapping of effluent-affected sediment distribution on the Palos Verdes Shelf

    SciTech Connect

    Murray, Christopher J. ); Lee, H J.; Hampton, M A.

    2001-12-01

    Geostatistical techniques were used to study the spatial continuity of the thickness of effluent-affected sediment in the offshore Palos Verdes margin area. The thickness data were measured directly from cores and indirectly from high-frequency subbottom profiles collected over the Palos Verdes Margin. Strong spatial continuity of the sediment thickness data was identified, with a maximum range of correlation in excess of 1.4 km. The spatial correlation showed a marked anisotropy, and was more than twice as continuous in the alongshore direction as in the cross-shelf direction. Sequential indicator simulation employing models fit to the thickness data variograms was used to map the distribution of the sediment, and to quantify the uncertainty in those estimates. A strong correlation between sediment thickness data and measurements of the mass of the contaminant p,p?-DDE per unit area was identified. A calibration based on the bivariate distribution of the thickness and p,p?-DDE data was applied using Markov-Bayes indicator simulation to extend the geostatistical study and map the contamination levels in the sediment. Integrating the map grids produced by the geostatistical study of the two variables indicated that 7.8 million cubic meters of effluent-affected sediment exist in the map area, containing approximately 61 to 72 Mg (metric tons) of p,p?-DDE. Most of the contaminated sediment (about 85% of the sediment and 89% of the p,p?-DDE) occurs in water depths less than 100 m. The geostatistical study also indicated that the samples available for mapping are well distributed and the uncertainty of the estimates of the thickness and contamination level of the sediments is lowest in areas where the contaminated sediment is most prevalent.

  18. Application of Bayesian geostatistics for evaluation of mass discharge uncertainty at contaminated sites

    NASA Astrophysics Data System (ADS)

    Troldborg, Mads; Nowak, Wolfgang; Lange, Ida V.; Santos, Marta C.; Binning, Philip J.; Bjerg, Poul L.

    2012-09-01

    Mass discharge estimates are increasingly being used when assessing risks of groundwater contamination and designing remedial systems at contaminated sites. Such estimates are, however, rather uncertain as they integrate uncertain spatial distributions of both concentration and groundwater flow. Here a geostatistical simulation method for quantifying the uncertainty of the mass discharge across a multilevel control plane is presented. The method accounts for (1) heterogeneity of both the flow field and the concentration distribution through Bayesian geostatistics, (2) measurement uncertainty, and (3) uncertain source zone and transport parameters. The method generates conditional realizations of the spatial flow and concentration distribution. An analytical macrodispersive transport solution is employed to simulate the mean concentration distribution, and a geostatistical model of the Box-Cox transformed concentration data is used to simulate observed deviations from this mean solution. By combining the flow and concentration realizations, a mass discharge probability distribution is obtained. The method has the advantage of avoiding the heavy computational burden of three-dimensional numerical flow and transport simulation coupled with geostatistical inversion. It may therefore be of practical relevance to practitioners compared to existing methods that are either too simple or computationally demanding. The method is demonstrated on a field site contaminated with chlorinated ethenes. For this site, we show that including a physically meaningful concentration trend and the cosimulation of hydraulic conductivity and hydraulic gradient across the transect helps constrain the mass discharge uncertainty. The number of sampling points required for accurate mass discharge estimation and the relative influence of different data types on mass discharge uncertainty is discussed.

  19. Tomogram-based comparison of geostatistical models: Application to the Macrodispersion Experiment (MADE) site

    NASA Astrophysics Data System (ADS)

    Linde, Niklas; Lochbühler, Tobias; Dogan, Mine; Van Dam, Remke L.

    2015-12-01

    We propose a new framework to compare alternative geostatistical descriptions of a given site. Multiple realizations of each of the considered geostatistical models and their corresponding tomograms (based on inversion of noise-contaminated simulated data) are used as a multivariate training image. The training image is scanned with a direct sampling algorithm to obtain conditional realizations of hydraulic conductivity that are not only in agreement with the geostatistical model, but also honor the spatially varying resolution of the site-specific tomogram. Model comparison is based on the quality of the simulated geophysical data from the ensemble of conditional realizations. The tomogram in this study is obtained by inversion of cross-hole ground-penetrating radar (GPR) first-arrival travel time data acquired at the MAcro-Dispersion Experiment (MADE) site in Mississippi (USA). Various heterogeneity descriptions ranging from multi-Gaussian fields to fields with complex multiple-point statistics inferred from outcrops are considered. Under the assumption that the relationship between porosity and hydraulic conductivity inferred from local measurements is valid, we find that conditioned multi-Gaussian realizations and derivatives thereof can explain the crosshole geophysical data. A training image based on an aquifer analog from Germany was found to be in better agreement with the geophysical data than the one based on the local outcrop, which appears to under-represent high hydraulic conductivity zones. These findings are only based on the information content in a single resolution-limited tomogram and extending the analysis to tracer or higher resolution surface GPR data might lead to different conclusions (e.g., that discrete facies boundaries are necessary). Our framework makes it possible to identify inadequate geostatistical models and petrophysical relationships, effectively narrowing the space of possible heterogeneity representations.

  20. Hierarchical probabilistic regionalization of volcanism for Sengan region in Japan using multivariate statistical techniques and geostatistical interpolation techniques

    SciTech Connect

    Park, Jinyong; Balasingham, P; McKenna, Sean Andrew; Pinnaduwa H.S.W. Kulatilake

    2004-09-01

    Sandia National Laboratories, under contract to Nuclear Waste Management Organization of Japan (NUMO), is performing research on regional classification of given sites in Japan with respect to potential volcanic disruption using multivariate statistics and geo-statistical interpolation techniques. This report provides results obtained for hierarchical probabilistic regionalization of volcanism for the Sengan region in Japan by applying multivariate statistical techniques and geostatistical interpolation techniques on the geologic data provided by NUMO. A workshop report produced in September 2003 by Sandia National Laboratories (Arnold et al., 2003) on volcanism lists a set of most important geologic variables as well as some secondary information related to volcanism. Geologic data extracted for the Sengan region in Japan from the data provided by NUMO revealed that data are not available at the same locations for all the important geologic variables. In other words, the geologic variable vectors were found to be incomplete spatially. However, it is necessary to have complete geologic variable vectors to perform multivariate statistical analyses. As a first step towards constructing complete geologic variable vectors, the Universal Transverse Mercator (UTM) zone 54 projected coordinate system and a 1 km square regular grid system were selected. The data available for each geologic variable on a geographic coordinate system were transferred to the aforementioned grid system. Also the recorded data on volcanic activity for Sengan region were produced on the same grid system. Each geologic variable map was compared with the recorded volcanic activity map to determine the geologic variables that are most important for volcanism. In the regionalized classification procedure, this step is known as the variable selection step. The following variables were determined as most important for volcanism: geothermal gradient, groundwater temperature, heat discharge, groundwater

  1. Topsoil moisture mapping using geostatistical techniques under different Mediterranean climatic conditions.

    PubMed

    Martínez-Murillo, J F; Hueso-González, P; Ruiz-Sinoga, J D

    2017-04-06

    Soil mapping has been considered as an important factor in the widening of Soil Science and giving response to many different environmental questions. Geostatistical techniques, through kriging and co-kriging techniques, have made possible to improve the understanding of eco-geomorphologic variables, e.g., soil moisture. This study is focused on mapping of topsoil moisture using geostatistical techniques under different Mediterranean climatic conditions (humid, dry and semiarid) in three small watersheds and considering topography and soil properties as key factors. A Digital Elevation Model (DEM) with a resolution of 1×1m was derived from a topographical survey as well as soils were sampled to analyzed soil properties controlling topsoil moisture, which was measured during 4-years. Afterwards, some topography attributes were derived from the DEM, the soil properties analyzed in laboratory, and the topsoil moisture was modeled for the entire watersheds applying three geostatistical techniques: i) ordinary kriging; ii) co-kriging considering as co-variate topography attributes; and iii) co-kriging ta considering as co-variates topography attributes and gravel content. The results indicated topsoil moisture was more accurately mapped in the dry and semiarid watersheds when co-kriging procedure was performed. The study is a contribution to improve the efficiency and accuracy of studies about the Mediterranean eco-geomorphologic system and soil hydrology in field conditions.

  2. Integration of GIS, Geostatistics, and 3-D Technology to Assess the Spatial Distribution of Soil Moisture

    NASA Technical Reports Server (NTRS)

    Betts, M.; Tsegaye, T.; Tadesse, W.; Coleman, T. L.; Fahsi, A.

    1998-01-01

    The spatial and temporal distribution of near surface soil moisture is of fundamental importance to many physical, biological, biogeochemical, and hydrological processes. However, knowledge of these space-time dynamics and the processes which control them remains unclear. The integration of geographic information systems (GIS) and geostatistics together promise a simple mechanism to evaluate and display the spatial and temporal distribution of this vital hydrologic and physical variable. Therefore, this research demonstrates the use of geostatistics and GIS to predict and display soil moisture distribution under vegetated and non-vegetated plots. The research was conducted at the Winfred Thomas Agricultural Experiment Station (WTAES), Hazel Green, Alabama. Soil moisture measurement were done on a 10 by 10 m grid from tall fescue grass (GR), alfalfa (AA), bare rough (BR), and bare smooth (BS) plots. Results indicated that variance associated with soil moisture was higher for vegetated plots than non-vegetated plots. The presence of vegetation in general contributed to the spatial variability of soil moisture. Integration of geostatistics and GIS can improve the productivity of farm lands and the precision of farming.

  3. A geostatistical methodology to assess the accuracy of unsaturated flow models

    SciTech Connect

    Smoot, J.L.; Williams, R.E.

    1996-04-01

    The Pacific Northwest National Laboratory spatiotemporal movement of water injected into (PNNL) has developed a Hydrologic unsaturated sediments at the Hanford Site in Evaluation Methodology (HEM) to assist the Washington State was used to develop a new U.S. Nuclear Regulatory Commission in method for evaluating mathematical model evaluating the potential that infiltrating meteoric predictions. Measured water content data were water will produce leachate at commercial low- interpolated geostatistically to a 16 x 16 x 36 level radioactive waste disposal sites. Two key grid at several time intervals. Then a issues are raised in the HEM: (1) evaluation of mathematical model was used to predict water mathematical models that predict facility content at the same grid locations at the selected performance, and (2) estimation of the times. Node-by-node comparison of the uncertainty associated with these mathematical mathematical model predictions with the model predictions. The technical objective of geostatistically interpolated values was this research is to adapt geostatistical tools conducted. The method facilitates a complete commonly used for model parameter estimation accounting and categorization of model error at to the problem of estimating the spatial every node. The comparison suggests that distribution of the dependent variable to be model results generally are within measurement calculated by the model. To fulfill this error. The worst model error occurs in silt objective, a database describing the lenses and is in excess of measurement error.

  4. Geostatistical estimation of signal-to-noise ratios for spectral vegetation indices

    USGS Publications Warehouse

    Ji, Lei; Zhang, Li; Rover, Jennifer R.; Wylie, Bruce K.; Chen, Xuexia

    2014-01-01

    In the past 40 years, many spectral vegetation indices have been developed to quantify vegetation biophysical parameters. An ideal vegetation index should contain the maximum level of signal related to specific biophysical characteristics and the minimum level of noise such as background soil influences and atmospheric effects. However, accurate quantification of signal and noise in a vegetation index remains a challenge, because it requires a large number of field measurements or laboratory experiments. In this study, we applied a geostatistical method to estimate signal-to-noise ratio (S/N) for spectral vegetation indices. Based on the sample semivariogram of vegetation index images, we used the standardized noise to quantify the noise component of vegetation indices. In a case study in the grasslands and shrublands of the western United States, we demonstrated the geostatistical method for evaluating S/N for a series of soil-adjusted vegetation indices derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The soil-adjusted vegetation indices were found to have higher S/N values than the traditional normalized difference vegetation index (NDVI) and simple ratio (SR) in the sparsely vegetated areas. This study shows that the proposed geostatistical analysis can constitute an efficient technique for estimating signal and noise components in vegetation indices.

  5. Geostatistical estimation of signal-to-noise ratios for spectral vegetation indices

    NASA Astrophysics Data System (ADS)

    Ji, Lei; Zhang, Li; Rover, Jennifer; Wylie, Bruce K.; Chen, Xuexia

    2014-10-01

    In the past 40 years, many spectral vegetation indices have been developed to quantify vegetation biophysical parameters. An ideal vegetation index should contain the maximum level of signal related to specific biophysical characteristics and the minimum level of noise such as background soil influences and atmospheric effects. However, accurate quantification of signal and noise in a vegetation index remains a challenge, because it requires a large number of field measurements or laboratory experiments. In this study, we applied a geostatistical method to estimate signal-to-noise ratio (S/N) for spectral vegetation indices. Based on the sample semivariogram of vegetation index images, we used the standardized noise to quantify the noise component of vegetation indices. In a case study in the grasslands and shrublands of the western United States, we demonstrated the geostatistical method for evaluating S/N for a series of soil-adjusted vegetation indices derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The soil-adjusted vegetation indices were found to have higher S/N values than the traditional normalized difference vegetation index (NDVI) and simple ratio (SR) in the sparsely vegetated areas. This study shows that the proposed geostatistical analysis can constitute an efficient technique for estimating signal and noise components in vegetation indices.

  6. A conceptual sedimentological-geostatistical model of aquifer heterogeneity based on outcrop studies

    SciTech Connect

    Davis, J.M.

    1994-01-01

    Three outcrop studies were conducted in deposits of different depositional environments. At each site, permeability measurements were obtained with an air-minipermeameter developed as part of this study. In addition, the geological units were mapped with either surveying, photographs, or both. Geostatistical analysis of the permeability data was performed to estimate the characteristics of the probability distribution function and the spatial correlation structure. The information obtained from the geological mapping was then compared with the results of the geostatistical analysis for any relationships that may exist. The main field site was located in the Albuquerque Basin of central New Mexico at an outcrop of the Pliocene-Pleistocene Sierra Ladrones Formation. The second study was conducted on the walls of waste pits in alluvial fan deposits at the Nevada Test Site. The third study was conducted on an outcrop of an eolian deposit (miocene) south of Socorro, New Mexico. The results of the three studies were then used to construct a conceptual model relating depositional environment to geostatistical models of heterogeneity. The model presented is largely qualitative but provides a basis for further hypothesis formulation and testing.

  7. Assessment of nitrate pollution in the Grand Morin aquifers (France): combined use of geostatistics and physically based modeling.

    PubMed

    Flipo, Nicolas; Jeannée, Nicolas; Poulin, Michel; Even, Stéphanie; Ledoux, Emmanuel

    2007-03-01

    The objective of this work is to combine several approaches to better understand nitrate fate in the Grand Morin aquifers (2700 km(2)), part of the Seine basin. cawaqs results from the coupling of the hydrogeological model newsam with the hydrodynamic and biogeochemical model of river ProSe. cawaqs is coupled with the agronomic model Stics in order to simulate nitrate migration in basins. First, kriging provides a satisfactory representation of aquifer nitrate contamination from local observations, to set initial conditions for the physically based model. Then associated confidence intervals, derived from data using geostatistics, are used to validate cawaqs results. Results and evaluation obtained from the combination of these approaches are given (period 1977-1988). Then cawaqs is used to simulate nitrate fate for a 20-year period (1977-1996). The mean nitrate concentrations increase in aquifers is 0.09 mgN L(-1)yr(-1), resulting from an average infiltration flux of 3500 kgN.km(-2)yr(-1).

  8. Geostatistics and Geographic Information Systems to Study the Spatial Distribution of Grapholita molesta (Busck) (Lepidoptera: Tortricidae) in Peach Fields.

    PubMed

    Duarte, F; Calvo, M V; Borges, A; Scatoni, I B

    2015-08-01

    The oriental fruit moth, Grapholita molesta (Busck), is the most serious pest in peach, and several insecticide applications are required to reduce crop damage to acceptable levels. Geostatistics and Geographic Information Systems (GIS) are employed to measure the range of spatial correlation of G. molesta in order to define the optimum sampling distance for performing spatial analysis and to determine the current distribution of the pest in peach orchards of southern Uruguay. From 2007 to 2010, 135 pheromone traps per season were installed and georeferenced in peach orchards distributed over 50,000 ha. Male adult captures were recorded weekly from September to April. Structural analysis of the captures was performed, yielding 14 semivariograms for the accumulated captures analyzed by generation and growing season. Two sets of maps were constructed to describe the pest distribution. Nine significant models were obtained in the 14 evaluated periods. The range estimated for the correlation was from 908 to 6884 m. Three hot spots of high population level and some areas with comparatively low populations were constant over the 3-year period, while there is a greater variation in the size of the population in different generations and years in other areas.

  9. Evaluating shrub-associated spatial patterns of soil properties in a shrub-steppe ecosystem using multiple-variable geostatistics

    SciTech Connect

    Halvorson, J.J.; Smith, J.L. |; Bolton, H. Jr.; Rossi, R.E.

    1995-09-01

    Geostatistics are often calculated for a single variable at a time, even though many natural phenomena are functions of several variables. The objective of this work was to demonstrate a nonparametric approach for assessing the spatial characteristics of multiple-variable phenomena. Specifically, we analyzed the spatial characteristics of resource islands in the soil under big sagebrush (Artemisia tridentala Nutt.), a dominant shrub in the intermountain western USA. For our example, we defined resource islands as a function of six soil variables representing concentrations of soil resources, populations of microorganisms, and soil microbial physiological variables. By collectively evaluating the indicator transformations of these individual variables, we created a new data set, termed a multiple-variable indicator transform or MVIT. Alternate MVITs were obtained by varying the selection criteria. Each MVIT was analyzed with variography to characterize spatial continuity, and with indicator kriging to predict the combined probability of their occurrence at unsampled locations in the landscape. Simple graphical analysis and variography demonstrated spatial dependence for all individual soil variables. Maps derived from ordinary kriging of MVITs suggested that the combined probabilities for encountering zones of above-median resources were greatest near big sagebrush. 51 refs., 5 figs., 1 tab.

  10. Groundwater levels time series sensitivity to pluviometry and air temperature: a geostatistical approach to Sfax region, Tunisia.

    PubMed

    Triki, Ibtissem; Trabelsi, Nadia; Hentati, Imen; Zairi, Moncef

    2014-03-01

    In this paper, the pattern of groundwater level fluctuations is investigated by statistical techniques for 24 monitoring wells located in an unconfined coastal aquifer in Sfax (Tunisia) for a time period from 1997 to 2006. Firstly, a geostatistical study is performed to characterize the temporal behaviors of data sets in terms of variograms and to make predictions about the value of the groundwater level at unsampled times. Secondly, multivariate statistical methods, i.e., principal component analysis (PCA) and cluster analysis (CA) of time series of groundwater levels are used to classify groundwater hydrographs regard to identical fluctuation pattern. Three groundwater groups (A, B, and C) were identified. In group "A," water level decreases continuously throughout the study periods with rapid annual cyclic variation, whereas in group "B," the water level contains much less high-frequency variation. The wells of group "C" represents a steady and gradual increase of groundwater levels caused by the aquifer artificial recharge. Furthermore, a cross-correlation analysis is used to investigate the aquifer response to local rainfall and temperature records. The result revealed that the temperature is more affecting the variation of the groundwater level of group A wells than the rainfall. However, the second and the third groups are less affected by rainfall or temperature.

  11. Short-term effects of goal-setting focusing on the life goal concept on subjective well-being and treatment engagement in subacute inpatients: a quasi-randomized controlled trial

    PubMed Central

    Ogawa, Tatsuya; Omon, Kyohei; Yuda, Tomohisa; Ishigaki, Tomoya; Imai, Ryota; Ohmatsu, Satoko; Morioka, Shu

    2016-01-01

    Objective: To investigate the short-term effects of the life goal concept on subjective well-being and treatment engagement, and to determine the sample size required for a larger trial. Design: A quasi-randomized controlled trial that was not blinded. Setting: A subacute rehabilitation ward. Subjects: A total of 66 patients were randomized to a goal-setting intervention group with the life goal concept (Life Goal), a standard rehabilitation group with no goal-setting intervention (Control 1), or a goal-setting intervention group without the life goal concept (Control 2). Interventions: The goal-setting intervention in the Life Goal and Control 2 was Goal Attainment Scaling. The Life Goal patients were assessed in terms of their life goals, and the hierarchy of goals was explained. The intervention duration was four weeks. Main measures: Patients were assessed pre- and post-intervention. The outcome measures were the Hospital Anxiety and Depression Scale, 12-item General Health Questionnaire, Pittsburgh Rehabilitation Participation Scale, and Functional Independence Measure. Results: Of the 296 potential participants, 66 were enrolled; Life Goal (n = 22), Control 1 (n = 22) and Control 2 (n = 22). Anxiety was significantly lower in the Life Goal (4.1 ±3.0) than in Control 1 (6.7 ±3.4), but treatment engagement was significantly higher in the Life Goal (5.3 ±0.4) compared with both the Control 1 (4.8 ±0.6) and Control 2 (4.9 ±0.5). Conclusions: The life goal concept had a short-term effect on treatment engagement. A sample of 31 patients per group would be required for a fully powered clinical trial. PMID:27496700

  12. A Bayesian geostatistical approach for evaluating the uncertainty of contaminant mass discharges from point sources

    NASA Astrophysics Data System (ADS)

    Troldborg, M.; Nowak, W.; Binning, P. J.; Bjerg, P. L.

    2012-12-01

    Estimates of mass discharge (mass/time) are increasingly being used when assessing risks of groundwater contamination and designing remedial systems at contaminated sites. Mass discharge estimates are, however, prone to rather large uncertainties as they integrate uncertain spatial distributions of both concentration and groundwater flow velocities. For risk assessments or any other decisions that are being based on mass discharge estimates, it is essential to address these uncertainties. We present a novel Bayesian geostatistical approach for quantifying the uncertainty of the mass discharge across a multilevel control plane. The method decouples the flow and transport simulation and has the advantage of avoiding the heavy computational burden of three-dimensional numerical flow and transport simulation coupled with geostatistical inversion. It may therefore be of practical relevance to practitioners compared to existing methods that are either too simple or computationally demanding. The method is based on conditional geostatistical simulation and accounts for i) heterogeneity of both the flow field and the concentration distribution through Bayesian geostatistics (including the uncertainty in covariance functions), ii) measurement uncertainty, and iii) uncertain source zone geometry and transport parameters. The method generates multiple equally likely realizations of the spatial flow and concentration distribution, which all honour the measured data at the control plane. The flow realizations are generated by analytical co-simulation of the hydraulic conductivity and the hydraulic gradient across the control plane. These realizations are made consistent with measurements of both hydraulic conductivity and head at the site. An analytical macro-dispersive transport solution is employed to simulate the mean concentration distribution across the control plane, and a geostatistical model of the Box-Cox transformed concentration data is used to simulate observed

  13. The PRESLO study: evaluation of a global secondary low back pain prevention program for health care personnel in a hospital setting. Multicenter, randomized intervention trial

    PubMed Central

    2012-01-01

    Background Common low back pain represents a major public health problem in terms of its direct cost to health care and its socio-economic repercussions. Ten percent of individuals who suffer from low back pain evolve toward a chronic case and as such are responsible for 75 to 80% of the direct cost of low back pain. It is therefore imperative to highlight the predictive factors of low back pain chronification in order to lighten the economic burden of low back pain-related invalidity. Despite being particularly affected by low back pain, Hospices Civils de Lyon (HCL) personnel have never been offered a specific, tailor-made treatment plan. The PRESLO study (with PRESLO referring to Secondary Low Back Pain Prevention, or in French, PREvention Secondaire de la LOmbalgie), proposed by HCL occupational health services and the Centre Médico-Chirurgical et de Réadaptation des Massues – Croix Rouge Française, is a randomized trial that aims to evaluate the feasibility and efficiency of a global secondary low back pain prevention program for the low back pain sufferers among HCL hospital personnel, a population at risk for recurrence and chronification. This program, which is based on the concept of physical retraining, employs a multidisciplinary approach uniting physical activity, cognitive education about low back pain and lumbopelvic morphotype analysis. No study targeting populations at risk for low back pain chronification has as yet evaluated the efficiency of lighter secondary prevention programs. Methods/Design This study is a two-arm parallel randomized controlled trial proposed to all low back pain sufferers among HCL workers, included between October 2008 and July 2011 and followed over two years. The personnel following their usual treatment (control group) and those following the global prevention program in addition to their usual treatment (intervention group) are compared in terms of low back pain recurrence and the impairments measured at the

  14. Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: a case study in an agricultural setting (Southern Spain).

    PubMed

    Rodriguez-Galiano, Victor; Mendes, Maria Paula; Garcia-Soldado, Maria Jose; Chica-Olmo, Mario; Ribeiro, Luis

    2014-04-01

    Watershed management decisions need robust methods, which allow an accurate predictive modeling of pollutant occurrences. Random Forest (RF) is a powerful machine learning data driven method that is rarely used in water resources studies, and thus has not been evaluated thoroughly in this field, when compared to more conventional pattern recognition techniques key advantages of RF include: its non-parametric nature; high predictive accuracy; and capability to determine variable importance. This last characteristic can be used to better understand the individual role and the combined effect of explanatory variables in both protecting and exposing groundwater from and to a pollutant. In this paper, the performance of the RF regression for predictive modeling of nitrate pollution is explored, based on intrinsic and specific vulnerability assessment of the Vega de Granada aquifer. The applicability of this new machine learning technique is demonstrated in an agriculture-dominated area where nitrate concentrations in groundwater can exceed the trigger value of 50 mg/L, at many locations. A comprehensive GIS database of twenty-four parameters related to intrinsic hydrogeologic proprieties, driving forces, remotely sensed variables and physical-chemical variables measured in "situ", were used as inputs to build different predictive models of nitrate pollution. RF measures of importance were also used to define the most significant predictors of nitrate pollution in groundwater, allowing the establishment of the pollution sources (pressures). The potential of RF for generating a vulnerability map to nitrate pollution is assessed considering multiple criteria related to variations in the algorithm parameters and the accuracy of the maps. The performance of the RF is also evaluated in comparison to the logistic regression (LR) method using different efficiency measures to ensure their generalization ability. Prediction results show the ability of RF to build accurate models

  15. Incorporating tailored interactive patient solutions using interactive voice response technology to improve statin adherence: results of a randomized clinical trial in a managed care setting.

    PubMed

    Stacy, Jane N; Schwartz, Steven M; Ershoff, Daniel; Shreve, Marilyn Standifer

    2009-10-01

    The current study presents the impact of a behavior change program to increase statin adherence using interactive voice response (IVR) technology. Subjects were affiliated with a large health benefit company, were prescribed a statin (index) and had no lipid-lowering pharmacy claims in the previous 6 months, and were continuously enrolled in the plan for 12 months prior and 6 months post index statin. Potential subjects (1219) were contacted by the IVR system; 497 gave informed consent. Subjects were asked to respond to 15 questions from the IVR that were guided by several behavior change theories. At the conclusion of the questions, subjects were randomly assigned to either a control group (n = 244), who received generic feedback at the conclusion of the call and were then mailed a generic cholesterol guide, or an experimental group (n = 253), who received tailored feedback based on their cholesterol-related knowledge, attitudes, beliefs, and perceived barriers to medication adherence, and were mailed a tailored guide that reinforced similar themes. Subjects in the experimental group had the opportunity to participate in 2 additional tailored IVR support calls. The primary dependent variable was 6-month point prevalence, defined as claims evidence of a statin on days 121-180 post index statin. Subjects in the experimental group had a significantly higher 6-month point prevalence than the controls (70.4% vs. 60.7%, P < 0.05). Results of this study suggest that a behavioral support program using IVR technology can be a cost-effective modality to address the important public health problem of patient nonadherence with statin medication.

  16. Promoting Early, Safe Return to Work in Injured Employees: A Randomized Trial of a Supervisor Training Intervention in a Healthcare Setting.

    PubMed

    Spector, June T; Reul, Nicholas K

    2017-03-01

    Purpose Supervisors in the healthcare sector have the potential to contribute to disability prevention in injured employees. Published data on the evaluation of return to work (RTW) interventions aimed at direct supervisors are scarce. We sought to determine the effect of a brief audiovisual supervisor training module on supervisor RTW attitudes and knowledge. Methods A parallel-group study, using equal randomization, comparing the training module intervention to usual practice in healthcare supervisors at a quaternary care hospital was conducted. Differences between groups in changes in RTW attitude and knowledge survey question scores between baseline and 3 months were assessed using the Mann-Whitney U test. The Benjamini-Hochberg-Yekutieli procedure was used to control for false discovery rate and generate adjusted p values. Results Forty supervisors were allocated to the intervention group and 41 to the usual practice group. Attitude and knowledge scores for most questions improved between baseline and immediately after intervention administration. Comparing intervention (n = 33) and usual practice groups (n = 37), there was a trend toward greater increase between baseline and 3 months follow-up in agreement that the supervisor can manage the RTW process (U = 515, adjusted p value = 0.074) and in confidence that the supervisor can answer employees' questions (U = 514, adjusted p value = 0.074) in the intervention group, although these findings were not statistically significant. Conclusions The training intervention may have provided the initial tools for supervisors to navigate the RTW process in collaboration with others in the RTW community of practice. A larger study with longer follow-up is needed to confirm results.

  17. Project Enhance: A Randomized Controlled Trial of an Individualized HIV Prevention Intervention for HIV-Infected Men Who Have Sex With Men Conducted in a Primary Care Setting

    PubMed Central

    Safren, Steven A.; O’Cleirigh, Conall M.; Skeer, Margie; Elsesser, Steven A.; Mayer, Kenneth H.

    2013-01-01

    Objective Men who have sex with men (MSM) are the largest group of individuals in the U.S. living with HIV and have the greatest number of new infections. This study was designed to test a brief, culturally relevant prevention intervention for HIV-infected MSM, which could be integrated into HIV care. Method HIV-infected MSM who received HIV care in a community health center (N = 201), and who reported HIV sexual transmission-risk behavior (TRB) in the prior 6 months, were randomized to receive the intervention or treatment as usual. The intervention, provided by a medical social worker, included proactive case management for psychosocial problems, counseling about living with HIV, and HIV TRB risk reduction. Participants were followed every 3 months for one year. Results Participants, regardless of study condition, reported reductions in HIV TRB, with no significant differential effect by condition in primary intent-to-treat analyses. When examining moderators, the intervention was differentially effective in reducing HIV TRB for those who screened in for baseline depression, but this was not the case for those who did not screen in for depression. Conclusions The similar level of reduction in HIV TRB in the intervention and control groups, consistent with other recent secondary prevention interventions, speaks to the need for new, creative designs, or more potent interventions in secondary HIV prevention trials, as the control group seemed to benefit from risk assessment, study contact, and referrals provided by study staff. The differential finding for those with depression may suggest that those without depression could reap benefits from limited interventions, but those with a comorbid psychiatric diagnosis may require additional interventions to modify their sexual risk behaviors. PMID:22746262

  18. [Spatial variability of soil nutrients based on geostatistics combined with GIS--a case study in Zunghua City of Hebei Province].

    PubMed

    Guo, X; Fu, B; Ma, K; Chen, L

    2000-08-01

    Geostatistics combined with GIS was applied to analyze the spatial variability of soil nutrients in topsoil (0-20 cm) in Zunghua City of Hebei Province. GIS can integrate attribute data with geographical data of system variables, which makes the application of geostatistics technique for large spatial scale more convenient. Soil nutrient data in this study included available N (alkaline hydrolyzing nitrogen), total N, available K, available P and organic matter. The results showed that the semivariograms of soil nutrients were best described by spherical model, except for that of available K, which was best fitted by complex structure of exponential model and linear with sill model. The spatial variability of available K was mainly produced by structural factor, while that of available N, total N, available P and organic matter was primarily caused by random factor. However, their spatial heterogeneity degree was different: the degree of total N and organic matter was higher, and that of available P and available N was lower. The results also indicated that the spatial correlation of the five tested soil nutrients at this large scale was moderately dependent. The ranges of available N and available P were almost same, which were 5 km and 5.5 km, respectively. The range of total N was up to 18 km, and that of organic matter was 8.5 km. For available K, the spatial variability scale primarily expressed exponential model between 0-3.5 km, but linear with sill model between 3.5-25.5 km. In addition, five soil nutrients exhibited different isotropic ranges. Available N and available P were isotropic through the whole research range (0-28 km). The isotropic range of available K was 0-8 km, and that of total N and organic matter was 0-10 km.

  19. Analysis of the spatio-temporal distribution of Eurygaster integriceps (Hemiptera: Scutelleridae) by using spatial analysis by distance indices and geostatistics.

    PubMed

    Karimzadeh, R; Hejazi, M J; Helali, H; Iranipour, S; Mohammadi, S A

    2011-10-01

    Eurygaster integriceps Puton (Hemiptera: Scutelleridae) is the most serious insect pest of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) in Iran. In this study, spatio-temporal distribution of this pest was determined in wheat by using spatial analysis by distance indices (SADIE) and geostatistics. Global positioning and geographic information systems were used for spatial sampling and mapping the distribution of this insect. The study was conducted for three growing seasons in Gharamalek, an agricultural region to the west of Tabriz, Iran. Weekly sampling began when E. integriceps adults migrated to wheat fields from overwintering sites and ended when the new generation adults appeared at the end of season. The adults were sampled using 1- by 1-m quadrat and distance-walk methods. A sweep net was used for sampling the nymphs, and five 180° sweeps were considered as the sampling unit. The results of spatial analyses by using geostatistics and SADIE indicated that E. integriceps adults were clumped after migration to fields and had significant spatial dependency. The second- and third-instar nymphs showed aggregated spatial structure in the middle of growing season. At the end of the season, population distribution changed toward random or regular patterns; and fourth and fifth instars had weaker spatial structure compared with younger nymphs. In Iran, management measures for E. integriceps in wheat fields are mainly applied against overwintering adults, as well as second and third instars. Because of the aggregated distribution of these life stages, site-specific spraying of chemicals is feasible in managing E. integriceps.

  20. Performance of a Culturally Tailored Cognitive Behavioral Intervention (CBI) Integrated in a Public Health Setting to Reduce Risk of Antepartum Depression: A Randomized Clinical Trial

    PubMed Central

    Jesse, D. Elizabeth; Gaynes, Bradley N.; Feldhousen, Elizabeth; Newton, Edward R.; Bunch, Shelia; Hollon, Steven D.

    2016-01-01

    Introduction Cognitive behavioral group interventions have been shown to improve depressive symptoms in adult populations. This article details the feasibility and efficacy of a 6-week culturally tailored cognitive behavioral intervention offered to rural, minority, low-income women at risk for antepartum depression. Methods 146 pregnant women were stratified by high-risk for antepartum depression (Edinburgh Postnatal Depression Scale (EPDS) score of 10 or higher) or low-moderate risk (EPDS score of 4-9) and randomized to a cognitive behavioral intervention or treatment-as-usual. Differences in mean change of EPDS and BDI-II scores for low-moderate and high-risk women in the cognitive behavioral intervention and treatment-as-usual for the full sample were assessed from baseline (T1), post-treatment (T2) and 1-month follow-up (T3) and for African-American women in the subsample. Results Both the cognitive behavioral intervention and treatment-as-usual groups had significant reductions in the EPDS scores from T1 to T2 and T1 to T3. In women at high-risk for depression (n=62), there was no significant treatment effect from T1 to T2 or T3 for the Edinburgh Postnatal Depression Scale. However, in low-moderate risk women, there was a significant decrease in the BDI-II scores from T1 to T2 (4.92 vs. 0.59, P=.018) and T1 to T3 (5.67 vs. 1.51, P=.04). Also, the cognitive behavioral intervention significantly reduced EPDS scores for African-American women at high-risk (n=43) from T1 to T2 (5.59 vs. 2.18, P=.02) and from T1 to T3 (6.32 vs. 3.14, P= .04). Discussion A cognitive behavioral intervention integrated within prenatal clinics is feasible in this sample, although attrition rates were high. Compared to treatment-as-usual, the cognitive behavioral intervention reduced depressive symptoms for African-American women at high-risk for antepartum depression and for the full sample of women at low-moderate risk for antepartum depression. These promising findings need to be

  1. Efficacy and Safety of Three Antiretroviral Regimens for Initial Treatment of HIV-1: A Randomized Clinical Trial in Diverse Multinational Settings

    PubMed Central

    Campbell, Thomas B.; Smeaton, Laura M.; Kumarasamy, N.; Flanigan, Timothy; Klingman, Karin L.; Firnhaber, Cynthia; Grinsztejn, Beatriz; Hosseinipour, Mina C.; Kumwenda, Johnstone; Lalloo, Umesh; Riviere, Cynthia; Sanchez, Jorge; Melo, Marineide; Supparatpinyo, Khuanchai; Tripathy, Srikanth; Martinez, Ana I.; Nair, Apsara; Walawander, Ann; Moran, Laura; Chen, Yun; Snowden, Wendy; Rooney, James F.; Uy, Jonathan; Schooley, Robert T.; De Gruttola, Victor; Hakim, James Gita; Swann, Edith; Barnett, Ronald L.; Brizz, Barbara; Delph, Yvette; Gettinger, Nikki; Mitsuyasu, Ronald T.; Eshleman, Susan; Safren, Steven; Fiscus, Susan A.; Andrade, Adriana; Haas, David W.; Amod, Farida; Berthaud, Vladimir; Bollinger, Robert C.; Bryson, Yvonne; Celentano, David; Chilongozi, David; Cohen, Myron; Collier, Ann C.; Currier, Judith Silverstein; Cu-Uvin, Susan; Eron, Joseph; Flexner, Charles; Gallant, Joel E.; Gulick, Roy M.; Hammer, Scott M.; Hoffman, Irving; Kazembe, Peter; Kumwenda, Newton; Lama, Javier R.; Lawrence, Jody; Maponga, Chiedza; Martinson, Francis; Mayer, Kenneth; Nielsen, Karin; Pendame, Richard B.; Ramratnam, Bharat; Sanne, Ian; Severe, Patrice; Sirisanthana, Thira; Solomon, Suniti; Tabet, Steve; Taha, Taha; van der Horst, Charles; Wanke, Christine; Gormley, Joan; Marcus, Cheryl J.; Putnam, Beverly; Loeliger, Edde; Pappa, Keith A.; Webb, Nancy; Shugarts, David L.; Winters, Mark A.; Descallar, Renard S.; Steele, Joseph; Wulfsohn, Michael; Said, Farideh; Chen, Yue; Martin, John C; Bischofberger, Norbert; Cheng, Andrew; Jaffe, Howard; Sharma, Jabin; Poongulali, S.; Cardoso, Sandra Wagner; Faria, Deise Lucia; Berendes, Sima; Burke, Kelly; Mngqibisa, Rosie; Kanyama, Cecelia; Kayoyo, Virginia; Samaneka, Wadzanai P.; Chisada, Anthony; Faesen, Sharla; Chariyalertsak, Suwat; Santos, Breno; Lira, Rita Alves; Joglekar, Anjali A.; Rosa, Alberto La; Infante, Rosa; Jain, Mamta; Petersen, Tianna; Godbole, Sheela; Dhayarkar, Sampada; Feinberg, Judith; Baer, Jenifer; Pollard, Richard B.; Asmuth, David; Gangakhedkar, Raman R; Gaikwad, Asmita; Ray, M. Graham; Basler, Cathi; Para, Michael F.; Watson, Kathy J.; Taiwo, Babafemi; McGregor, Donna; Balfour, Henry H.; Mullan, Beth; Kim, Ge-Youl; Klebert, Michael K.; Cox, Gary Matthew; Silberman, Martha; Mildvan, Donna; Revuelta, Manuel; Tashima, Karen T.; Patterson, Helen; Geiseler, P. Jan; Santos, Bartolo; Daar, Eric S; Lopez, Ruben; Frarey, Laurie; Currin, David; Haas, David H.; Bailey, Vicki L.; Tebas, Pablo; Zifchak, Larisa; Noel-Connor, Jolene; Torres, Madeline; Sha, Beverly E.; Fritsche, Janice M.; Cespedes, Michelle; Forcht, Janet; O'Brien, William A.; Mogridge, Cheryl; Hurley, Christine; Corales, Roberto; Palmer, Maria; Adams, Mary; Luque, Amneris; Lopez-Detres, Luis; Stroberg, Todd

    2012-01-01

    Background Antiretroviral regimens with simplified dosing and better safety are needed to maximize the efficiency of antiretroviral delivery in resource-limited settings. We investigated the efficacy and safety of antiretroviral regimens with once-daily compared to twice-daily dosing in diverse areas of the world. Methods and Findings 1,571 HIV-1-infected persons (47% women) from nine countries in four continents were assigned with equal probability to open-label antiretroviral therapy with efavirenz plus lamivudine-zidovudine (EFV+3TC-ZDV), atazanavir plus didanosine-EC plus emtricitabine (ATV+DDI+FTC), or efavirenz plus emtricitabine-tenofovir-disoproxil fumarate (DF) (EFV+FTC-TDF). ATV+DDI+FTC and EFV+FTC-TDF were hypothesized to be non-inferior to EFV+3TC-ZDV if the upper one-sided 95% confidence bound for the hazard ratio (HR) was ≤1.35 when 30% of participants had treatment failure. An independent monitoring board recommended stopping study follow-up prior to accumulation of 472 treatment failures. Comparing EFV+FTC-TDF to EFV+3TC-ZDV, during a median 184 wk of follow-up there were 95 treatment failures (18%) among 526 participants versus 98 failures among 519 participants (19%; HR 0.95, 95% CI 0.72–1.27; p = 0.74). Safety endpoints occurred in 243 (46%) participants assigned to EFV+FTC-TDF versus 313 (60%) assigned to EFV+3TC-ZDV (HR 0.64, CI 0.54–0.76; p<0.001) and there was a significant interaction between sex and regimen safety (HR 0.50, CI 0.39–0.64 for women; HR 0.79, CI 0.62–1.00 for men; p = 0.01). Comparing ATV+DDI+FTC to EFV+3TC-ZDV, during a median follow-up of 81 wk there were 108 failures (21%) among 526 participants assigned to ATV+DDI+FTC and 76 (15%) among 519 participants assigned to EFV+3TC-ZDV (HR 1.51, CI 1.12–2.04; p = 0.007). Conclusion EFV+FTC-TDF had similar high efficacy compared to EFV+3TC-ZDV in this trial population, recruited in diverse multinational settings. Superior safety, especially in HIV-1-infected

  2. Family, Community and Clinic Collaboration to Treat Overweight and Obese Children: Stanford GOALS -- a Randomized Controlled Trial of a Three-Year, Multi-Component, Multi-Level, Multi-Setting Intervention

    PubMed Central

    Robinson, Thomas N.; Matheson, Donna; Desai, Manisha; Wilson, Darrell M.; Weintraub, Dana L.; Haskell, William L.; McClain, Arianna; McClure, Samuel; Banda, Jorge; Sanders, Lee M.; Haydel, K. Farish; Killen, Joel D.

    2013-01-01

    Objective To test the effects of a three-year, community-based, multi-component, multi-level, multi-setting (MMM) approach for treating overweight and obese children. Design Two-arm, parallel group, randomized controlled trial with measures at baseline, 12, 24, and 36 months after randomization. Participants Seven through eleven year old, overweight and obese children (BMI ≥ 85th percentile) and their parents/caregivers recruited from community locations in low-income, primarily Latino neighborhoods in Northern California. Interventions Families are randomized to the MMM intervention versus a community health education active-placebo comparison intervention. Interventions last for three years for each participant. The MMM intervention includes a community-based after school team sports program designed specifically for overweight and obese children, a home-based family intervention to reduce screen time, alter the home food/eating environment, and promote self-regulatory skills for eating and activity behavior change, and a primary care behavioral counseling intervention linked to the community and home interventions. The active-placebo comparison intervention includes semi-annual health education home visits, monthly health education newsletters for children and for parents/guardians, and a series of community-based health education events for families. Main Outcome Measure Body mass index trajectory over the three-year study. Secondary outcome measures include waist circumference, triceps skinfold thickness, accelerometer-measured physical activity, 24-hour dietary recalls, screen time and other sedentary behaviors, blood pressure, fasting lipids, glucose, insulin, hemoglobin A1c, C-reactive protein, alanine aminotransferase, and psychosocial measures. Conclusions The Stanford GOALS trial is testing the efficacy of a novel community-based multi-component, multi-level, multi-setting treatment for childhood overweight and obesity in low-income, Latino families

  3. Acceptability of Home-Assessment Post Medical Abortion and Medical Abortion in a Low-Resource Setting in Rajasthan, India. Secondary Outcome Analysis of a Non-Inferiority Randomized Controlled Trial

    PubMed Central

    Paul, Mandira; Iyengar, Kirti; Essén, Birgitta; Gemzell-Danielsson, Kristina; Iyengar, Sharad D.; Bring, Johan; Soni, Sunita; Klingberg-Allvin, Marie

    2015-01-01

    Background Studies evaluating acceptability of simplified follow-up after medical abortion have focused on high-resource or urban settings where telephones, road connections, and modes of transport are available and where women have formal education. Objective To investigate women’s acceptability of home-assessment of abortion and whether acceptability of medical abortion differs by in-clinic or home-assessment of abortion outcome in a low-resource setting in India. Design Secondary outcome of a randomised, controlled, non-inferiority trial. Setting Outpatient primary health care clinics in rural and urban Rajasthan, India. Population Women were eligible if they sought abortion with a gestation up to 9 weeks, lived within defined study area and agreed to follow-up. Women were ineligible if they had known contraindications to medical abortion, haemoglobin < 85mg/l and were below 18 years. Methods Abortion outcome assessment through routine clinic follow-up by a doctor was compared with home-assessment using a low-sensitivity pregnancy test and a pictorial instruction sheet. A computerized random number generator generated the randomisation sequence (1:1) in blocks of six. Research assistants randomly allocated eligible women who opted for medical abortion (mifepristone and misoprostol), using opaque sealed envelopes. Blinding during outcome assessment was not possible. Main Outcome Measures Women’s acceptability of home-assessment was measured as future preference of follow-up. Overall satisfaction, expectations, and comparison with previous abortion experiences were compared between study groups. Results 731 women were randomized to the clinic follow-up group (n = 353) or home-assessment group (n = 378). 623 (85%) women were successfully followed up, of those 597 (96%) were satisfied and 592 (95%) found the abortion better or as expected, with no difference between study groups. The majority, 355 (57%) women, preferred home-assessment in the event of a future

  4. Improved hydrological model parametrization for climate change impact assessment under data scarcity - The potential of field monitoring techniques and geostatistics.

    PubMed

    Meyer, Swen; Blaschek, Michael; Duttmann, Rainer; Ludwig, Ralf

    2016-02-01

    According to current climate projections, Mediterranean countries are at high risk for an even pronounced susceptibility to changes in the hydrological budget and extremes. These changes are expected to have severe direct impacts on the management of water resources, agricultural productivity and drinking water supply. Current projections of future hydrological change, based on regional climate model results and subsequent hydrological modeling schemes, are very uncertain and poorly validated. The Rio Mannu di San Sperate Basin, located in Sardinia, Italy, is one test site of the CLIMB project. The Water Simulation Model (WaSiM) was set up to model current and future hydrological conditions. The availability of measured meteorological and hydrological data is poor as it is common for many Mediterranean catchments. In this study we conducted a soil sampling campaign in the Rio Mannu catchment. We tested different deterministic and hybrid geostatistical interpolation methods on soil textures and tested the performance of the applied models. We calculated a new soil texture map based on the best prediction method. The soil model in WaSiM was set up with the improved new soil information. The simulation results were compared to standard soil parametrization. WaSiMs was validated with spatial evapotranspiration rates using the triangle method (Jiang and Islam, 1999). WaSiM was driven with the meteorological forcing taken from 4 different ENSEMBLES climate projections for a reference (1971-2000) and a future (2041-2070) times series. The climate change impact was assessed based on differences between reference and future time series. The simulated results show a reduction of all hydrological quantities in the future in the spring season. Furthermore simulation results reveal an earlier onset of dry conditions in the catchment. We show that a solid soil model setup based on short-term field measurements can improve long-term modeling results, which is especially important

  5. Geostatistical analysis of field hydraulic conductivity in compacted clay

    SciTech Connect

    Rogowski, A.S.; Simmons, D.E.

    1988-05-01

    Hydraulic conductivity (K) of fractured or porous materials is associated intimately with water flow and chemical transport. Basic concepts imply uniform flux through a homogeneous cross-sectional area. If flow were to occur only through part of the area, actual rates could be considerably different. Because laboratory values of K in compacted clays seldom agree with field estimates, questions arise as to what the true values of K are and how they should be estimated. Hydraulic conductivity values were measured on a 10 x 25 m elevated bridge-like platform. A constant water level was maintained for 1 yr over a 0.3-m thick layer of compacted clay, and inflow and outflow rates were monitored using 10 x 25 grids of 0.3-m diameter infiltration rings and outflow drains subtending approximately 1 x 1 m blocks of compacted clay. Variography of inflow and outflow data established relationships between cores and blocks of clay, respectively. Because distributions of outflow rates were much less and bore little resemblance to the distributions of break-through rates based on tracer studies, presence of macropores and preferential flow through the macropores was suspected. Subsequently, probability kriging was applied to reevaluate distribution of flux rates and possible location of macropores. Sites exceeding a threshold outflow of 100 x 10/sup -9/ m/s were classified as outliers and were assumed to probably contain a significant population of macropores. Different sampling schemes were examined. Variogram analysis of outflows with and without outliers suggested adequacy of sampling the site at 50 randomly chosen locations. Because of the potential contribution of macropores to pollutant transport and the practical necessity of extrapolating small plot values to larger areas, conditional simulations with and without outliers were carried out.

  6. Fractional randomness

    NASA Astrophysics Data System (ADS)

    Tapiero, Charles S.; Vallois, Pierre

    2016-11-01

    The premise of this paper is that a fractional probability distribution is based on fractional operators and the fractional (Hurst) index used that alters the classical setting of random variables. For example, a random variable defined by its density function might not have a fractional density function defined in its conventional sense. Practically, it implies that a distribution's granularity defined by a fractional kernel may have properties that differ due to the fractional index used and the fractional calculus applied to define it. The purpose of this paper is to consider an application of fractional calculus to define the fractional density function of a random variable. In addition, we provide and prove a number of results, defining the functional forms of these distributions as well as their existence. In particular, we define fractional probability distributions for increasing and decreasing functions that are right continuous. Examples are used to motivate the usefulness of a statistical approach to fractional calculus and its application to economic and financial problems. In conclusion, this paper is a preliminary attempt to construct statistical fractional models. Due to the breadth and the extent of such problems, this paper may be considered as an initial attempt to do so.

  7. Overview and technical and practical aspects for use of geostatistics in hazardous-, toxic-, and radioactive-waste-site investigations

    SciTech Connect

    Bossong, C.R.; Karlinger, M.R.; Troutman, B.M.; Vecchia, A.V.

    1999-10-01

    Technical and practical aspects of applying geostatistics are developed for individuals involved in investigation at hazardous-, toxic-, and radioactive-waste sites. Important geostatistical concepts, such as variograms and ordinary, universal, and indicator kriging, are described in general terms for introductory purposes and in more detail for practical applications. Variogram modeling using measured ground-water elevation data is described in detail to illustrate principles of stationarity, anisotropy, transformations, and cross validation. Several examples of kriging applications are described using ground-water-level elevations, bedrock elevations, and ground-water-quality data. A review of contemporary literature and selected public domain software associated with geostatistics also is provided, as is a discussion of alternative methods for spatial modeling, including inverse distance weighting, triangulation, splines, trend-surface analysis, and simulation.

  8. Insights into bedrock surface morphology using low-cost passive seismic surveys and integrated geostatistical analysis.

    PubMed

    Trevisani, S; Boaga, J; Agostini, L; Galgaro, A

    2017-02-01

    The HVSR (Horizontal to Vertical Spectral Ratio) technique is very popular in the context of seismic microzonation and for the mapping of shallow seismic reflectors, such as the sediment/bedrock transition surface. This easy-to-deploy single station passive seismic technique permits the collection of a considerable amount of HVSR data in a cost-effective way. It is not surprising that some recent studies have adopted single station micro-tremor analyses in order to retrieve information on geological structures in 1D, 2D or even 3D reconstructions. However, the interpolation approaches followed in these studies for extending the punctual HVSR data spatially are not supported by a detailed spatial statistical analysis. Conversely, in order to exploit the informative content and quantify the related uncertainty of HVSR data it is necessary to utilize a deep spatial statistical analysis and objective interpolation approaches. Moreover, the interpolation approach should make it possible to use expert knowledge and auxiliary information. Accordingly, we present an integrated geostatistical approach applied to HVSR data, collected for retrieving information on the morphology of a buried bedrock surface. The geostatistical study is conducted on an experimental dataset of 116 HVSR data collected in a small thermal basin located in the Venetian Plain (Caldiero Basin, N-E Italy). The explorative geostatistical analysis of the data coupled with the use of interpolation kriging techniques permit the extraction of relevant information on the resonance properties of the subsoil. The utilized approach, based on kriging with external drift (or its extension, i.e. regression kriging), permits the researcher to take into account auxiliary information, evaluate the related prediction uncertainty, and highlight abrupt variations in subsoil resonance frequencies. The results of the analysis are discussed, also with reflections pertaining to the geo-engineering and geo

  9. Characterizing Geothermal Surface Manifestation Based on Multivariate Geostatistics of Ground Measurements Data

    NASA Astrophysics Data System (ADS)

    Ishaq; Nur Heriawan, Mohamad; Saepuloh, Asep

    2016-09-01

    Mt. Wayang Windu is one of geothermal field located in West Java, Indonesia. The characterization of steam spots at surface manifestation zones based on the soil physical measurements of the area is presented in this study. The multivariate geostatistical methods incorporating the soil physical parameter data were used to characterize the zonation of geothermal surface manifestations. The purpose of this study is to evaluate the performance of spatial estimation method of multivariate geostatistics using Ordinary Cokriging (COK) to characterize the physical properties of geothermal surface manifestations at Mt. Wayang Windu. The COK method was selected because this method is favorable when the secondary variables has more number than the primary variables. There are four soil physical parameters used as the basis of COK method, i.e. Electrical Conductivity, Susceptibility, pH, and Temperature. The parameters were measured directly at and around geothermal surface manifestations including hot springs, fumaroles, and craters. Each location of surface manifestations was measured about 30 points with 30 x 30 m grids. The measurement results were analyzed by descriptive statistics to identify at the nature of data. The correlation among variables was analyzed using linear regression. When the correlation coefficient among variables is higher, the estimation results is expected to have better Linear Coregionalization Model (LCM). LCM was used to analyze the spatial correlation of each variable based on their variogram and cross-variogram model. In oder to evaluate the performance of multivariate geostatistical using COK method, a Root Mean Square Error (RMSE) was performed. Estimation result using COK method is well applicable for characterizing the surface physics parameters of radar images data.

  10. Long-term postseismic relaxation in southern California: Evidence from a geostatistical analysis of geodetic data

    NASA Astrophysics Data System (ADS)

    Potter, David Russell

    In this thesis I use a geostatistical approach to analyze geodetic data to quantify current rates of crustal deformation in southern California. The data consist of triangulation, trilateration and the 287 GPS derived velocity vectors of the Southern California Earthquake Center Velocity Map. The defining feature of the geostatistical technique I use is the observation-based differentiable covariance function. This characteristic allows transformation of the observed data into representations of the continuous two-dimensional velocity field and strain rate fields. The geostatistical analysis of geodetic data shows the differences between geologic and geodetic estimates of crustal deformation. The three main conclusions from this work are as follows. First, my results show that long-term postseismic relaxation of the upper ductile crust occurs after large (M > 6.75) earthquakes. The postseismic signatures, caused by large earthquakes rupturing the entire brittle crust, are especially clear for the 1952 Kern County and 1992 Landers earthquakes. The dilation rate and rotation rate plots reveal that in both events, the postseismic motions posses the same sense as the coseismic rupture, indicating continued motion long after the coseismic rupture ceases. Second, I show that the geologic slip rate estimates are too high by at least one standard deviation along much of the San Andreas fault in southern California. The residual (geodetic-geologic) deformation rate fields show high residual shearing rates along the SAF. However, the sense of motion is not consistent with postseismic effects following the 1857 Fort Tejon earthquake, but is consistent with an overestimate of the geologic slip rate. Third, I show that the WGCEP Phase II geologic models underestimate the amount of deformation occurring along the western portion of California, especially in the Ventura Basin and the Los Angeles basin.

  11. Conditioning geostatistical simulations of a bedrock fluvial aquifer using single well pumping tests

    NASA Astrophysics Data System (ADS)

    Niazi, A.; Bentley, L. R.; Hayashi, M.

    2015-12-01

    Geostatistical simulation is a powerful tool to explore the uncertainty associated with heterogeneity in groundwater and reservoir studies. Nonetheless, conditioning simulations merely with lithological information does not utilize all of the available information and so some workers additionally condition simulations with flow data. In this study, we introduce an approach to condition geostatistical simulations of the Paskapoo Formation, which is a paleo-fluvial system consisting of sandstone channels embedded in mudstone. The conditioning data consist of two-hour single well pumping tests extracted from the public water well database in Alberta, Canada. In this approach, lithologic models of an entire watershed are simulated and conditioned with hard lithological data using transition probability geostatistics (TPROGS). Then, a segment of the simulation around a pumping well was used to populate a flow model (FEFLOW) with either sand or mudstone. The values of the hydraulic conductivity and specific storage of sand and mudstone were then adjusted to minimize the difference between simulated and actual pumping test data using the parameter estimation program PEST. If the simulated data do not adequately match the measured data, the lithologic model is updated by locally deforming the lithology distribution using the probability perturbation method (PPM) and the model parameters are again updated with PEST. This procedure is repeated until the simulated and measured data agree within a pre-determined tolerance. The procedure is repeated for each pumping well that has pumping test data. The method constrains the lithological simulations and provides estimates of hydraulic conductivity and specific storage that are consistent with the pumping test data. Eventually, the simulations will be combined in watershed scale groundwater models.

  12. New advances in methodology for statistical tests useful in geostatistical studies

    SciTech Connect

    Borgman, L.E.

    1988-05-01

    Methodology for statistical procedures to perform tests of hypothesis pertaining to various aspects of geostatistical investigations has been slow in developing. The correlated nature of the data precludes most classical tests and makes the design of new tests difficult. Recent studies have led to modifications of the classical t test which allow for the intercorrelation. In addition, results for certain nonparametric tests have been obtained. The conclusions of these studies provide a variety of new tools for the geostatistician in deciding questions on significant differences and magnitudes.

  13. Mapping forest parameters using geostatistics and remote sensing data

    NASA Astrophysics Data System (ADS)

    Lewis, Sian Patricia

    This work presents a new method for characterising forests with remote sensing data using numerical scene simulations and spatial statistics. The principal study area is Cat Tien National Park, Vietnam. This site has undergone both recent changes in vegetation composition associated with population pressures, as well as historical changes due to military activities during the 1960s and 70s and provides an appropriate location for spatio-temporal monitoring of forest structure. The principal remote sensing data used comprises a set of panchromatic declassified air-photos (1965--1966). The lack of flight details for these makes established techniques for exterior orientation impractical. An alternative means to geo-rectifying these data is therefore presented. This focuses on a new application of a stereomatching algorithm, where a disparity model, related to topographic features, is first built and then co-registered to a geo-referenced elevation model to provide the transformation required to correct the air-photos. These geo-rectified data are then processed for forest parameter extraction. Scene modelling is used to produce simulations of varying ground structure. A geo-optical model is used to capture the shape and size distribution of objects in the scene, and to allow for crown shading on the trees. The scene variogram is considered as a combination of spatial interactions between scene elements (crown and ground), which are described by 'component variograms'. These are examined under differing scene specifications, and used to explore and explain the mechanisms responsible for variations in scene variogram 'range' across multi-spectral data. The scene simulations provide a set of candidate model variograms, derived from physical realisations of scene structure, for use in inverting the experimental scene variogram, where forest structural parameters are derived from the realisation associated with the best fit. Results are presented for the high resolution

  14. Application of multiple-point geostatistics on modelling pumping tests and tracer tests in heterogeneous environments with complex geological structures

    NASA Astrophysics Data System (ADS)

    Huysmans, Marijke; Dassargues, Alain

    2014-05-01

    In heterogeneous environments with complex geological structures, analysis of pumping and tracer tests is often problematic. Standard interpretation methods do not account for heterogeneity or simulate this heterogeneity introducing empirical zonation of the calibrated parameters or using variogram-based geostatistical techniques that are often not able to describe realistic heterogeneity in complex geological environments where e.g. sedimentary structures, multi-facies deposits, structures with large connectivity or curvi-linear structures can be present. Multiple-point geostatistics aims to overcome the limitations of the variogram and can be applied in different research domains to simulate heterogeneity in complex environments. In this project, multiple-point geostatistics is applied to the interpretation of pumping tests and a tracer test in an actual case of a sandy heterogeneous aquifer. This study allows to deduce the main advantages and disadvantages of this technique compared to variogram-based techniques for interpretation of pumping tests and tracer tests. A pumping test and a tracer test were performed in the same sandbar deposit consisting of cross-bedded units composed of materials with different grain sizes and hydraulic conductivities. The pumping test and the tracer test are analyzed with a local 3D groundwater model in which fine-scale sedimentary heterogeneity is modelled using multiple-point geostatistics. To reduce CPU and RAM requirements of the multiple-point geostatistical simulation steps, edge properties indicating the presence of irregularly-shaped surfaces are directly simulated. Results show that for the pumping test as well as for the tracer test, incorporating heterogeneity results in a better fit between observed and calculated drawdowns/concentrations. The improvement of the fit is however not as large as expected. In this paper, the reasons for these somewhat unsatisfactory results are explored and recommendations for future

  15. Assessing Landscape-Scale Soil Moisture Distribution Using Auxiliary Sensing Technologies and Multivariate Geostatistics

    NASA Astrophysics Data System (ADS)

    Landrum, C.; Castrignanò, A.; Mueller, T.; Zourarakis, D.; Zhu, J.

    2013-12-01

    It is important to assess soil moisture to develop strategies to better manage its availability and use. At the landscape scale, soil moisture distribution derives from an integration of hydrologic, pedologic and geomorphic processes that cause soil moisture variability (SMV) to be time, space, and scale-dependent. Traditional methods to assess SMV at this scale are often costly, labor intensive, and invasive, which can lead to inadequate sampling density and spatial coverage. Fusing traditional sampling techniques with georeferenced auxiliary sensing technologies, such as geoelectric sensing and LiDAR, provide an alternative approach. Because geoelectric and LiDAR measurements are sensitive to soil properties and terrain features that affect soil moisture variation, they are often employed as auxiliary measures to support less dense direct sampling. Georeferenced proximal sensing acquires rapid, real-time, high resolution data over large spatial extents that is enriched with spatial, temporal and scale-dependent information. Data fusion becomes important when proximal sensing is used in tandem with more sparse direct sampling. Multicollocated factorial cokriging (MFC) is one technique of multivariate geostatistics to fuse multiple data sources collected at different sampling scales to study the spatial characteristics of environmental properties. With MFC sparse soil observations are supported by more densely sampled auxiliary attributes to produce more consistent spatial descriptions of scale-dependent parameters affecting SMV. This study uses high resolution geoelectric and LiDAR data as auxiliary measures to support direct soil sampling (n=127) over a 40 hectare Central Kentucky (USA) landscape. Shallow and deep apparent electrical resistivity (ERa) were measured using a Veris 3100 in tandem with soil moisture sampling on three separate dates with ascending soil moisture contents ranging from plant wilting point to field capacity. Terrain features were produced

  16. Geostatistical and Stochastic Study of Flow and Transport in the Unsaturated Zone at Yucca Mountain

    SciTech Connect

    Ye, Ming; Pan, Feng; Hu, Xiaolong; Zhu, Jianting

    2007-08-14

    Yucca Mountain has been proposed by the U.S. Department of Energy as the nation’s long-term, permanent geologic repository for spent nuclear fuel or high-level radioactive waste. The potential repository would be located in Yucca Mountain’s unsaturated zone (UZ), which acts as a critical natural barrier delaying arrival of radionuclides to the water table. Since radionuclide transport in groundwater can pose serious threats to human health and the environment, it is important to understand how much and how fast water and radionuclides travel through the UZ to groundwater. The UZ system consists of multiple hydrogeologic units whose hydraulic and geochemical properties exhibit systematic and random spatial variation, or heterogeneity, at multiple scales. Predictions of radionuclide transport under such complicated conditions are uncertain, and the uncertainty complicates decision making and risk analysis. This project aims at using geostatistical and stochastic methods to assess uncertainty of unsaturated flow and radionuclide transport in the UZ at Yucca Mountain. Focus of this study is parameter uncertainty of hydraulic and transport properties of the UZ. The parametric uncertainty arises since limited parameter measurements are unable to deterministically describe spatial variability of the parameters. In this project, matrix porosity, permeability and sorption coefficient of the reactive tracer (neptunium) of the UZ are treated as random variables. Corresponding propagation of parametric uncertainty is quantitatively measured using mean, variance, 5th and 95th percentiles of simulated state variables (e.g., saturation, capillary pressure, percolation flux, and travel time). These statistics are evaluated using a Monte Carlo method, in which a three-dimensional flow and transport model implemented using the TOUGH2 code is executed with multiple parameter realizations of the random model parameters. The project specifically studies uncertainty of unsaturated

  17. Multiscale organization of joints and faults in a fractured reservoir revealed by geostatistical, multifractal and wavelet techniques

    SciTech Connect

    Castaing, C.; Genter, A.; Ouillon, G.

    1995-08-01

    Datasets of the geometry of fracture systems were analysed at various scales in the western Arabian sedimentary platform by means of geostatistical, multifractal, and anisotropic-wavelet techniques. The investigations covered a wide range of scales, from regional to outcrops in a well-exposed area, and were based on field mapping of fractures, and the interpretation and digitizing of fracture patterns on aerial photographs and satellite images. As a first step, fracture data sets were used to examine the direction, size, spacing and density systematics, and the variability in these quantities with space and scale. Secondly, a multifractal analysis was carried out, which consists in estimating the moments of the spatial distribution of fractures at different resolutions. This global multifractal method was complemented by a local wavelet analysis, using a new anisotropic technique tailored to linear structures. For a map with a given scale of detail, this procedure permits to define integrated fracture patterns and their associated directions at a more regional scale. The main result of this combined approach is that fracturing is not a self-similar process from the centimeter scale up to the one-million-kilometer scale. Spatial distribution of faults appears as being highly controlled by the thickness of the different rheological layers that constitute the crust. A proceeding for upscaling fracture systems in sedimentary reservoirs can be proposed, based on (i) a power law for joint-length distribution, (ii) characteristic joint spacing depending on the critical sedimentary units, and (iii) fractal fault geometry for faults larger than the whole thickness of the sedimentary basin.

  18. Geostatistical Analysis of Tritium, 3H/3He Age and Noble Gas Derived Parameters in California Groundwater

    NASA Astrophysics Data System (ADS)

    Visser, A.; Singleton, M. J.; Moran, J. E.; Fram, M. S.; Kulongoski, J. T.; Esser, B. K.

    2014-12-01

    Key characteristics of California groundwater systems related to aquifer vulnerability, sustainability, recharge locations and mechanisms, and anthropogenic impact on recharge, are revealed in a spatial geostatistical analysis of the data set of tritium, dissolved noble gas and helium isotope analyses collected for the California State Water Resources Control Board's Groundwater Ambient Monitoring and Assessment (GAMA) and California Aquifer Susceptibility (CAS) programs. Over 4,000 tritium and noble gas analyses are available from wells across California. 25% of the analyzed samples contained less than 1 pCi/L indicating recharge occurred before 1950. The correlation length of tritium concentration is 120 km. Nearly 50% of the wells show a significant component of terrigenic helium. Over 50% of these samples show a terrigenic helium isotope ratio (Rter) that is significantly higher than the radiogenic helium isotope ratio (Rrad = 2×10-8). Rter values of more than three times the atmospheric isotope ratio (Ra = 1.384×10-6) are associated with known faults and volcanic provinces in Northern California. In the Central Valley, Rter varies from radiogenic to 2.25 Ra, complicating 3H/3He dating. The Rter was mapped by kriging, showing a correlation length of less than 50 km. The local predicted Rter was used to separate tritiogenic from atmospheric and terrigenic 3He. Regional groundwater recharge areas, indicated by young groundwater ages, are located in the southern Santa Clara Basin and in the upper LA basin and in the eastern San Joaquin Valley and along unlined canals carrying Colorado River water. Recharge in California is dominated by agricultural return flows, river recharge and managed aquifer recharge rather than precipitation excess. Combined application of noble gases and other groundwater tracers reveal the impact of engineered groundwater recharge and prove invaluable for the study of complex groundwater systems. This work was performed under the

  19. Geostatistical modelling of arsenic in drinking water wells and related toenail arsenic concentrations across Nova Scotia, Canada.

    PubMed

    Dummer, T J B; Yu, Z M; Nauta, L; Murimboh, J D; Parker, L

    2015-02-01

    Arsenic is a naturally occurring class 1 human carcinogen that is widespread in private drinking water wells throughout the province of Nova Scotia in Canada. In this paper we explore the spatial variation in toenail arsenic concentrations (arsenic body burden) in Nova Scotia. We describe the regional distribution of arsenic concentrations in private well water supplies in the province, and evaluate the geological and environmental features associated with higher levels of arsenic in well water. We develop geostatistical process models to predict high toenail arsenic concentrations and high well water arsenic concentrations, which have utility for studies where no direct measurements of arsenic body burden or arsenic exposure are available. 892 men and women who participated in the Atlantic Partnership for Tomorrow's Health Project provided both drinking water and toenail clipping samples. Information on socio-demographic, lifestyle and health factors was obtained with a set of standardized questionnaires. Anthropometric indices and arsenic concentrations in drinking water and toenails were measured. In addition, data on arsenic concentrations in 10,498 private wells were provided by the Nova Scotia Department of Environment. We utilised stepwise multivariable logistic regression modelling to develop separate statistical models to: a) predict high toenail arsenic concentrations (defined as toenail arsenic levels ≥0.12 μg g(-1)) and b) predict high well water arsenic concentrations (defined as well water arsenic levels ≥5.0 μg L(-1)). We found that the geological and environmental information that predicted well water arsenic concentrations can also be used to accurately predict toenail arsenic concentrations. We conclude that geological and environmental factors contributing to arsenic contamination in well water are the major contributing influences on arsenic body burden among Nova Scotia residents. Further studies are warranted to assess appropriate

  20. Geostatistical Analysis of County-Level Lung Cancer Mortality Rates in the Southeastern United States

    PubMed Central

    Goovaerts, Pierre

    2009-01-01

    The analysis of health data and putative covariates, such as environmental, socioeconomic, demographic, behavioral, or occupational factors, is a promising application for geostatistics. Transferring methods originally developed for the analysis of earth properties to health science, however, presents several methodological and technical challenges. These arise because health data are typically aggregated over irregular spatial supports (e.g., counties) and consist of a numerator and a denominator (i.e., rates). This article provides an overview of geostatistical methods tailored specifically to the characteristics of areal health data, with an application to lung cancer mortality rates in 688 U.S. counties of the southeast (1970–1994). Factorial Poisson kriging can filter short-scale variation and noise, which can be large in sparsely populated counties, to reveal similar regional patterns for male and female cancer mortality that correlate well with proximity to shipyards. Rate uncertainty was transferred through local cluster analysis using stochastic simulation, allowing the computation of the likelihood of clusters of low or high cancer mortality. Accounting for population size and rate uncertainty led to the detection of new clusters of high mortality around Oak Ridge National Laboratory for both sexes, in counties with high concentrations of pig farms and paper mill industries for males (occupational exposure) and in the vicinity of Atlanta for females. PMID:20445829

  1. Geostatistical analysis of soil moisture distribution in a part of Solani River catchment

    NASA Astrophysics Data System (ADS)

    Kumar, Kamal; Arora, M. K.; Hariprasad, K. S.

    2016-03-01

    The aim of this paper is to estimate soil moisture at spatial level by applying geostatistical techniques on the point observations of soil moisture in parts of Solani River catchment in Haridwar district of India. Undisturbed soil samples were collected at 69 locations with soil core sampler at a depth of 0-10 cm from the soil surface. Out of these, discrete soil moisture observations at 49 locations were used to generate a spatial soil moisture distribution map of the region. Two geostatistical techniques, namely, moving average and kriging, were adopted. Root mean square error (RMSE) between observed and estimated soil moisture at remaining 20 locations was determined to assess the accuracy of the estimated soil moisture. Both techniques resulted in low RMSE at small limiting distance, which increased with the increase in the limiting distance. The root mean square error varied from 7.42 to 9.77 in moving average method, while in case of kriging it varied from 7.33 to 9.99 indicating similar performance of the two techniques.

  2. Acceleration of the Geostatistical Software Library (GSLIB) by code optimization and hybrid parallel programming

    NASA Astrophysics Data System (ADS)

    Peredo, Oscar; Ortiz, Julián M.; Herrero, José R.

    2015-12-01

    The Geostatistical Software Library (GSLIB) has been used in the geostatistical community for more than thirty years. It was designed as a bundle of sequential Fortran codes, and today it is still in use by many practitioners and researchers. Despite its widespread use, few attempts have been reported in order to bring this package to the multi-core era. Using all CPU resources, GSLIB algorithms can handle large datasets and grids, where tasks are compute- and memory-intensive applications. In this work, a methodology is presented to accelerate GSLIB applications using code optimization and hybrid parallel processing, specifically for compute-intensive applications. Minimal code modifications are added decreasing as much as possible the elapsed time of execution of the studied routines. If multi-core processing is available, the user can activate OpenMP directives to speed up the execution using all resources of the CPU. If multi-node processing is available, the execution is enhanced using MPI messages between the compute nodes.Four case studies are presented: experimental variogram calculation, kriging estimation, sequential gaussian and indicator simulation. For each application, three scenarios (small, large and extra large) are tested using a desktop environment with 4 CPU-cores and a multi-node server with 128 CPU-nodes. Elapsed times, speedup and efficiency results are shown.

  3. Geostatistical reservoir characterization of complex lateral and vertical sequences in a mixed carbonate platform

    SciTech Connect

    Norris, R.J.; Alabert, F.G.; Massonnat, G.J. )

    1994-07-01

    In recent years reservoir characterization through the use of geostatistics has become an almost routine part of production geology. Many techniques are available within the broad title of geostatistics, having been developed in response to many types of problem. One characteristic feature of almost all techniques (Stochastic Indicator Simulation, Boolean [open quotes]object[close quotes] Modeling, Gaussian [and Truncated Gaussian] methods and Optimized Markov-fields) is their reliance on the concept of quantifiable correlations, which reflect some aspect of the shape of [open quotes]objects.[close quotes] For example, almost any of the above noted techniques, and their variants, could be used to model fluvial, deltaic, or turbiditic reservoirs because in each case facies can be described in terms of geometries (channels, lobes, etc.). This study considers the complex lateral and vertical variations of a mixed carbonate platform environment, where facies cannot be easily characterized by simple geometries. The complex heterogeneities are a function of changes in sea level, representing fluctuations over several orders of cyclicity. Given facies have no characteristic form, being the product of the interplay between sediment supply and sea level change. This type of environment is, therefore, characterized by a good deal of information concerning trends in the data, while correlations and geometries are almost meaningless. Associated with the concepts of cyclicity, rules concerning the reappearance of facies, or otherwise, were developed. For example, minor recurrences of maximum flooding surfaces could be tolerated within individual units but other specified recurrences need to be excluded.

  4. Redesigning rain gauges network in Johor using geostatistics and simulated annealing

    SciTech Connect

    Aziz, Mohd Khairul Bazli Mohd; Yusof, Fadhilah; Daud, Zalina Mohd; Yusop, Zulkifli; Kasno, Mohammad Afif

    2015-02-03

    Recently, many rainfall network design techniques have been developed, discussed and compared by many researchers. Present day hydrological studies require higher levels of accuracy from collected data. In numerous basins, the rain gauge stations are located without clear scientific understanding. In this study, an attempt is made to redesign rain gauge network for Johor, Malaysia in order to meet the required level of accuracy preset by rainfall data users. The existing network of 84 rain gauges in Johor is optimized and redesigned into a new locations by using rainfall, humidity, solar radiation, temperature and wind speed data collected during the monsoon season (November - February) of 1975 until 2008. This study used the combination of geostatistics method (variance-reduction method) and simulated annealing as the algorithm of optimization during the redesigned proses. The result shows that the new rain gauge location provides minimum value of estimated variance. This shows that the combination of geostatistics method (variance-reduction method) and simulated annealing is successful in the development of the new optimum rain gauge system.

  5. Geostatistics: a common link between medical geography, mathematical geology, and medical geology.

    PubMed

    Goovaerts, P

    2014-08-01

    Since its development in the mining industry, geostatistics has emerged as the primary tool for spatial data analysis in various fields, ranging from earth and atmospheric sciences to agriculture, soil science, remote sensing, and more recently environmental exposure assessment. In the last few years, these tools have been tailored to the field of medical geography or spatial epidemiology, which is concerned with the study of spatial patterns of disease incidence and mortality and the identification of potential 'causes' of disease, such as environmental exposure, diet and unhealthy behaviours, economic or socio-demographic factors. On the other hand, medical geology is an emerging interdisciplinary scientific field studying the relationship between natural geological factors and their effects on human and animal health. This paper provides an introduction to the field of medical geology with an overview of geostatistical methods available for the analysis of geological and health data. Key concepts are illustrated using the mapping of groundwater arsenic concentration across eleven Michigan counties and the exploration of its relationship to the incidence of prostate cancer at the township level.

  6. Geostatistics: a common link between medical geography, mathematical geology, and medical geology

    PubMed Central

    Goovaerts, P.

    2015-01-01

    Synopsis Since its development in the mining industry, geostatistics has emerged as the primary tool for spatial data analysis in various fields, ranging from earth and atmospheric sciences to agriculture, soil science, remote sensing, and more recently environmental exposure assessment. In the last few years, these tools have been tailored to the field of medical geography or spatial epidemiology, which is concerned with the study of spatial patterns of disease incidence and mortality and the identification of potential ‘causes’ of disease, such as environmental exposure, diet and unhealthy behaviours, economic or socio-demographic factors. On the other hand, medical geology is an emerging interdisciplinary scientific field studying the relationship between natural geological factors and their effects on human and animal health. This paper provides an introduction to the field of medical geology with an overview of geostatistical methods available for the analysis of geological and health data. Key concepts are illustrated using the mapping of groundwater arsenic concentration across eleven Michigan counties and the exploration of its relationship to the incidence of prostate cancer at the township level. PMID:25722963

  7. Bridges between multiple-point geostatistics and texture synthesis: Review and guidelines for future research

    NASA Astrophysics Data System (ADS)

    Mariethoz, Gregoire; Lefebvre, Sylvain

    2014-05-01

    Multiple-Point Simulations (MPS) is a family of geostatistical tools that has received a lot of attention in recent years for the characterization of spatial phenomena in geosciences. It relies on the definition of training images to represent a given type of spatial variability, or texture. We show that the algorithmic tools used are similar in many ways to techniques developed in computer graphics, where there is a need to generate large amounts of realistic textures for applications such as video games and animated movies. Similarly to MPS, these texture synthesis methods use training images, or exemplars, to generate realistic-looking graphical textures. Both domains of multiple-point geostatistics and example-based texture synthesis present similarities in their historic development and share similar concepts. These disciplines have however remained separated, and as a result significant algorithmic innovations in each discipline have not been universally adopted. Texture synthesis algorithms present drastically increased computational efficiency, patterns reproduction and user control. At the same time, MPS developed ways to condition models to spatial data and to produce 3D stochastic realizations, which have not been thoroughly investigated in the field of texture synthesis. In this paper we review the possible links between these disciplines and show the potential and limitations of using concepts and approaches from texture synthesis in MPS. We also provide guidelines on how recent developments could benefit both fields of research, and what challenges remain open.

  8. Assessment of groundwater pollution in West Delhi, India using geostatistical approach.

    PubMed

    Adhikary, Partha Pratim; Chandrasekharan, H; Chakraborty, Debashis; Kamble, Kalpana

    2010-08-01

    The exploration, exploitation, and unscientific management of groundwater resources in the National Capital Territory (NCT) of Delhi, India have posed a serious threat of reduction in quantity and deterioration of quality. The objective of the study is to determine the groundwater quality and to assess the risk of groundwater pollution at Najafgarh, NCT of Delhi. The groundwater quality parameters were analyzed from the existing wells of the Najafgarh and the thematic maps were generated using geostatistical concepts. Ordinary kriging and indicator kriging methods were used as geostatistical approach for preparation of thematic maps of the groundwater quality parameters such as bicarbonate, calcium, chloride, electrical conductivity (EC), magnesium, nitrate, sodium, and sulphate with concentrations equal or greater than their respective groundwater pollution cutoff value. Experimental semivariogram values were fitted well in spherical model for the water quality parameters, such as bicarbonate, chloride, EC, magnesium, sodium, and sulphate and in exponential model for calcium and nitrate. The thematic maps of all the groundwater quality parameters exhibited an increasing trend of pollution from the northern and western part of the study area towards the southern and eastern part. The concentration was highest at the southernmost part of the study area but it could not reflect correctly the groundwater pollution status. The indicator kriging method is useful to assess the risk of groundwater pollution by giving the conditional probability of concentrations of different chemical parameters exceeding their cutoff values. Thus, risk assessment of groundwater pollution is useful for proper management of groundwater resources and minimizing the pollution threat.

  9. Geostatistical borehole image-based mapping of karst-carbonate aquifer pores

    USGS Publications Warehouse

    Michael Sukop,; Cunningham, Kevin J.

    2016-01-01

    Quantification of the character and spatial distribution of porosity in carbonate aquifers is important as input into computer models used in the calculation of intrinsic permeability and for next-generation, high-resolution groundwater flow simulations. Digital, optical, borehole-wall image data from three closely spaced boreholes in the karst-carbonate Biscayne aquifer in southeastern Florida are used in geostatistical experiments to assess the capabilities of various methods to create realistic two-dimensional models of vuggy megaporosity and matrix-porosity distribution in the limestone that composes the aquifer. When the borehole image data alone were used as the model training image, multiple-point geostatistics failed to detect the known spatial autocorrelation of vuggy megaporosity and matrix porosity among the three boreholes, which were only 10 m apart. Variogram analysis and subsequent Gaussian simulation produced results that showed a realistic conceptualization of horizontal continuity of strata dominated by vuggy megaporosity and matrix porosity among the three boreholes.

  10. The Direct Sampling method to perform multiple-point geostatistical simulations

    NASA Astrophysics Data System (ADS)

    Mariethoz, Gregoire; Renard, Philippe; Straubhaar, Julien

    2010-11-01

    Multiple-point geostatistics is a general statistical framework to model spatial fields displaying a wide range of complex structures. In particular, it allows controlling connectivity patterns that have a critical importance for groundwater flow and transport problems. This approach involves considering data events (spatial arrangements of values) derived from a training image (TI). All data events found in the TI are usually stored in a database, which is used to retrieve conditional probabilities for the simulation. Instead, we propose to sample directly the training image for a given data event, making the database unnecessary. Our method is statistically equivalent to previous implementations, but in addition it allows extending the application of multiple-point geostatistics to continuous variables and to multivariate problems. The method can be used for the simulation of geological heterogeneity, accounting or not for indirect observations such as geophysics. We show its applicability in the presence of complex features, nonlinear relationships between variables, and with various cases of nonstationarity. Computationally, it is fast, easy to parallelize, parsimonious in memory needs, and straightforward to implement.

  11. Stochastic Estimates of the Permeability Field of the Soultz-sous-Forêts Geothermal Reservoir - Comparison of Bayesian Inversion, MC Geostatistics, and EnKF Assimilation

    NASA Astrophysics Data System (ADS)

    Kosack, Christian; Vogt, Christian; Rath, Volker; Marquart, Gabriele

    2010-05-01

    The knowledge of the permeability distribution at depth is of primary concern for any geothermal reservoir engineering. However, permeability might change over orders of magnitude even for a single rock type and is additionally controlled by tectonic or engineered fracturing of the rocks. During reservoir exploration pumping tests are regularly performed where tracer marked water is pumped in one borehole and retrieved at one or a few others. At the European Enhanced Geothermal System (EGS) test site at Soultz-sous-Forêts three wells had been drilled in the granitic bedrock down to 4 to 5 km and were hydraulically stimulated to enhance the hydraulic connectivity between the wells. In July 2005, a tracer circulation test was carried out in order to estimate the changes of the hydraulic properties. Therefore a tracer was injected into the well GPK3 for 19 hours at a rate of 0.015 m3 s-1 and a concentration of 0.389 mol m-3. Tracer concentration was measured in the production wells over the following 5 months, while the produced water was re-injected into GPK3. This experiment demonstrated a good hydraulic connection between GPK3 and one of the production wells, GPK2, while a very low connectivity was observed in the other one, GPK4. We tested three different approaches simulating the pumping experiment with the numerical simulator shemat_suite in a simplified 3D model of the site in order to study their respective potential to estimate a reliable permeability distribution for the Soultz reservoir: A full-physics gradient-based Bayesian inversion, a massive Monte Carlo approach with geostatistic analysis, and an Ensemble-Kalman-Filter (EnKF) assimilation. A common feature in all models is a high permeability zone which acts as main flow area and transports most of the tracer. It is assumed to be associated with the fault zone cutting through the boreholes GPK2 and GPK3. With the Bayesian Inversion we were able to estimate a parameter set consisting of porosity

  12. Quality measures for geostatistical prediction of log-normal soil properties.

    NASA Astrophysics Data System (ADS)

    Lark, R. M.

    2012-04-01

    A signature of non-linear processes in the soil is the non-normal distribution of soil properties. A common non-normal distribution is the log-normal, in which the variable Z can be transformed to a variable with a normal distribution by Y = log e(Z). Log-normal variables are common in soil geochemistry and hydrology. It is standard practice in geostatistics to use the log-transformation for such variables before spatial modelling and prediction, and there are procedures to back-transform predictions of Y to the original scale of measurement Z. This is important because values on the original scale are commonly required either for scientific purposes or for practical applications such as the assessment of potential contaminant concentrations in soil. One of the strengths of geostatistics is that geostatistical prediction returns a prediction error variance. Furthermore, this variance can be computed before a survey is undertaken, for a range of possible different sampling networks, since it depends only on the disposition of sample sites, and the variogram model of spatial dependence. This allows the most efficient network to be selected: one which will provide estimates of sufficient precision (where the prediction error variances are within acceptable bounds) without over-sampling. In log-normal kriging the prediction error variance depends not only on the variogram and the sampling array, but also on the conditional mean value of the variable, which is not known until after sampling. This means that the usual pre-survey quality measures which can be computed to guide the planning of geostatistical surveys are not available for log-normal variables. Given that many critical variables, such as contaminant concentrations, are often log-normally distributed, this is a serious gap in the capablity of geostatistics to facilitate rational sampling design for environmental management and monitoring. In this paper I propose and demonstrate some quality measures that can

  13. Comparing Estimators of Microbiological Attributes by Random Subsamples

    NASA Astrophysics Data System (ADS)

    Li, M.; Adriaens, P.

    2005-12-01

    validation approach called bootstrapping, which randomly take a designed number of data points out of the data set, and examine the reproduction of their estimate by the rest of the data. The repeated random selection will be used to compare the M-scale model to the ordinary kriging, visualized quantitatively by quantile-quantile (Q-Q) plots and scatter plots. Estimates and uncertainties evaluated by the M-scale model will be compared using the random subsets, to examine the unbiasedness of the estimate as well as the appropriateness of the uncertainty evaluated. An additional comparison, using the dataset collected in Anacostia River, Washington D.C., can further be used to inform further applicability under sparsely sampled site. For a conclusive test, artificial datasets based on different scenario will then be generated, in order to examine the general performance and restriction of the models under different data distribution and spatial structures.

  14. Approaches in highly parameterized inversion: bgaPEST, a Bayesian geostatistical approach implementation with PEST: documentation and instructions

    USGS Publications Warehouse

    Fienen, Michael N.; D'Oria, Marco; Doherty, John E.; Hunt, Randall J.

    2013-01-01

    The application bgaPEST is a highly parameterized inversion software package implementing the Bayesian Geostatistical Approach in a framework compatible with the parameter estimation suite PEST. Highly parameterized inversion refers to cases in which parameters are distributed in space or time and are correlated with one another. The Bayesian aspect of bgaPEST is related to Bayesian probability theory in which prior information about parameters is formally revised on the basis of the calibration dataset used for the inversion. Conceptually, this approach formalizes the conditionality of estimated parameters on the specific data and model available. The geostatistical component of the method refers to the way in which prior information about the parameters is used. A geostatistical autocorrelation function is used to enforce structure on the parameters to avoid overfitting and unrealistic results. Bayesian Geostatistical Approach is designed to provide the smoothest solution that is consistent with the data. Optionally, users can specify a level of fit or estimate a balance between fit and model complexity informed by the data. Groundwater and surface-water applications are used as examples in this text, but the possible uses of bgaPEST extend to any distributed parameter applications.

  15. Precipitation estimation in mountainous terrain using multivariate geostatistics. Part I: structural analysis

    USGS Publications Warehouse

    Hevesi, Joseph A.; Istok, Jonathan D.; Flint, Alan L.

    1992-01-01

    Values of average annual precipitation (AAP) are desired for hydrologic studies within a watershed containing Yucca Mountain, Nevada, a potential site for a high-level nuclear-waste repository. Reliable values of AAP are not yet available for most areas within this watershed because of a sparsity of precipitation measurements and the need to obtain measurements over a sufficient length of time. To estimate AAP over the entire watershed, historical precipitation data and station elevations were obtained from a network of 62 stations in southern Nevada and southeastern California. Multivariate geostatistics (cokriging) was selected as an estimation method because of a significant (p = 0.05) correlation of r = .75 between the natural log of AAP and station elevation. A sample direct variogram for the transformed variable, TAAP = ln [(AAP) 1000], was fitted with an isotropic, spherical model defined by a small nugget value of 5000, a range of 190 000 ft, and a sill value equal to the sample variance of 163 151. Elevations for 1531 additional locations were obtained from topographic maps to improve the accuracy of cokriged estimates. A sample direct variogram for elevation was fitted with an isotropic model consisting of a nugget value of 5500 and three nested transition structures: a Gaussian structure with a range of 61 000 ft, a spherical structure with a range of 70 000 ft, and a quasi-stationary, linear structure. The use of an isotropic, stationary model for elevation was considered valid within a sliding-neighborhood radius of 120 000 ft. The problem of fitting a positive-definite, nonlinear model of coregionalization to an inconsistent sample cross variogram for TAAP and elevation was solved by a modified use of the Cauchy-Schwarz inequality. A selected cross-variogram model consisted of two nested structures: a Gaussian structure with a range of 61 000 ft and a spherical structure with a range of 190 000 ft. Cross validation was used for model selection and for

  16. Precipitation estimation in mountainous terrain using multivariate geostatistics. Part II: isohyetal maps

    USGS Publications Warehouse

    Hevesi, Joseph A.; Flint, Alan L.; Istok, Jonathan D.

    1992-01-01

    Values of average annual precipitation (AAP) may be important for hydrologic characterization of a potential high-level nuclear-waste repository site at Yucca Mountain, Nevada. Reliable measurements of AAP are sparse in the vicinity of Yucca Mountain, and estimates of AAP were needed for an isohyetal mapping over a 2600-square-mile watershed containing Yucca Mountain. Estimates were obtained with a multivariate geostatistical model developed using AAP and elevation data from a network of 42 precipitation stations in southern Nevada and southeastern California. An additional 1531 elevations were obtained to improve estimation accuracy. Isohyets representing estimates obtained using univariate geostatistics (kriging) defined a smooth and continuous surface. Isohyets representing estimates obtained using multivariate geostatistics (cokriging) defined an irregular surface that more accurately represented expected local orographic influences on AAP. Cokriging results included a maximum estimate within the study area of 335 mm at an elevation of 7400 ft, an average estimate of 157 mm for the study area, and an average estimate of 172 mm at eight locations in the vicinity of the potential repository site. Kriging estimates tended to be lower in comparison because the increased AAP expected for remote mountainous topography was not adequately represented by the available sample. Regression results between cokriging estimates and elevation were similar to regression results between measured AAP and elevation. The position of the cokriging 250-mm isohyet relative to the boundaries of pinyon pine and juniper woodlands provided indirect evidence of improved estimation accuracy because the cokriging result agreed well with investigations by others concerning the relationship between elevation, vegetation, and climate in the Great Basin. Calculated estimation variances were also mapped and compared to evaluate improvements in estimation accuracy. Cokriging estimation variances

  17. A geostatistical method applied to the geochemical study of the Chichinautzin Volcanic Field in Mexico

    NASA Astrophysics Data System (ADS)

    Robidoux, P.; Roberge, J.; Urbina Oviedo, C. A.

    2011-12-01

    The origin of magmatism and the role of the subducted Coco's Plate in the Chichinautzin volcanic field (CVF), Mexico is still a subject of debate. It has been established that mafic magmas of alkali type (subduction) and calc-alkali type (OIB) are produced in the CVF and both groups cannot be related by simple fractional crystallization. Therefore, many geochemical studies have been done, and many models have been proposed. The main goal of the work present here is to provide a new tool for the visualization and interpretation of geochemical data using geostatistics and geospatial analysis techniques. It contains a complete geodatabase built from referred samples over the 2500 km2 area of CVF and its neighbour stratovolcanoes (Popocatepetl, Iztaccihuatl and Nevado de Toluca). From this database, map of different geochemical markers were done to visualise geochemical signature in a geographical manner, to test the statistic distribution with a cartographic technique and highlight any spatial correlations. The distribution and regionalization of the geochemical signatures can be viewed in a two-dimensional space using a specific spatial analysis tools from a Geographic Information System (GIS). The model of spatial distribution is tested with Linear Decrease (LD) and Inverse Distance Weight (IDW) interpolation technique because they best represent the geostatistical characteristics of the geodatabase. We found that ratio of Ba/Nb, Nb/Ta, Th/Nb show first order tendency, which means visible spatial variation over a large scale area. Monogenetic volcanoes in the center of the CVF have distinct values compare to those of the Popocatepetl-Iztaccihuatl polygenetic complex which are spatially well defined. Inside the Valley of Mexico, a large quantity of monogenetic cone in the eastern portion of CVF has ratios similar to the Iztaccihuatl and Popocatepetl complex. Other ratios like alkalis vs SiO2, V/Ti, La/Yb, Zr/Y show different spatial tendencies. In that case, second

  18. Introduction to this Special Issue on Geostatistics and Scaling of Remote Sensing

    NASA Technical Reports Server (NTRS)

    Quattrochi, Dale A.

    1999-01-01

    The germination of this special PE&RS issue began at the Royal Geographical Society (with the Institute of British Geographers)(RCS-IBC) annual meeting in January, 1997 held at the University of Exeter in Exeter, England. The cold and snow of an England winter were greatly tempered by the friendly and cordial discussions that ensued at the meeting on possible ways to foster both dialog and research across "the Big Pond" between geographers in the US and the UK on the use of geostatistics and geospatial techniques for remote sensing of land surface processes. It was decided that one way to stimulate and enhance cooperation on the application of geostatistics and geospatial methods in remote sensing was to hold parallel sessions on these topics at appropriate meeting venues in 1998 in both the US and the UK Selected papers given at these sessions would be published as a special issue of PE&RS on the US side, and as a special issue of Computers and Geosciences (C&G) on the UK side, to highlight the commonality in research on geostatistics and geospatial methods in remote sensing and spatial data analysis on both sides of the Atlantic Ocean. As a consequence, a session on "Ceostatistics and Geospatial Techniques for Remote Sensing of Land Surface Processes" was held at the Association of American Geographers (AAG) annual meeting in Boston, Massachusetts in March, 1998, sponsored by the AAG's Remote Sensing Specialty Group (RSSG). A similar session was held at the RGS-IBG annual meeting in Guildford, Surrey, England in January 1998, organized by the Modeling and Advanced Techniques Special Interest Group (MAT SIG) of the Remote Sensing Society (RSS). The six papers that in part, comprise this issue of PE&RS, are the US complement to such a dual journal publication effort. Both of us are co-editors of each of the journal special issues, with the lead editor of each journal being from their respective side of the Atlantic where the journals are published. The special

  19. The impact of an intervention to introduce malaria rapid diagnostic tests on fever case management in a high transmission setting in Uganda: A mixed-methods cluster-randomized trial (PRIME)

    PubMed Central

    Chandler, Clare I. R.; Webb, Emily L.; Maiteki-Sebuguzi, Catherine; Nayiga, Susan; Nabirye, Christine; DiLiberto, Deborah D.; Ssemmondo, Emmanuel; Dorsey, Grant; Kamya, Moses R.; Staedke, Sarah G.

    2017-01-01

    Background Rapid diagnostic tests for malaria (mRDTs) have been scaled-up widely across Africa. The PRIME study evaluated an intervention aiming to improve fever case management using mRDTs at public health centers in Uganda. Methods A cluster-randomized trial was conducted from 2010–13 in Tororo, a high malaria transmission setting. Twenty public health centers were randomized in a 1:1 ratio to intervention or control. The intervention included training in health center management, fever case management with mRDTs, and patient-centered services; plus provision of mRDTs and artemether-lumefantrine (AL) when stocks ran low. Three rounds of Interviews were conducted with caregivers of children under five years of age as they exited health centers (N = 1400); reference mRDTs were done in children with fever (N = 1336). Health worker perspectives on mRDTs were elicited through semi-structured questionnaires (N = 49) and in-depth interviews (N = 10). The primary outcome was inappropriate treatment of malaria, defined as the proportion of febrile children who were not treated according to guidelines based on the reference mRDT. Findings There was no difference in inappropriate treatment of malaria between the intervention and control arms (24.0% versus 29.7%, adjusted risk ratio 0.81 [95% CI: 0.56, 1.17] p = 0.24). Most children (76.0%) tested positive by reference mRDT, but many were not prescribed AL (22.5% intervention versus 25.9% control, p = 0.53). Inappropriate treatment of children testing negative by reference mRDT with AL was also common (31.3% invention vs 42.4% control, p = 0.29). Health workers appreciated mRDTs but felt that integrating testing into practice was challenging given constraints on time and infrastructure. Conclusions The PRIME intervention did not have the desired impact on inappropriate treatment of malaria for children under five. In this high transmission setting, use of mRDTs did not lead to the reductions in antimalarial prescribing

  20. Implementation of the Iterative Proportion Fitting Algorithm for Geostatistical Facies Modeling

    SciTech Connect

    Li Yupeng Deutsch, Clayton V.

    2012-06-15

    In geostatistics, most stochastic algorithm for simulation of categorical variables such as facies or rock types require a conditional probability distribution. The multivariate probability distribution of all the grouped locations including the unsampled location permits calculation of the conditional probability directly based on its definition. In this article, the iterative proportion fitting (IPF) algorithm is implemented to infer this multivariate probability. Using the IPF algorithm, the multivariate probability is obtained by iterative modification to an initial estimated multivariate probability using lower order bivariate probabilities as constraints. The imposed bivariate marginal probabilities are inferred from profiles along drill holes or wells. In the IPF process, a sparse matrix is used to calculate the marginal probabilities from the multivariate probability, which makes the iterative fitting more tractable and practical. This algorithm can be extended to higher order marginal probability constraints as used in multiple point statistics. The theoretical framework is developed and illustrated with estimation and simulation example.

  1. [Spatial structure analysis and distribution simulation of Therioaphis trifolii population based on geostatistics and GIS].

    PubMed

    Zhang, Rong; Leng, Yun-fa; Zhu, Meng-meng; Wang, Fang

    2007-11-01

    Based on geographic information system and geostatistics, the spatial structure of Therioaphis trifolii population of different periods in Yuanzhou district of Guyuan City, the southern Ningxia Province, was analyzed. The spatial distribution of Therioaphis trifolii population was also simulated by ordinary Kriging interpretation. The results showed that Therioaphis trifolii population of different periods was correlated spatially in the study area. The semivariograms of Therioaphis trifolii could be described by exponential model, indicating an aggregated spatial arrangement. The spatial variance varied from 34.13%-48.77%, and the range varied from 8.751-12.049 km. The degree and direction of aggregation showed that the trend was increased gradually from southwest to northeast. The dynamic change of Therioaphis trifolii population in different periods could be analyzed intuitively on the simulated maps of the spatial distribution from the two aspects of time and space, The occurrence position and degree of Therioaphis trifolii to a state of certain time could be determined easily.

  2. Soil risk assessment of As and Zn contamination in a coal mining region using geostatistics [corrected].

    PubMed

    Komnitsas, Kostas; Modis, Kostas

    2006-12-01

    The present paper aims to map As and Zn contamination and assess the risk for agricultural soils in a wider disposal site containing wastes derived from coal beneficiation. Geochemical data related to environmental studies show that the waste characteristics favor solubilisation and mobilization of inorganic contaminants and in some cases the generation of acidic leachates. 135 soil samples were collected from a 34 km(2) area and analysed by using geostatistics under the maximum entropy principle in order to produce risk assessment maps and estimate the probability of soil contamination. In addition, the present paper discusses the main issues related to risk assessment in wider mining and waste disposal sites in order to assist decision makers in selecting feasible rehabilitation schemes.

  3. Decreasing sampling costs by increasing statistical efficiency through geostatistics: A case study

    SciTech Connect

    Ferns, T.W.; Ariss, C.W.

    1988-01-01

    The INEL (Idaho National Engineering Laboratory) conducted a post- mortem examination of a dioxin cleanup study having a data base of over 2000 sampling points. The result of the post-mortem study was that approximately 60% of the sampling and analysis budget could have been saved, had geostatistics been used in the sampling design. Additionally, the final product of the sampling plan would have had greater utility at the site closure negotiations because of the reliability map uniquely generated through a technique known as ''kriging.'' We feel that kriging is an important cost saving tool which should be utilized more often in Installation Restoration Program (IRP) work. Directional variograms showing data anisotropy and sample planning information are shown. Maps delineating the dioxin isopleths and areal variances are also presented. 13 refs., 4 figs., 1 tab.

  4. [Evaluation on environmental quality of heavy metals in soils and vegetables based on geostatistics and GIS].

    PubMed

    Xie, Zheng-miao; Li, Jing; Wang, Bi-ling; Chen, Jian-jun

    2006-10-01

    Contents of heavy metals (Pb, Zn, Cd, Cu) in soils and vegetables from Dongguan town in Shangyu city, China were studied using geostatistical analysis and GIS technique to evaluate environmental quality. Based on the evaluation criteria, the distribution of the spatial variability of heavy metals in soil-vegetable system was mapped and analyzed. The results showed that the distribution of soil heavy metals in a large number of soil samples in Dongguan town was asymmetric. The contents of Zn and Cu were lower than those of Cd and Pb. The concentrations distribution of Pb, Zn, Cd and Cu in soils and vegetables were different in spatial variability. There was a close relationship between total and available contents of heavy metals in soil. The contents of Pb and Cd in green vegetables were higher than those of Zn and Cu and exceeded the national sanitation standards for vegetables.

  5. An assessment of gas emanation hazard using a geographic information system and geostatistics.

    PubMed

    Astorri, F; Beaubien, S E; Ciotoli, G; Lombardi, S

    2002-03-01

    This paper describes the use of geostatistical analysis and GIS techniques to assess gas emanation hazards. The Mt. Vulsini volcanic district was selected for this study because of the wide range of natural phenomena locally present that affect gas migration in the near surface. In addition, soil gas samples that were collected in this area should allow for a calibration between the generated risk/hazard models and the measured distribution of toxic gas species at surface. The approach used during this study consisted of three general stages. First data were digitally organized into thematic layers, then software functions in the GIS program "ArcView" were used to compare and correlate these various layers, and then finally the produced "potential-risk" map was compared with radon soil gas data in order to validate the model and/or to select zones for further, more-detailed soil gas investigations.

  6. Supervised restoration of degraded medical images using multiple-point geostatistics.

    PubMed

    Pham, Tuan D

    2012-06-01

    Reducing noise in medical images has been an important issue of research and development for medical diagnosis, patient treatment, and validation of biomedical hypotheses. Noise inherently exists in medical and biological images due to the acquisition and transmission in any imaging devices. Being different from image enhancement, the purpose of image restoration is the process of removing noise from a degraded image in order to recover as much as possible its original version. This paper presents a statistically supervised approach for medical image restoration using the concept of multiple-point geostatistics. Experimental results have shown the effectiveness of the proposed technique which has potential as a new methodology for medical and biological image processing.

  7. Evaluation of a soil contaminated site and clean-up criteria: A geostatistical approach

    SciTech Connect

    Leonte, D.; Schofield, N.

    1996-12-31

    A case study of soil contamination assessment and clean-up in a site proposed for residential development is presented in this paper. The contamination consists mainly of heavy metals of which lead is the most important contaminant. The site has been sampled on an approximately 25 x 25 square meter grid to between 1 and 3 meters depth to evaluate the extent of the contamination. Three hotspots were identified based on eyeballing the lead sample values and a crude contouring. A geostatistical approach is proposed to map the lead contamination and provide an alternate evaluation. The results suggest a significantly different evaluation of the area for clean-up based on the probability of the lead concentration exceeding allowable levels.

  8. A geostatistical extreme-value framework for fast simulation of natural hazard events.

    PubMed

    Youngman, Benjamin D; Stephenson, David B

    2016-05-01

    We develop a statistical framework for simulating natural hazard events that combines extreme value theory and geostatistics. Robust generalized additive model forms represent generalized Pareto marginal distribution parameters while a Student's t-process captures spatial dependence and gives a continuous-space framework for natural hazard event simulations. Efficiency of the simulation method allows many years of data (typically over 10 000) to be obtained at relatively little computational cost. This makes the model viable for forming the hazard module of a catastrophe model. We illustrate the framework by simulating maximum wind gusts for European windstorms, which are found to have realistic marginal and spatial properties, and validate well against wind gust measurements.

  9. Soil Moisture Estimation by Assimilating L-Band Microwave Brightness Temperature with Geostatistics and Observation Localization

    PubMed Central

    Han, Xujun; Li, Xin; Rigon, Riccardo; Jin, Rui; Endrizzi, Stefano

    2015-01-01

    The observation could be used to reduce the model uncertainties with data assimilation. If the observation cannot cover the whole model area due to spatial availability or instrument ability, how to do data assimilation at locations not covered by observation? Two commonly used strategies were firstly described: One is covariance localization (CL); the other is observation localization (OL). Compared with CL, OL is easy to parallelize and more efficient for large-scale analysis. This paper evaluated OL in soil moisture profile characterizations, in which the geostatistical semivariogram was used to fit the spatial correlated characteristics of synthetic L-Band microwave brightness temperature measurement. The fitted semivariogram model and the local ensemble transform Kalman filter algorithm are combined together to weight and assimilate the observations within a local region surrounding the grid cell of land surface model to be analyzed. Six scenarios were compared: 1_Obs with one nearest observation assimilated, 5_Obs with no more than five nearest local observations assimilated, and 9_Obs with no more than nine nearest local observations assimilated. The scenarios with no more than 16, 25, and 36 local observations were also compared. From the results we can conclude that more local observations involved in assimilation will improve estimations with an upper bound of 9 observations in this case. This study demonstrates the potentials of geostatistical correlation representation in OL to improve data assimilation of catchment scale soil moisture using synthetic L-band microwave brightness temperature, which cannot cover the study area fully in space due to vegetation effects. PMID:25635771

  10. Geostatistical Modeling of Uncertainty Attached to the Spatial Distribution of Arsenic in Groundwater of Southeast Michigan

    NASA Astrophysics Data System (ADS)

    Goovaerts, P.; Avruskin, G.; Meliker, J.; Slotnick, M.; Jacquez, G.; Nriagu, J.

    2003-12-01

    Assessment of the health risks associated with exposure to elevated levels of arsenic in drinking water has become the subject of considerable interest and some controversy in both regulatory and public health communities. The objective of this research is to explore the factors that have contributed to the observed geographic co-clustering in bladder cancer mortality and arsenic concentrations in drinking water in Michigan. A corner stone is the building of a probabilistic space-time model of arsenic concentrations, accounting for information collected at private residential wells and the hydrogeochemistry of the area. Because of the small changes in concentration observed in time, the study has focused on the spatial variability of arsenic, which can be considerable over very short distances. Various geostatistical techniques, based either on lognormal or indicator transforms of the data to accommodate the highly skewed distribution, have been compared using a cross validation procedure. The most promising approach involves a soft indicator coding of arsenic measurements, which allows one to account for data below the detection limit and the magnitude of measurement errors. Prior probabilities of exceeding various arsenic thresholds are also derived from secondary information, such as type of bedrock and surficial material, well casing depth, using logistic regression. Both well and secondary data are combined using kriging, leading to a non-parametric assessment of the uncertainty attached to arsenic concentration at each node of a 500m grid. This geostatistical model can be used to map either the expected arsenic concentration, the probability that it exceeds any giventhreshold, or the variance of the prediction indicating where supplementary information should be collected. The accuracy and precision of these local probability distributions is assessed using cross validation.

  11. Soil moisture estimation by assimilating L-band microwave brightness temperature with geostatistics and observation localization.

    PubMed

    Han, Xujun; Li, Xin; Rigon, Riccardo; Jin, Rui; Endrizzi, Stefano

    2015-01-01

    The observation could be used to reduce the model uncertainties with data assimilation. If the observation cannot cover the whole model area due to spatial availability or instrument ability, how to do data assimilation at locations not covered by observation? Two commonly used strategies were firstly described: One is covariance localization (CL); the other is observation localization (OL). Compared with CL, OL is easy to parallelize and more efficient for large-scale analysis. This paper evaluated OL in soil moisture profile characterizations, in which the geostatistical semivariogram was used to fit the spatial correlated characteristics of synthetic L-Band microwave brightness temperature measurement. The fitted semivariogram model and the local ensemble transform Kalman filter algorithm are combined together to weight and assimilate the observations within a local region surrounding the grid cell of land surface model to be analyzed. Six scenarios were compared: 1_Obs with one nearest observation assimilated, 5_Obs with no more than five nearest local observations assimilated, and 9_Obs with no more than nine nearest local observations assimilated. The scenarios with no more than 16, 25, and 36 local observations were also compared. From the results we can conclude that more local observations involved in assimilation will improve estimations with an upper bound of 9 observations in this case. This study demonstrates the potentials of geostatistical correlation representation in OL to improve data assimilation of catchment scale soil moisture using synthetic L-band microwave brightness temperature, which cannot cover the study area fully in space due to vegetation effects.

  12. Bayesian Geostatistical Analysis and Ecoclimatic Determinants of Corynebacterium pseudotuberculosis Infection among Horses

    PubMed Central

    Boysen, Courtney; Davis, Elizabeth G.; Beard, Laurie A.; Lubbers, Brian V.; Raghavan, Ram K.

    2015-01-01

    Kansas witnessed an unprecedented outbreak in Corynebacterium pseudotuberculosis infection among horses, a disease commonly referred to as pigeon fever during fall 2012. Bayesian geostatistical models were developed to identify key environmental and climatic risk factors associated with C. pseudotuberculosis infection in horses. Positive infection status among horses (cases) was determined by positive test results for characteristic abscess formation, positive bacterial culture on purulent material obtained from a lanced abscess (n = 82), or positive serologic evidence of exposure to organism (≥1:512)(n = 11). Horses negative for these tests (n = 172)(controls) were considered free of infection. Information pertaining to horse demographics and stabled location were obtained through review of medical records and/or contact with horse owners via telephone. Covariate information for environmental and climatic determinants were obtained from USDA (soil attributes), USGS (land use/land cover), and NASA MODIS and NASA Prediction of Worldwide Renewable Resources (climate). Candidate covariates were screened using univariate regression models followed by Bayesian geostatistical models with and without covariates. The best performing model indicated a protective effect for higher soil moisture content (OR = 0.53, 95% CrI = 0.25, 0.71), and detrimental effects for higher land surface temperature (≥35°C) (OR = 2.81, 95% CrI = 2.21, 3.85) and habitat fragmentation (OR = 1.31, 95% CrI = 1.27, 2.22) for C. pseudotuberculosis infection status in horses, while age, gender and breed had no effect. Preventative and ecoclimatic significance of these findings are discussed. PMID:26473728

  13. Prediction of regional flow duration curves: geostatistical techniques versus multivariate regression

    NASA Astrophysics Data System (ADS)

    Pugliese, A.; Farmer, W. H.; Castellarin, A.; Archfield, S. A.; Vogel, R. M.

    2015-12-01

    A period-of-record flow duration curve (FDC) represents the relationship between the magnitude and frequency of daily streamflows. Prediction of FDCs in ungauged basins is of great importance in those locations characterized by sparse, or more often missing, streamflow observations. We present a detailed comparison of two approaches which are capable of predicting an FDC in ungauged basins. An adaptation of the geostatistical method Top-kriging employs a linear weighted average of dimensionless empirical FDCs, standardized for a reference streamflow value. Weights are the result of the application of Top-kriging over a point index which, empirically, expresses the similarity between curves. Dimensional FDCs are then reconstructed developing a similar Top-kriging-based model capable of predicting the reference streamflow in the same sites. The second method is based on regional multiple linear regressions and is one of the most common method for prediction of FDCs in ungauged sites. Comparisons of these two methods are made at 182, mostly unregulated, river catchments in the southeastern U.S. using a three-fold cross-validation algorithm. Our results reveal that the two methods perform very similarly throughout flow-regimes, showing average Nash-Sutcliffe Efficiencies of 0.566 and 0.662 in natural scale, while 0.883 and 0.829 in log-transformed scale, for the geostatistical and the linear regression models, respectively. However, some complementarities are shown in the very low-flow regime, i.e. duration greater than 0.95, where the two models highlight different behaviors whether considering natural or log-transformed streamflows.

  14. Bayesian Geostatistical Analysis and Ecoclimatic Determinants of Corynebacterium pseudotuberculosis Infection among Horses.

    PubMed

    Boysen, Courtney; Davis, Elizabeth G; Beard, Laurie A; Lubbers, Brian V; Raghavan, Ram K

    2015-01-01

    Kansas witnessed an unprecedented outbreak in Corynebacterium pseudotuberculosis infection among horses, a disease commonly referred to as pigeon fever during fall 2012. Bayesian geostatistical models were developed to identify key environmental and climatic risk factors associated with C. pseudotuberculosis infection in horses. Positive infection status among horses (cases) was determined by positive test results for characteristic abscess formation, positive bacterial culture on purulent material obtained from a lanced abscess (n = 82), or positive serologic evidence of exposure to organism (≥ 1:512)(n = 11). Horses negative for these tests (n = 172)(controls) were considered free of infection. Information pertaining to horse demographics and stabled location were obtained through review of medical records and/or contact with horse owners via telephone. Covariate information for environmental and climatic determinants were obtained from USDA (soil attributes), USGS (land use/land cover), and NASA MODIS and NASA Prediction of Worldwide Renewable Resources (climate). Candidate covariates were screened using univariate regression models followed by Bayesian geostatistical models with and without covariates. The best performing model indicated a protective effect for higher soil moisture content (OR = 0.53, 95% CrI = 0.25, 0.71), and detrimental effects for higher land surface temperature (≥ 35°C) (OR = 2.81, 95% CrI = 2.21, 3.85) and habitat fragmentation (OR = 1.31, 95% CrI = 1.27, 2.22) for C. pseudotuberculosis infection status in horses, while age, gender and breed had no effect. Preventative and ecoclimatic significance of these findings are discussed.

  15. Interventions for physical activity promotion applied to the primary healthcare settings for people living in regions of low socioeconomic level: study protocol for a non-randomized controlled trial

    PubMed Central

    2014-01-01

    Background Regular physical activity practice has been widely recommended for promoting health, but the physical activity levels remain low in the population. Therefore, the study of interventions to promote physical activity is essential. Objective: To present the methodology of two physical activity interventions from the “Ambiente Ativo” (“Active Environment”) project. Methods 12-month non-randomized controlled intervention trial. 157 healthy and physically inactive individuals were selected: health education (n = 54) supervised exercise (n = 54) and control (n = 49). Intervention based on health education: a multidisciplinary team of health professionals organized the intervention in group discussions, phone calls, SMS and educational material. Intervention based on supervised exercise program: consisted of offering an exercise program in groups supervised by physical education professionals involving strength, endurance and flexibility exercises. The physical activity level was assessed by the International Physical Activity Questionnaire (long version), physical activities recalls, pedometers and accelerometers over a seven-day period. Result This study described two different proposals for promoting physical activity that were applied to adults attended through the public healthcare settings. The participants were living in a region of low socioeconomic level, while respecting the characteristics and organization of the system and its professionals, and also adapting the interventions to the realities of the individuals attended. Conclusion Both interventions are applicable in regions of low socioeconomic level, while respecting the social and economic characteristics of each region. Trial registration ClinicalTrials.gov NCT01852981 PMID:24624930

  16. 0.6-1.0 V operation set/reset voltage (3 V) generator for three-dimensional integrated resistive random access memory and NAND flash hybrid solid-state drive

    NASA Astrophysics Data System (ADS)

    Tanaka, Masahiro; Hachiya, Shogo; Ishii, Tomoya; Ning, Sheyang; Tsurumi, Kota; Takeuchi, Ken

    2016-04-01

    A 0.6-1.0 V, 25.9 mm2 boost converter is proposed to generate resistive random access memory (ReRAM) write (set/reset) voltage for three-dimensional (3D) integrated ReRAM and NAND flash hybrid solid-state drive (SSD). The proposed boost converter uses an integrated area-efficient V BUF generation circuit to obtain short ReRAM sector write time, small circuit size, and small energy consumption simultaneously. In specific, the proposed boost converter reduces ReRAM sector write time by 65% compared with a conventional one-stage boost converter (Conventional 1) which uses 1.0 V operating voltage. On the other hand, by using the same ReRAM sector write time, the proposed boost converter reduces 49% circuit area and 46% energy consumption compared with a conventional two-stage boost converter (Conventional 2). In addition, by using the proposed boost converter, the operating voltage, V DD, can be reduced to 0.6 V. The lowest 159 nJ energy consumption can be obtained when V DD is 0.7 V.

  17. Geostatistical analysis of disease data: visualization and propagation of spatial uncertainty in cancer mortality risk using Poisson kriging and p-field simulation

    PubMed Central

    Goovaerts, Pierre

    2006-01-01

    Background Smoothing methods have been developed to improve the reliability of risk cancer estimates from sparsely populated geographical entities. Filtering local details of the spatial variation of the risk leads however to the detection of larger clusters of low or high cancer risk while most spatial outliers are filtered out. Static maps of risk estimates and the associated prediction variance also fail to depict the uncertainty attached to the spatial distribution of risk values and does not allow its propagation through local cluster analysis. This paper presents a geostatistical methodology to generate multiple realizations of the spatial distribution of risk values. These maps are then fed into spatial operators, such as in local cluster analysis, allowing one to assess how risk spatial uncertainty translates into uncertainty about the location of spatial clusters and outliers. This novel approach is applied to age-adjusted breast and pancreatic cancer mortality rates recorded for white females in 295 US counties of the Northeast (1970–1994). A public-domain executable with example datasets is provided. Results Geostatistical simulation generates risk maps that are more variable than the smooth risk map estimated by Poisson kriging and reproduce better the spatial pattern captured by the risk semivariogram model. Local cluster analysis of the set of simulated risk maps leads to a clear visualization of the lower reliability of the classification obtained for pancreatic cancer versus breast cancer: only a few counties in the large cluster of low risk detected in West Virginia and Southern Pennsylvania are significant over 90% of all simulations. On the other hand, the cluster of high breast cancer mortality in Niagara county, detected after application of Poisson kriging, appears on 60% of simulated risk maps. Sensitivity analysis shows that 500 realizations are needed to achieve a stable classification for pancreatic cancer, while convergence is reached

  18. Transdiagnostic Cognitive Behavioral Therapy Versus Treatment as Usual in Adult Patients With Emotional Disorders in the Primary Care Setting (PsicAP Study): Protocol for a Randomized Controlled Trial

    PubMed Central

    Muñoz-Navarro, Roger; Wood, Cristina Mae; Limonero, Joaquín T; Medrano, Leonardo Adrián; Ruiz-Rodríguez, Paloma; Gracia-Gracia, Irene; Dongil-Collado, Esperanza; Iruarrizaga, Iciar; Chacón, Fernando; Santolaya, Francisco

    2016-01-01

    Background Demand for primary care (PC) services in Spain exceeds available resources. Part of this strong demand is due to the high prevalence of emotional disorders (EDs)—anxiety, depression, and somatic symptom disorders—and related comorbidities such as pain or chronic illnesses. EDs are often under- or misdiagnosed by general practitioners (GPs) and, consequently, treatment is frequently inadequate. Objective We aim to compare the short- and long-term effectiveness of group-delivered transdiagnostic cognitive behavioral therapy (TD-CBT) versus treatment as usual (TAU) in the treatment of EDs in the PC setting in Spain. We also aim to compare the effect of these treatments on disability, quality of life, cognitive-emotional factors, and treatment satisfaction. Methods Here we present the study design of a two-arm, single-blind, randomized controlled trial (N=1126) to compare TAU to TD-CBT for EDs. TAU will consist primarily of pharmacological treatment and practical advice from the GP while TD-CBT will be administered in seven 90-minute group sessions held over a period ranging from 12 to 14 weeks. Psychological assessments are carried out at baseline (ie, pretreatment); posttreatment; and at 3-, 6-, and 12-month follow-up. The study is conducted in approximately 26 PC centers from the National Health System in Spain. Results This study was initiated in December 2013 and will remain open to new participants until recruitment and follow-up has been completed. We expect all posttreatment evaluations to be completed by December 2017, and follow-up will end in December 2018. Conclusions We expect the TD-CBT group to have better results compared to TAU on all posttreatment measures and that this improvement will be maintained during follow-up. This project could serve as a model for use in other areas or services of the National Health System in Spain and even in other countries. ClinicalTrial International Standard Randomized Controlled Trial Number (ISRCTN

  19. Comparison of kriging and cokriging for the geostatistical estimation of specific capacity in the Newark Basin (NJ) aquifer system.

    PubMed

    Carter, Gail P; Miskewitz, Robert J; Isukapalli, Sastry; Mun, Yuri; Vyas, Vikram; Yoon, Sungwon; Georgeopoulos, Panos; Uchrin, Christopher G

    2011-01-01

    Groundwater is a major water source in New Jersey; hence, accurate hydrogeologic data are extremely important. However, most measured data have inadequate spatial density and their locations are often clustered. Our study focuses on implementing geostatistical methods to generate the spatial distribution of specific capacity over the Newark Basin in New Jersey. Two geostatistical methods, ordinary kriging and cokriging, were employed and compared. Ordinary kriging was employed to estimate the spatial distribution of specific capacity by using measured values. Cokriging incorporated the spatial variability of fracture density into the estimation with the spatial variability of specific capacity, as groundwater flow in fractured rock aquifers depends on the fracture characteristics in the Newark Basin. Results indicate that cokriging manifested substantial improvements over ordinary kriging including a larger areal coverage, a more detailed variation of specific capacity, and reduction in the variance of its estimates.

  20. Multicenter prospective randomized study comparing the technique of using a bovine pericardium biological prosthesis reinforcement in parietal herniorrhaphy (Tutomesh TUTOGEN) with simple parietal herniorrhaphy, in a potentially contaminated setting.

    PubMed

    Nedelcu, Marius; Verhaeghe, Pierre; Skalli, Mehdi; Champault, Gerard; Barrat, Christophe; Sebbag, Hugues; Reche, Fabian; Passebois, Laurent; Beyrne, Daniel; Gugenheim, Jean; Berdah, Stephane; Bouayed, Amine; Michel Fabre, Jean; Nocca, David

    2016-03-01

    The use of parietal synthetic prosthetic reinforcement material in potentially contaminated settings is not recommended, as there is a risk that the prosthesis may become infected. Thus, simple parietal herniorrhaphy, is the conventional treatment, even though there is a significant risk that the hernia may recur. Using new biomaterials of animal origin presently appears to offer a new therapeutic solution, but their effectiveness has yet to be demonstrated. The purpose of this multicenter prospective randomized single-blind study was to compare the surgical treatment of inguinal hernia or abdominal incisional hernia by simple parietal herniorrhaphy without prosthetic reinforcement (Group A), with Tutomesh TUTOGEN biological prosthesis reinforcement parietal herniorrhaphy (Group B), in a potentially contaminated setting. We examined early postoperative complications in the first month after the operation, performed an assessment after one year of survival without recurrence and analyzed the quality of life and pain of the patients (using SF-12 health status questionnaire and Visual Analog Pain Scale) at 1, 6, and 12 months, together with an economic impact study. Hundred and thirty four patients were enrolled between January 2009 and October 2010 in 20 French hospitals. The groups were comparable with respect to their enrollment characteristics, their history, types of operative indications and procedures carried out. At one month post-op, the rate of infectious complications (n(A) = 11(18.33%) vs. n(B) = 12(19.05%), p = 0.919) was not significantly different between the two groups. The assessment after one year of survival without recurrence revealed that survival was significantly greater in Group B (Group A recurrence: 10, Group B: 3; p = 0.0475). No difference in the patients' quality of life was demonstrated at 1, 6, or 12 months. However, at the 1 month follow-up, the "perceived health" rating seemed better in the group with Tutomesh (p

  1. Dentists United to Extinguish Tobacco (DUET): a study protocol for a cluster randomized, controlled trial for enhancing implementation of clinical practice guidelines for treating tobacco dependence in dental care settings

    PubMed Central

    2014-01-01

    Background Although dental care settings provide an exceptional opportunity to reach smokers and provide brief cessation advice and treatment to reduce oral and other tobacco-related health conditions, dental care providers demonstrate limited adherence to evidence-based guidelines for treatment of tobacco use and dependence. Methods/Design Guided by a multi-level, conceptual framework that emphasizes changes in provider beliefs and organizational characteristics as drivers of improvement in tobacco treatment delivery, the current protocol will use a cluster, randomized design and multiple data sources (patient exit interviews, provider surveys, site observations, chart audits, and semi-structured provider interviews) to study the process of implementing clinical practice guidelines for treating tobacco dependence in 18 public dental care clinics in New York City. The specific aims of this comparative-effectiveness research trial are to: compare the effectiveness of three promising strategies for implementation of tobacco use treatment guidelines—staff training and current best practices (CBP), CBP + provider performance feedback (PF), and CBP + PF + provider reimbursement for delivery of tobacco cessation treatment (pay-for-performance, or P4P); examine potential theory-driven mechanisms hypothesized to explain the comparative effectiveness of three strategies for implementation; and identify baseline organizational factors that influence the implementation of evidence-based tobacco use treatment practices in dental clinics. The primary outcome is change in providers’ tobacco treatment practices and the secondary outcomes are cost per quit, use of tobacco cessation treatments, quit attempts, and smoking abstinence. Discussion We hypothesize that the value of these promising implementation strategies is additive and that incorporating all three strategies (CBP, PF, and P4P) will be superior to CBP alone and CBP + PF in improving delivery of

  2. Fine-grained sediment spatial distribution on the basis of a geostatistical analysis: Example of the eastern Bay of the Seine (France)

    NASA Astrophysics Data System (ADS)

    Méar, Y.; Poizot, E.; Murat, A.; Lesueur, P.; Thomas, M.

    2006-12-01

    The eastern Bay of the Seine (English Channel) was the subject in 1991 of a sampling survey of superficial sediments. Geostatistic tools were used to examine the complexity of the spatial distribution of the fine-grained fraction (<50 μm). A central depocentre of fine sediments (i.e. content up to 50%) oriented in a NW-SE direction in a muddy coastal strip, in a very high energy hydrodynamical situation due to storm swells and its megatidal setting, is for the first time recognised and discussed. Within this sedimentary unit, the distribution of the fine fraction is very heterogeneous, with mud patches of less than 4000 m diameter; the boundary between these mud patches and their substratum is very sharp. The distribution of this fine fraction appears to be controlled by an anticyclonic eddy located off the Pays de Caux. Under the influence of this, the suspended material expelled from the Seine estuary moves along the coast and swings off Antifer harbour, towards the NW. It is trapped within this eddy because of the settling of suspended particulate matter. Both at a general scale and a local scale the morphology (whether inherited or due to modern processes) has a strong influence on the spatial distribution of the fine fraction. At the general scale, the basin-like shape of the area facilitates the silting, and the presence of the submarine dunes, called "Ridins d'Antifer", clearly determines the northern limit of the muddy zone. At a local scale, the same influence is obvious: paleovalleys trap the fine sediments, whereas isolated sand dunes and ripples limit the silting. This duality of role of the morphology is therefore one of the reasons why the muddy surface is extremely heterogeneous spatially. The presence of an important population of suspension feeding echinoderm, the brittle-star Ophiothrix fragilis Abildgaard, has led to a local increase in the silting, and to the modification of the physicochemical and sedimentological parameters. A complex

  3. Mapping in random-structures

    SciTech Connect

    Reidys, C.M.

    1996-06-01

    A mapping in random-structures is defined on the vertices of a generalized hypercube Q{sub {alpha}}{sup n}. A random-structure will consist of (1) a random contact graph and (2) a family of relations imposed on adjacent vertices. The vertex set of a random contact graph will be the set of all coordinates of a vertex P {element_of} Q{sub {alpha}}{sup n}. Its edge will be the union of the edge sets of two random graphs. The first is a random 1-regular graph on 2m vertices (coordinates) and the second is a random graph G{sub p} with p = c{sub 2}/n on all n vertices (coordinates). The structure of the random contact graphs will be investigated and it will be shown that for certain values of m, c{sub 2} the mapping in random-structures allows to search by the set of random-structures. This is applied to mappings in RNA-secondary structures. Also, the results on random-structures might be helpful for designing 3D-folding algorithms for RNA.

  4. The dynamics and interaction of compaction bands in Valley of Fire State Park, Nevada (USA): Implications for their growth, evolution, and geostatistical property

    NASA Astrophysics Data System (ADS)

    Torabi, A.; Aydin, A.; Cilona, A.; Jarstø, B. E.; Deng, S.

    2015-08-01

    Geometry, geostatistics and microstructures of two sets of high-angle compaction bands within Aztec Sandstone are investigated for the purpose of characterizing their dimensions, distributions, and growth dynamics. Both sets generally terminate at the dune boundaries. The first set of compaction bands is longer than the second set, which is thought to be controlled by the depositional architecture. The macroscopic thickness of single compaction bands varies from zero at their tips to maximum values, 3 to 10 mm commonly within the central parts. The compaction bands often cluster into zones, the thickness of which increases by addition of a new band sub-parallel to the previously formed bands. The average thickness of a given zone does not change significantly except for a limited number of places along their lengths and a high thickness/distance gradient at their ends. The high thickness anomalies in the thickness distribution plots along the zones correspond to local complexities such as eye structures, steps, and intersections of converging or diverging bands and may reach to about 35 cm. Microstructural analysis of single compaction bands highlights the presence of two typical portions, a thin portion and a thick portion. The thin portion of the band contains pockets of tightly packed grains surrounded by large pores in the band. The thick portion is characterized by an almost homogeneously distributed compaction throughout the band. The thin portions are found close to the tips, whereas the thick portions occur primarily in the central parts of the bands. Two other characteristics of both single bands and zones of bands in high resolution images are that their thicknesses appear to be smaller and that their lateral boundaries are unexpectedly rough with respect to their appearance in outcrops and hand samples, the latter of which may play an important role in their lateral growth.

  5. The XXZ Heisenberg model on random surfaces

    NASA Astrophysics Data System (ADS)

    Ambjørn, J.; Sedrakyan, A.

    2013-09-01

    We consider integrable models, or in general any model defined by an R-matrix, on random surfaces, which are discretized using random Manhattan lattices. The set of random Manhattan lattices is defined as the set dual to the lattice random surfaces embedded on a regular d-dimensional lattice. They can also be associated with the random graphs of multiparticle scattering nodes. As an example we formulate a random matrix model where the partition function reproduces the annealed average of the XXZ Heisenberg model over all random Manhattan lattices. A technique is presented which reduces the random matrix integration in partition function to an integration over their eigenvalues.

  6. Multivariate Analysis and Modeling of Sediment Pollution Using Neural Network Models and Geostatistics

    NASA Astrophysics Data System (ADS)

    Golay, Jean; Kanevski, Mikhaïl

    2013-04-01

    The present research deals with the exploration and modeling of a complex dataset of 200 measurement points of sediment pollution by heavy metals in Lake Geneva. The fundamental idea was to use multivariate Artificial Neural Networks (ANN) along with geostatistical models and tools in order to improve the accuracy and the interpretability of data modeling. The results obtained with ANN were compared to those of traditional geostatistical algorithms like ordinary (co)kriging and (co)kriging with an external drift. Exploratory data analysis highlighted a great variety of relationships (i.e. linear, non-linear, independence) between the 11 variables of the dataset (i.e. Cadmium, Mercury, Zinc, Copper, Titanium, Chromium, Vanadium and Nickel as well as the spatial coordinates of the measurement points and their depth). Then, exploratory spatial data analysis (i.e. anisotropic variography, local spatial correlations and moving window statistics) was carried out. It was shown that the different phenomena to be modeled were characterized by high spatial anisotropies, complex spatial correlation structures and heteroscedasticity. A feature selection procedure based on General Regression Neural Networks (GRNN) was also applied to create subsets of variables enabling to improve the predictions during the modeling phase. The basic modeling was conducted using a Multilayer Perceptron (MLP) which is a workhorse of ANN. MLP models are robust and highly flexible tools which can incorporate in a nonlinear manner different kind of high-dimensional information. In the present research, the input layer was made of either two (spatial coordinates) or three neurons (when depth as auxiliary information could possibly capture an underlying trend) and the output layer was composed of one (univariate MLP) to eight neurons corresponding to the heavy metals of the dataset (multivariate MLP). MLP models with three input neurons can be referred to as Artificial Neural Networks with EXternal

  7. Near-Infrared Spectroscopy and Geostatistical Analysis for Modeling Spatial Distribution of Analytical Constituents in Bulk Animal By-Product Protein Meals.

    PubMed

    Adame-Siles, José A; Fearn, Tom; Guerrero-Ginel, José E; Garrido-Varo, Ana; Maroto-Molina, Francisco; Pérez-Marín, Dolores

    2017-03-01

    Control and inspection operations within the context of safety and quality assessment of bulk foods and feeds are not only of particular importance, they are also demanding challenges, given the complexity of food/feed production systems and the variability of product properties. Existing methodologies have a variety of limitations, such as high costs of implementation per sample or shortcomings in early detection of potential threats for human/animal health or quality deviations. Therefore, new proposals are required for the analysis of raw materials in situ in a more efficient and cost-effective manner. For this purpose, a pilot laboratory study was performed on a set of bulk lots of animal by-product protein meals to introduce and test an approach based on near-infrared (NIR) spectroscopy and geostatistical analysis. Spectral data, provided by a fiber optic probe connected to a Fourier transform (FT) NIR spectrometer, were used to predict moisture and crude protein content at each sampling point. Variographic analysis was carried out for spatial structure characterization, while ordinary Kriging achieved continuous maps for those parameters. The results indicated that the methodology could be a first approximation to an approach that, properly complemented with the Theory of Sampling and supported by experimental validation in real-life conditions, would enhance efficiency and the decision-making process regarding safety and adulteration issues.

  8. Framework for the mapping of the monthly average daily solar radiation using an advanced case-based reasoning and a geostatistical technique.

    PubMed

    Lee, Minhyun; Koo, Choongwan; Hong, Taehoon; Park, Hyo Seon

    2014-04-15

    For the effective photovoltaic (PV) system, it is necessary to accurately determine the monthly average daily solar radiation (MADSR) and to develop an accurate MADSR map, which can simplify the decision-making process for selecting the suitable location of the PV system installation. Therefore, this study aimed to develop a framework for the mapping of the MADSR using an advanced case-based reasoning (CBR) and a geostatistical technique. The proposed framework consists of the following procedures: (i) the geographic scope for the mapping of the MADSR is set, and the measured MADSR and meteorological data in the geographic scope are collected; (ii) using the collected data, the advanced CBR model is developed; (iii) using the advanced CBR model, the MADSR at unmeasured locations is estimated; and (iv) by applying the measured and estimated MADSR data to the geographic information system, the MADSR map is developed. A practical validation was conducted by applying the proposed framework to South Korea. It was determined that the MADSR map developed through the proposed framework has been improved in terms of accuracy. The developed MADSR map can be used for estimating the MADSR at unmeasured locations and for determining the optimal location for the PV system installation.

  9. Indoor terrestrial gamma dose rate mapping in France: a case study using two different geostatistical models.

    PubMed

    Warnery, E; Ielsch, G; Lajaunie, C; Cale, E; Wackernagel, H; Debayle, C; Guillevic, J

    2015-01-01

    Terrestrial gamma dose rates show important spatial variations in France. Previous studies resulted in maps of arithmetic means of indoor terrestrial gamma dose rates by "departement" (French district). However, numerous areas could not be characterized due to the lack of data. The aim of our work was to obtain more precise estimates of the spatial variability of indoor terrestrial gamma dose rates in France by using a more recent and complete data base and geostatistics. The study was based on the exploitation of 97,595 measurements results distributed in 17,404 locations covering all of France. Measurements were done by the Institute for Radioprotection and Nuclear Safety (IRSN) using RPL (Radio Photo Luminescent) dosimeters, exposed during several months between years 2011 and 2012 in French dentist surgeries and veterinary clinics. The data used came from dosimeters which were not exposed to anthropic sources. After removing the cosmic rays contribution in order to study only the telluric gamma radiation, it was decided to work with the arithmetic means of the time-series measurements, weighted by the time-exposure of the dosimeters, for each location. The values varied between 13 and 349 nSv/h, with an arithmetic mean of 76 nSv/h. The observed statistical distribution of the gamma dose rates was skewed to the right. Firstly, ordinary kriging was performed in order to predict the gamma dose rate on cells of 1*1 km(2), all over the domain. The second step of the study was to use an auxiliary variable in estimates. The IRSN achieved in 2010 a classification of the French geological formations, characterizing their uranium potential on the bases of geology and local measurement results of rocks uranium content. This information is georeferenced in a map at the scale 1:1,000,000. The geological uranium potential (GUP) was classified in 5 qualitative categories. As telluric gamma rays mostly come from the progenies of the (238)Uranium series present in rocks, this

  10. A multivariate geostatistical methodology to delineate areas of potential interest for future sedimentary gold exploration

    PubMed Central

    Goovaerts, P.; Albuquerque, Teresa; Antunes, Margarida

    2015-01-01

    This paper describes a multivariate geostatistical methodology to delineate areas of potential interest for future sedimentary gold exploration, with an application to an abandoned sedimentary gold mining region in Portugal. The main challenge was the existence of only a dozen gold measurements confined to the grounds of the old gold mines, which precluded the application of traditional interpolation techniques, such as cokriging. The analysis could, however, capitalize on 376 stream sediment samples that were analyzed for twenty two elements. Gold (Au) was first predicted at all 376 locations using linear regression (R2=0.798) and four metals (Fe, As, Sn and W), which are known to be mostly associated with the local gold’s paragenesis. One hundred realizations of the spatial distribution of gold content were generated using sequential indicator simulation and a soft indicator coding of regression estimates, to supplement the hard indicator coding of gold measurements. Each simulated map then underwent a local cluster analysis to identify significant aggregates of low or high values. The one hundred classified maps were processed to derive the most likely classification of each simulated node and the associated probability of occurrence. Examining the distribution of the hot-spots and cold-spots reveals a clear enrichment in Au along the Erges River downstream from the old sedimentary mineralization. PMID:27777638

  11. Spatial Analysis of Metal Profiles in Sediments in a Tropical Estuary: A Geostatistical Approach.

    PubMed

    Vázquez-Sauceda, María de la Luz; Pérez-Castañeda, Roberto; Sánchez-Martínez, Jesús Genaro; Rábago-Castro, Jaime Luis

    2015-11-01

    The spatial structure and distribution of heavy metals [cadmium (Cd), copper (Cu), iron (Fe), lead (Pb), and nickel (Ni)] in sediments were geostatistically analyzed along the estuarine ecosystem of Tigre River-San Andres Lagoon (Tamaulipas, Mexico). In most cases, heavy-metal concentrations exhibited a strong spatial autocorrelation along the estuary as indicated by variogram analysis. Heavy-metal concentrations were found to be higher in the middle estuary, close to the mouth of the Tigre River, and declined as distance from the mouth increased. Metal mean levels at the middle estuary were 2.41 mg/kg Cd, 4.80 mg/kg Cu, 172.36 mg/kg Fe, 5.22 mg/kg Pb, and 2.10 mg/kg Ni. The spatial distribution of heavy metals suggests the existence of a common heavy-metal source located in this area of the estuary. The importance of wastewater discharges and open dumping in the town of El Moron, adjacent to the mouth of the Tigre River, is highlighted; these are believed to be the anthropogenic sources for heavy metals in this estuarine ecosystem.

  12. Epidemiological Study of Hazelnut Bacterial Blight in Central Italy by Using Laboratory Analysis and Geostatistics

    PubMed Central

    Lamichhane, Jay Ram; Fabi, Alfredo; Ridolfi, Roberto; Varvaro, Leonardo

    2013-01-01

    Incidence of Xanthomonas arboricola pv. corylina, the causal agent of hazelnut bacterial blight, was analyzed spatially in relation to the pedoclimatic factors. Hazelnut grown in twelve municipalities situated in the province of Viterbo, central Italy was studied. A consistent number of bacterial isolates were obtained from the infected tissues of hazelnut collected in three years (2010–2012). The isolates, characterized by phenotypic tests, did not show any difference among them. Spatial patterns of pedoclimatic data, analyzed by geostatistics showed a strong positive correlation of disease incidence with higher values of rainfall, thermal shock and soil nitrogen; a weak positive correlation with soil aluminium content and a strong negative correlation with the values of Mg/K ratio. No correlation of the disease incidence was found with soil pH. Disease incidence ranged from very low (<1%) to very high (almost 75%) across the orchards. Young plants (4-year old) were the most affected by the disease confirming a weak negative correlation of the disease incidence with plant age. Plant cultivars did not show any difference in susceptibility to the pathogen. Possible role of climate change on the epidemiology of the disease is discussed. Improved management practices are recommended for effective control of the disease. PMID:23424654

  13. Geostatistics and the representative elementary volume of gamma ray tomography attenuation in rocks cores

    USGS Publications Warehouse

    Vogel, J.R.; Brown, G.O.

    2003-01-01

    Semivariograms of samples of Culebra Dolomite have been determined at two different resolutions for gamma ray computed tomography images. By fitting models to semivariograms, small-scale and large-scale correlation lengths are determined for four samples. Different semivariogram parameters were found for adjacent cores at both resolutions. Relative elementary volume (REV) concepts are related to the stationarity of the sample. A scale disparity factor is defined and is used to determine sample size required for ergodic stationarity with a specified correlation length. This allows for comparison of geostatistical measures and representative elementary volumes. The modifiable areal unit problem is also addressed and used to determine resolution effects on correlation lengths. By changing resolution, a range of correlation lengths can be determined for the same sample. Comparison of voxel volume to the best-fit model correlation length of a single sample at different resolutions reveals a linear scaling effect. Using this relationship, the range of the point value semivariogram is determined. This is the range approached as the voxel size goes to zero. Finally, these results are compared to the regularization theory of point variables for borehole cores and are found to be a better fit for predicting the volume-averaged range.

  14. Geostatistics as a tool to study mite dispersion in physic nut plantations.

    PubMed

    Rosado, J F; Picanço, M C; Sarmento, R A; Pereira, R M; Pedro-Neto, M; Galdino, T V S; de Sousa Saraiva, A; Erasmo, E A L

    2015-08-01

    Spatial distribution studies in pest management identify the locations where pest attacks on crops are most severe, enabling us to understand and predict the movement of such pests. Studies on the spatial distribution of two mite species, however, are rather scarce. The mites Polyphagotarsonemus latus and Tetranychus bastosi are the major pests affecting physic nut plantations (Jatropha curcas). Therefore, the objective of this study was to measure the spatial distributions of P. latus and T. bastosi in the physic nut plantations. Mite densities were monitored over 2 years in two different plantations. Sample locations were georeferenced. The experimental data were analyzed using geostatistical analyses. The total mite density was found to be higher when only one species was present (T. bastosi). When both the mite species were found in the same plantation, their peak densities occurred at different times. These mites, however, exhibited uniform spatial distribution when found at extreme densities (low or high). However, the mites showed an aggregated distribution in intermediate densities. Mite spatial distribution models were isotropic. Mite colonization commenced at the periphery of the areas under study, whereas the high-density patches extended until they reached 30 m in diameter. This has not been reported for J. curcas plants before.

  15. Effective use of field screening techniques in environmental investigations: A multivariate geostatistical approach

    SciTech Connect

    Wild, M.R.; Rouhani, S.

    1996-12-31

    Environmental investigations typically entail broad data gathering efforts which include field screening surveys and laboratory analyses. Although usually collected extensively, data from field screening surveys are rarely used in the actual delineation of media contamination. On the other hand, laboratory analyses, which are used in the delineation, are minimized to avoid potentially high cost. Multivariate geostatistical techniques, such as indicator cokriging, were employed to incorporate volatile organic screening and laboratory data in order to better estimate soil contamination concentrations at an underground storage tank site. In this work, the direct and cross variographies are based on a multi-scale approach. The results indicate that soil gas measurements show good correlations with laboratory data at large scales. These correlations however, can be masked by poor correlations at micro-scale distances. Consequently, a classical direct correlation analysis between the two measured values is very likely to fail. In contrast, the presented multi-scale co-estimation procedure provides tools for a cost-effective and reliable assessment of soil contamination based on a combined use of laboratory and field screening data.

  16. The applications of model-based geostatistics in helminth epidemiology and control.

    PubMed

    Magalhães, Ricardo J Soares; Clements, Archie C A; Patil, Anand P; Gething, Peter W; Brooker, Simon

    2011-01-01

    Funding agencies are dedicating substantial resources to tackle helminth infections. Reliable maps of the distribution of helminth infection can assist these efforts by targeting control resources to areas of greatest need. The ability to define the distribution of infection at regional, national and subnational levels has been enhanced greatly by the increased availability of good quality survey data and the use of model-based geostatistics (MBG), enabling spatial prediction in unsampled locations. A major advantage of MBG risk mapping approaches is that they provide a flexible statistical platform for handling and representing different sources of uncertainty, providing plausible and robust information on the spatial distribution of infections to inform the design and implementation of control programmes. Focussing on schistosomiasis and soil-transmitted helminthiasis, with additional examples for lymphatic filariasis and onchocerciasis, we review the progress made to date with the application of MBG tools in large-scale, real-world control programmes and propose a general framework for their application to inform integrative spatial planning of helminth disease control programmes.

  17. Geostatistical simulation of rock quality designation (RQD) to support facilities design at Yucca Mountain, Nevada

    SciTech Connect

    Cromer, M.V.; Zelinski, W.P.

    1996-12-31

    The conceptual design of the proposed Yucca Mountain nuclear waste repository facility includes shafts and ramps as access to the repository horizon, located 200 to 400 m below ground surface. Geostatistical simulation techniques are being employed to produce numerical models of selected material properties (rock characteristics) in their proper spatial positions. These numerical models will be used to evaluate behavior of various engineered features, the effects of construction and operating practices, and the waste-isolation performance of the overall repository system. The work presented here represents the first attempt to evaluate the spatial character of the rock strength index known as rock quality designation (RQD). Although it is likely that RQD reflects an intrinsic component of the rock matrix, this component becomes difficult to resolve given the frequency and orientation of data made available from vertical core records. The constraints of the two-dimensional study along the axis of an exploratory drift allow bounds to be placed upon the resulting interpretations, while the use of an indicator transformation allows focus to be placed on specific details that may be of interest to design engineers. The analytical process and subsequent development of material property models is anticipated to become one of the principal means of summarizing, integrating, and reconciling the diverse suite of earth-science data acquired through site characterization and of recasting the data in formats specifically designed for use in further modeling of various physical processes.

  18. Using geostatistical methods to estimate snow water equivalence distribution in a mountain watershed

    USGS Publications Warehouse

    Balk, B.; Elder, K.; Baron, J.

    1998-01-01

    Knowledge of the spatial distribution of snow water equivalence (SWE) is necessary to adequately forecast the volume and timing of snowmelt runoff.  In April 1997, peak accumulation snow depth and density measurements were independently taken in the Loch Vale watershed (6.6 km2), Rocky Mountain National Park, Colorado.  Geostatistics and classical statistics were used to estimate SWE distribution across the watershed.  Snow depths were spatially distributed across the watershed through kriging interpolation methods which provide unbiased estimates that have minimum variances.  Snow densities were spatially modeled through regression analysis.  Combining the modeled depth and density with snow-covered area (SCA produced an estimate of the spatial distribution of SWE.  The kriged estimates of snow depth explained 37-68% of the observed variance in the measured depths.  Steep slopes, variably strong winds, and complex energy balance in the watershed contribute to a large degree of heterogeneity in snow depth.

  19. Epidemiological study of hazelnut bacterial blight in central Italy by using laboratory analysis and geostatistics.

    PubMed

    Lamichhane, Jay Ram; Fabi, Alfredo; Ridolfi, Roberto; Varvaro, Leonardo

    2013-01-01

    Incidence of Xanthomonas arboricola pv. corylina, the causal agent of hazelnut bacterial blight, was analyzed spatially in relation to the pedoclimatic factors. Hazelnut grown in twelve municipalities situated in the province of Viterbo, central Italy was studied. A consistent number of bacterial isolates were obtained from the infected tissues of hazelnut collected in three years (2010-2012). The isolates, characterized by phenotypic tests, did not show any difference among them. Spatial patterns of pedoclimatic data, analyzed by geostatistics showed a strong positive correlation of disease incidence with higher values of rainfall, thermal shock and soil nitrogen; a weak positive correlation with soil aluminium content and a strong negative correlation with the values of Mg/K ratio. No correlation of the disease incidence was found with soil pH. Disease incidence ranged from very low (<1%) to very high (almost 75%) across the orchards. Young plants (4-year old) were the most affected by the disease confirming a weak negative correlation of the disease incidence with plant age. Plant cultivars did not show any difference in susceptibility to the pathogen. Possible role of climate change on the epidemiology of the disease is discussed. Improved management practices are recommended for effective control of the disease.

  20. Spatial analysis of lettuce downy mildew using geostatistics and geographic information systems.

    PubMed

    Wu, B M; van Bruggen, A H; Subbarao, K V; Pennings, G G

    2001-02-01

    ABSTRACT The epidemiology of lettuce downy mildew has been investigated extensively in coastal California. However, the spatial patterns of the disease and the distance that Bremia lactucae spores can be transported have not been determined. During 1995 to 1998, we conducted several field- and valley-scale surveys to determine spatial patterns of this disease in the Salinas valley. Geostatistical analyses of the survey data at both scales showed that the influence range of downy mildew incidence at one location on incidence at other locations was between 80 and 3,000 m. A linear relationship was detected between semivariance and lag distance at the field scale, although no single statistical model could fit the semi-variograms at the valley scale. Spatial interpolation by the inverse distance weighting method with a power of 2 resulted in plausible estimates of incidence throughout the valley. Cluster analysis in geographic information systems on the interpolated disease incidence from different dates demonstrated that the Salinas valley could be divided into two areas, north and south of Salinas City, with high and low disease pressure, respectively. Seasonal and spatial trends along the valley suggested that the distinction between the downy mildew conducive and nonconducive areas might be determined by environmental factors.

  1. Stochastic simulation of geological data using isometric mapping and multiple-point geostatistics with data incorporation

    NASA Astrophysics Data System (ADS)

    Zhang, Ting; Du, Yi; Huang, Tao; Li, Xue

    2016-02-01

    Constrained by current hardware equipment and techniques, acquisition of geological data sometimes is difficult or even impossible. Stochastic simulation for geological data is helpful to address this issue, providing multiple possible results of geological data for resource prediction and risk evaluation. Multiple-point geostatistics (MPS) being one of the main branches of stochastic simulation can extract the intrinsic features of patterns from training images (TIs) that provide prior information to limit the under-determined simulated results, and then copy them to the simulated regions. Because the generated models from TIs are not always linear, some MPS methods using linear dimensionality reduction are not suitable to deal with nonlinear models of TIs. A new MPS method named ISOMAPSIM was proposed to resolve this issue, which reduces the dimensionality of patterns from TIs using isometric mapping (ISOMAP) and then classifies these low-dimensional patterns for simulation. Since conditional models including hard data and soft data influence the simulated results greatly, this paper further studies ISOMAPSIM using hard data and soft data to obtain more accurate simulations for geological modeling. Stochastic simulation of geological data is processed respectively under several conditions according to different situations of conditional models. The tests show that the proposed method can reproduce the structural characteristics of TIs under all conditions, but the condition using soft data and hard data together performs best in simulation quality; moreover, the proposed method shows its advantages over other MPS methods that use linear dimensionality reduction.

  2. Characterization of an unregulated landfill using surface-based geophysics and geostatistics

    SciTech Connect

    Woldt, W.E.; Jones, D.D.; Hagemeister, M.E.

    1998-11-01

    This paper develops a geoelectrical and geostatistical-based methodology that can be used to screen unregulated landfills for the presence of leachate and obtain an approximation of the vertical/spatial extent of waste. The methodology uses a surface electromagnetic (EM) survey combined with indicator kriging. Indicator kriging allows for the use of EM data that have been collected over highly electrically conductive material such as that occurring within landfills. Indicator kriging maps were generated for several vertical sections cut through a landfill to establish the landfill strata. Similarly, horizontal sections were generated to evaluate the areas extent of waste and leachate in the vadose zone and saturated zones, respectively. The horizontal and vertical maps were combined to estimate the volume of solid waste and liquid within three zones: (1) waste, (2) waste and/or leachate, and (3) potential leachate. The methodology appears to hold promise in providing results that can be used as part of a hazard assessment, or to assist in the placement of monitoring wells at sites requiring additional study. A case study is used to demonstrate the methodology at an unregulated landfill in eastern Nebraska.

  3. Using Predictions Based on Geostatistics to Monitor Trends in Aspergillus flavus Strain Composition.

    PubMed

    Orum, T V; Bigelow, D M; Cotty, P J; Nelson, M R

    1999-09-01

    ABSTRACT Aspergillus flavus is a soil-inhabiting fungus that frequently produces aflatoxins, potent carcinogens, in cottonseed and other seed crops. A. flavus S strain isolates, characterized on the basis of sclerotial morphology, are highly toxigenic. Spatial and temporal characteristics of the percentage of the A. flavus isolates that are S strain (S strain incidence) were used to predict patterns across areas of more than 30 km(2). Spatial autocorrelation in S strain incidence in Yuma County, AZ, was shown to extend beyond field boundaries to adjacent fields. Variograms revealed both short-range (2 to 6 km) and long-range (20 to 30 km) spatial structure in S strain incidence. S strain incidence at 36 locations sampled in July 1997 was predicted with a high correlation between expected and observed values (R = 0.85, P = 0.0001) by kriging data from July 1995 and July 1996. S strain incidence at locations sampled in October 1997 and March 1998 was markedly less than predicted by kriging data from the same months in prior years. Temporal analysis of four locations repeatedly sampled from April 1995 through July 1998 also indicated a major reduction in S strain incidence in the Texas Hill area after July 1997. Surface maps generated by kriging point data indicated a similarity in the spatial pattern of S strain incidence among all sampling dates despite temporal changes in the overall S strain incidence. Geostatistics provided useful descriptions of variability in S strain incidence over space and time.

  4. Comparison of Geostatistical Kriging Algorithms for Intertidal Surface Sediment Facies Mapping with Grain Size Data

    PubMed Central

    2014-01-01

    This paper compares the predictive performance of different geostatistical kriging algorithms for intertidal surface sediment facies mapping using grain size data. Indicator kriging, which maps facies types from conditional probabilities of predefined facies types, is first considered. In the second approach, grain size fractions are first predicted using cokriging and the facies types are then mapped. As grain size fractions are compositional data, their characteristics should be considered during spatial prediction. For efficient prediction of compositional data, additive log-ratio transformation is applied before cokriging analysis. The predictive performance of cokriging of the transformed variables is compared with that of cokriging of raw fractions in terms of both prediction errors of fractions and facies mapping accuracy. From a case study of the Baramarae tidal flat, Korea, the mapping method based on cokriging of log-ratio transformation of fractions outperformed the one based on cokriging of untransformed fractions in the prediction of fractions and produced the best facies mapping accuracy. Indicator kriging that could not account for the variation of fractions within each facies type showed the worst mapping accuracy. These case study results indicate that the proper processing of grain size fractions as compositional data is important for reliable facies mapping. PMID:24688362

  5. Comparison of geostatistical kriging algorithms for intertidal surface sediment facies mapping with grain size data.

    PubMed

    Park, No-Wook; Jang, Dong-Ho

    2014-01-01

    This paper compares the predictive performance of different geostatistical kriging algorithms for intertidal surface sediment facies mapping using grain size data. Indicator kriging, which maps facies types from conditional probabilities of predefined facies types, is first considered. In the second approach, grain size fractions are first predicted using cokriging and the facies types are then mapped. As grain size fractions are compositional data, their characteristics should be considered during spatial prediction. For efficient prediction of compositional data, additive log-ratio transformation is applied before cokriging analysis. The predictive performance of cokriging of the transformed variables is compared with that of cokriging of raw fractions in terms of both prediction errors of fractions and facies mapping accuracy. From a case study of the Baramarae tidal flat, Korea, the mapping method based on cokriging of log-ratio transformation of fractions outperformed the one based on cokriging of untransformed fractions in the prediction of fractions and produced the best facies mapping accuracy. Indicator kriging that could not account for the variation of fractions within each facies type showed the worst mapping accuracy. These case study results indicate that the proper processing of grain size fractions as compositional data is important for reliable facies mapping.

  6. Spatial variability of selected physicochemical parameters within peat deposits in small valley mire: a geostatistical approach

    NASA Astrophysics Data System (ADS)

    Pawłowski, Dominik; Okupny, Daniel; Włodarski, Wojciech; Zieliński, Tomasz

    2014-12-01

    Geostatistical methods for 2D and 3D modelling spatial variability of selected physicochemical properties of biogenic sediments were applied to a small valley mire in order to identify the processes that lead to the formation of various types of peat. A sequential Gaussian simulation was performed to reproduce the statistical distribution of the input data (pH and organic matter) and their semivariances, as well as to honouring of data values, yielding more `realistic' models that show microscale spatial variability, despite the fact that the input sample cores were sparsely distributed in the X-Y space of the study area. The stratigraphy of peat deposits in the Ldzań mire shows a record of long-term evolution of water conditions, which is associated with the variability in water supply over time. Ldzań is a fen (a rheotrophic mire) with a through-flow of groundwater. Additionally, the vicinity of the Grabia River is marked by seasonal inundations of the southwest part of the mire and increased participation of mineral matter in the peat. In turn, the upper peat layers of some of the central part of Ldzań mire are rather spongy, and these peat-forming phytocoenoses probably formed during permanent waterlogging.

  7. Inverse modeling of hydraulic tests in fractured crystalline rock based on a transition probability geostatistical approach

    NASA Astrophysics Data System (ADS)

    Blessent, Daniela; Therrien, René; Lemieux, Jean-Michel

    2011-12-01

    This paper presents numerical simulations of a series of hydraulic interference tests conducted in crystalline bedrock at Olkiluoto (Finland), a potential site for the disposal of the Finnish high-level nuclear waste. The tests are in a block of crystalline bedrock of about 0.03 km3 that contains low-transmissivity fractures. Fracture density, orientation, and fracture transmissivity are estimated from Posiva Flow Log (PFL) measurements in boreholes drilled in the rock block. On the basis of those data, a geostatistical approach relying on a transitional probability and Markov chain models is used to define a conceptual model based on stochastic fractured rock facies. Four facies are defined, from sparsely fractured bedrock to highly fractured bedrock. Using this conceptual model, three-dimensional groundwater flow is then simulated to reproduce interference pumping tests in either open or packed-off boreholes. Hydraulic conductivities of the fracture facies are estimated through automatic calibration using either hydraulic heads or both hydraulic heads and PFL flow rates as targets for calibration. The latter option produces a narrower confidence interval for the calibrated hydraulic conductivities, therefore reducing the associated uncertainty and demonstrating the usefulness of the measured PFL flow rates. Furthermore, the stochastic facies conceptual model is a suitable alternative to discrete fracture network models to simulate fluid flow in fractured geological media.

  8. Integrating address geocoding, land use regression, and spatiotemporal geostatistical estimation for groundwater tetrachloroethylene.

    PubMed

    Messier, Kyle P; Akita, Yasuyuki; Serre, Marc L

    2012-03-06

    Geographic information systems (GIS) based techniques are cost-effective and efficient methods used by state agencies and epidemiology researchers for estimating concentration and exposure. However, budget limitations have made statewide assessments of contamination difficult, especially in groundwater media. Many studies have implemented address geocoding, land use regression, and geostatistics independently, but this is the first to examine the benefits of integrating these GIS techniques to address the need of statewide exposure assessments. A novel framework for concentration exposure is introduced that integrates address geocoding, land use regression (LUR), below detect data modeling, and Bayesian Maximum Entropy (BME). A LUR model was developed for tetrachloroethylene that accounts for point sources and flow direction. We then integrate the LUR model into the BME method as a mean trend while also modeling below detects data as a truncated Gaussian probability distribution function. We increase available PCE data 4.7 times from previously available databases through multistage geocoding. The LUR model shows significant influence of dry cleaners at short ranges. The integration of the LUR model as mean trend in BME results in a 7.5% decrease in cross validation mean square error compared to BME with a constant mean trend.

  9. Forward modeling of gravity data using geostatistically generated subsurface density variations

    USGS Publications Warehouse

    Phelps, Geoffrey

    2016-01-01

    Using geostatistical models of density variations in the subsurface, constrained by geologic data, forward models of gravity anomalies can be generated by discretizing the subsurface and calculating the cumulative effect of each cell (pixel). The results of such stochastically generated forward gravity anomalies can be compared with the observed gravity anomalies to find density models that match the observed data. These models have an advantage over forward gravity anomalies generated using polygonal bodies of homogeneous density because generating numerous realizations explores a larger region of the solution space. The stochastic modeling can be thought of as dividing the forward model into two components: that due to the shape of each geologic unit and that due to the heterogeneous distribution of density within each geologic unit. The modeling demonstrates that the internally heterogeneous distribution of density within each geologic unit can contribute significantly to the resulting calculated forward gravity anomaly. Furthermore, the stochastic models match observed statistical properties of geologic units, the solution space is more broadly explored by producing a suite of successful models, and the likelihood of a particular conceptual geologic model can be compared. The Vaca Fault near Travis Air Force Base, California, can be successfully modeled as a normal or strike-slip fault, with the normal fault model being slightly more probable. It can also be modeled as a reverse fault, although this structural geologic configuration is highly unlikely given the realizations we explored.

  10. A geostatistical approach to identify and mitigate agricultural nitrous oxide emission hotspots.

    PubMed

    Turner, P A; Griffis, T J; Mulla, D J; Baker, J M; Venterea, R T

    2016-12-01

    Anthropogenic emissions of nitrous oxide (N2O), a trace gas with severe environmental costs, are greatest from agricultural soils amended with nitrogen (N) fertilizer. However, accurate N2O emission estimates at fine spatial scales are made difficult by their high variability, which represents a critical challenge for the management of N2O emissions. Here, static chamber measurements (n=60) and soil samples (n=129) were collected at approximately weekly intervals (n=6) for 42-d immediately following the application of N in a southern Minnesota cornfield (15.6-ha), typical of the systems prevalent throughout the U.S. Corn Belt. These data were integrated into a geostatistical model that resolved N2O emissions at a high spatial resolution (1-m). Field-scale N2O emissions exhibited a high degree of spatial variability, and were partitioned into three classes of emission strength: hotspots, intermediate, and coldspots. Rates of emission from hotspots were 2-fold greater than non-hotspot locations. Consequently, 36% of the field-scale emissions could be attributed to hotspots, despite representing only 21% of the total field area. Variations in elevation caused hotspots to develop in predictable locations, which were prone to nutrient and moisture accumulation caused by terrain focusing. Because these features are relatively static, our data and analyses indicate that targeted management of hotspots could efficiently reduce field-scale emissions by as much 17%, a significant benefit considering the deleterious effects of atmospheric N2O.

  11. The geostatistical approach for structural and stratigraphic framework analysis of offshore NW Bonaparte Basin, Australia

    SciTech Connect

    Wahid, Ali Salim, Ahmed Mohamed Ahmed Yusoff, Wan Ismail Wan; Gaafar, Gamal Ragab

    2016-02-01

    Geostatistics or statistical approach is based on the studies of temporal and spatial trend, which depend upon spatial relationships to model known information of variable(s) at unsampled locations. The statistical technique known as kriging was used for petrophycial and facies analysis, which help to assume spatial relationship to model the geological continuity between the known data and the unknown to produce a single best guess of the unknown. Kriging is also known as optimal interpolation technique, which facilitate to generate best linear unbiased estimation of each horizon. The idea is to construct a numerical model of the lithofacies and rock properties that honor available data and further integrate with interpreting seismic sections, techtonostratigraphy chart with sea level curve (short term) and regional tectonics of the study area to find the structural and stratigraphic growth history of the NW Bonaparte Basin. By using kriging technique the models were built which help to estimate different parameters like horizons, facies, and porosities in the study area. The variograms were used to determine for identification of spatial relationship between data which help to find the depositional history of the North West (NW) Bonaparte Basin.

  12. Integrating Address Geocoding, Land Use Regression, and Spatiotemporal Geostatistical Estimation for Groundwater Tetrachloroethylene

    PubMed Central

    Messier, Kyle P.; Akita, Yasuyuki; Serre, Marc L.

    2012-01-01

    Geographic Information Systems (GIS) based techniques are cost-effective and efficient methods used by state agencies and epidemiology researchers for estimating concentration and exposure. However, budget limitations have made statewide assessments of contamination difficult, especially in groundwater media. Many studies have implemented address geocoding, land use regression, and geostatistics independently, but this is the first to examine the benefits of integrating these GIS techniques to address the need of statewide exposure assessments. A novel framework for concentration exposure is introduced that integrates address geocoding, land use regression (LUR), below detect data modeling, and Bayesian Maximum Entropy (BME). A LUR model was developed for Tetrachloroethylene that accounts for point sources and flow direction. We then integrate the LUR model into the BME method as a mean trend while also modeling below detects data as a truncated Gaussian probability distribution function. We increase available PCE data 4.7 times from previously available databases through multistage geocoding. The LUR model shows significant influence of dry cleaners at short ranges. The integration of the LUR model as mean trend in BME results in a 7.5% decrease in cross validation mean square error compared to BME with a constant mean trend. PMID:22264162

  13. Usage of multivariate geostatistics in interpolation processes for meteorological precipitation maps

    NASA Astrophysics Data System (ADS)

    Gundogdu, Ismail Bulent

    2017-01-01

    Long-term meteorological data are very important both for the evaluation of meteorological events and for the analysis of their effects on the environment. Prediction maps which are constructed by different interpolation techniques often provide explanatory information. Conventional techniques, such as surface spline fitting, global and local polynomial models, and inverse distance weighting may not be adequate. Multivariate geostatistical methods can be more significant, especially when studying secondary variables, because secondary variables might directly affect the precision of prediction. In this study, the mean annual and mean monthly precipitations from 1984 to 2014 for 268 meteorological stations in Turkey have been used to construct country-wide maps. Besides linear regression, the inverse square distance and ordinary co-Kriging (OCK) have been used and compared to each other. Also elevation, slope, and aspect data for each station have been taken into account as secondary variables, whose use has reduced errors by up to a factor of three. OCK gave the smallest errors (1.002 cm) when aspect was included.

  14. Automatic sets and Delone sets

    NASA Astrophysics Data System (ADS)

    Barbé, A.; von Haeseler, F.

    2004-04-01

    Automatic sets D\\subset{\\bb Z}^m are characterized by having a finite number of decimations. They are equivalently generated by fixed points of certain substitution systems, or by certain finite automata. As examples, two-dimensional versions of the Thue-Morse, Baum-Sweet, Rudin-Shapiro and paperfolding sequences are presented. We give a necessary and sufficient condition for an automatic set D\\subset{\\bb Z}^m to be a Delone set in {\\bb R}^m . The result is then extended to automatic sets that are defined as fixed points of certain substitutions. The morphology of automatic sets is discussed by means of examples.

  15. Randomization Strategies.

    PubMed

    Kepler, Christopher K

    2017-04-01

    An understanding of randomization is important both for study design and to assist medical professionals in evaluating the medical literature. Simple randomization can be done through a variety of techniques, but carries a risk of unequal distribution of subjects into treatment groups. Block randomization can be used to overcome this limitation by ensuring that small subgroups are distributed evenly between treatment groups. Finally, techniques can be used to evenly distribute subjects between treatment groups while accounting for confounding variables, so as to not skew results when there is a high index of suspicion that a particular variable will influence outcome.

  16. Text Sets.

    ERIC Educational Resources Information Center

    Giorgis, Cyndi; Johnson, Nancy J.

    2002-01-01

    Presents annotations of approximately 30 titles grouped in text sets. Defines a text set as five to ten books on a particular topic or theme. Discusses books on the following topics: living creatures; pirates; physical appearance; natural disasters; and the Irish potato famine. (SG)

  17. A geostatistical analysis of small-scale spatial variability in bacterial abundance and community structure in salt marsh creek bank sediments

    NASA Technical Reports Server (NTRS)

    Franklin, Rima B.; Blum, Linda K.; McComb, Alison C.; Mills, Aaron L.

    2002-01-01

    Small-scale variations in bacterial abundance and community structure were examined in salt marsh sediments from Virginia's eastern shore. Samples were collected at 5 cm intervals (horizontally) along a 50 cm elevation gradient, over a 215 cm horizontal transect. For each sample, bacterial abundance was determined using acridine orange direct counts and community structure was analyzed using randomly amplified polymorphic DNA fingerprinting of whole-community DNA extracts. A geostatistical analysis was used to determine the degree of spatial autocorrelation among the samples, for each variable and each direction (horizontal and vertical). The proportion of variance in bacterial abundance that could be accounted for by the spatial model was quite high (vertical: 60%, horizontal: 73%); significant autocorrelation was found among samples separated by 25 cm in the vertical direction and up to 115 cm horizontally. In contrast, most of the variability in community structure was not accounted for by simply considering the spatial separation of samples (vertical: 11%, horizontal: 22%), and must reflect variability from other parameters (e.g., variation at other spatial scales, experimental error, or environmental heterogeneity). Microbial community patch size based upon overall similarity in community structure varied between 17 cm (vertical) and 35 cm (horizontal). Overall, variability due to horizontal position (distance from the creek bank) was much smaller than that due to vertical position (elevation) for both community properties assayed. This suggests that processes more correlated with elevation (e.g., drainage and redox potential) vary at a smaller scale (therefore producing smaller patch sizes) than processes controlled by distance from the creek bank. c2002 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved.

  18. Random thoughts

    NASA Astrophysics Data System (ADS)

    ajansen; kwhitefoot; panteltje1; edprochak; sudhakar, the

    2014-07-01

    In reply to the physicsworld.com news story “How to make a quantum random-number generator from a mobile phone” (16 May, http://ow.ly/xFiYc, see also p5), which describes a way of delivering random numbers by counting the number of photons that impinge on each of the individual pixels in the camera of a Nokia N9 smartphone.

  19. Geostatistical Modeling of Malaria Endemicity using Serological Indicators of Exposure Collected through School Surveys

    PubMed Central

    Ashton, Ruth A.; Kefyalew, Takele; Rand, Alison; Sime, Heven; Assefa, Ashenafi; Mekasha, Addis; Edosa, Wasihun; Tesfaye, Gezahegn; Cano, Jorge; Teka, Hiwot; Reithinger, Richard; Pullan, Rachel L.; Drakeley, Chris J.; Brooker, Simon J.

    2015-01-01

    Ethiopia has a diverse ecology and geography resulting in spatial and temporal variation in malaria transmission. Evidence-based strategies are thus needed to monitor transmission intensity and target interventions. A purposive selection of dried blood spots collected during cross-sectional school-based surveys in Oromia Regional State, Ethiopia, were tested for presence of antibodies against Plasmodium falciparum and P. vivax antigens. Spatially explicit binomial models of seroprevalence were created for each species using a Bayesian framework, and used to predict seroprevalence at 5 km resolution across Oromia. School seroprevalence showed a wider prevalence range than microscopy for both P. falciparum (0–50% versus 0–12.7%) and P. vivax (0–53.7% versus 0–4.5%), respectively. The P. falciparum model incorporated environmental predictors and spatial random effects, while P. vivax seroprevalence first-order trends were not adequately explained by environmental variables, and a spatial smoothing model was developed. This is the first demonstration of serological indicators being used to detect large-scale heterogeneity in malaria transmission using samples from cross-sectional school-based surveys. The findings support the incorporation of serological indicators into periodic large-scale surveillance such as Malaria Indicator Surveys, and with particular utility for low transmission and elimination settings. PMID:25962770

  20. A space-time geostatistical framework for ensemble nowcasting using rainfall radar fields and gauge data

    NASA Astrophysics Data System (ADS)

    Caseri, Angelica; Ramos, Maria Helena; Javelle, Pierre; Leblois, Etienne

    2016-04-01

    Floods are responsible for a major part of the total damage caused by natural disasters. Nowcasting systems providing public alerts to flash floods are very important to prevent damages from extreme events and reduce their socio-economic impacts. The major challenge of these systems is to capture high-risk situations in advance, with good accuracy in the intensity, location and timing of future intense precipitation events. Flash flood forecasting has been studied by several authors in different affected areas. The majority of the studies combines rain gauge data with radar imagery advection to improve prediction for the next few hours. Outputs of Numerical Weather Prediction (NWP) models have also been increasingly used to predict ensembles of extreme precipitation events that might trigger flash floods. One of the challenges of the use of NWP for ensemble nowcasting is to successfully generate ensemble forecasts of precipitation in a short time calculation period to enable the production of flood forecasts with sufficient advance to issue flash flood alerts. In this study, we investigate an alternative space-time geostatistical framework to generate multiple scenarios of future rainfall for flash floods nowcasting. The approach is based on conditional simulation and an advection method applied within the Turning Bands Method (TBM). Ensemble forecasts of precipitation fields are generated based on space-time properties given by radar images and precipitation data collected from rain gauges during the development of the rainfall event. The results show that the approach developed can be an interesting alternative to capture precipitation uncertainties in location and intensity and generate ensemble forecasts of rainfall that can be useful to improve alerts for flash floods, especially in small areas.

  1. Novel applications of multiple-point geostatistics in remote sensing, geophysics, climate science and surface hydrology

    NASA Astrophysics Data System (ADS)

    Mariethoz, G.; Jha, S. K.; McCabe, M. F.; Evans, J. P.

    2012-12-01

    Recent advances in multiple-point geostatistics (MPS) offer new possibilities in remote sensing, surface hydrology and climate modeling. MPS is an ensemble of tools for the characterization of spatial phenomena. Its most prominent characteristic is the use of training images for defining what type of spatial patterns are deemed to result from the processes under study. In the last decade, MPS have been increasingly used to characterize 3D subsurface structures consisting of geological facies, with application primarily to reservoir engineering, hydrogeology and mining. Although the methods show good results, a consistent difficulty relates to finding appropriate training images to describe largely unknown geological formations. Despite this issue, the growing interest in MPS triggered a series of different methodological advances, leading to improved computational performance and increased flexibility. With these recent improvements, the scientific community now has unprecedented numerical tools that allow dealing with a wide range of problems outside the realm of subsurface applications. These include the simulation of continuous variables as well as complex non-linear ensembles of multivariate properties. It is found that these new tools are ideal to address a number of issues in scientific fields related to surface modeling of environmental systems and geophysical data. Shifting focus and investigating the application of MPS to surface hydrology results in a wealth of training images that are readily available, thanks to global networks of remote sensing measurements. This presentation will delineate recent results in this direction, including MPS applications to the stochastic downscaling of climate models, the completion of partially informed remote sensing images and the processing of geophysical data. A major advantage is the use of satellite images taken at regular intervals, which can be used to inform both the spatial and temporal variability of

  2. Simulation of Solute Flow and Transport in a Geostatistically Generated Fractured Porous System

    NASA Astrophysics Data System (ADS)

    Assteerawatt, A.; Helmig, R.; Haegland, H.; Bárdossy, A.

    2007-12-01

    Fractured aquifer systems have provided important natural resources such as petroleum, gas, water and geothermal energy and have also been recently under investigation for their suitability as storage sites for high-level nuclear waste. The resource exploitation and potential utilization have led to extensive studies aiming of understanding, characterizing and finally predicting the behavior of fractured aquifer systems. By applying a discrete model approach to study flow and transport processes, fractures are determined discretely and the effect of individual fractures can be explicitly investigated. The critical step for the discrete model is the generation of a representative fracture network since the development of flow paths within a fractured system strongly depends on its structure. The geostatistical fracture generation (GFG) developed in this study aims to create a representative fracture network, which combines the spatial structures and connectivity of a fracture network, and the statistical distribution of fracture geometries. The spatial characteristics are characterized from indicator fields, which are evaluated from fracture trace maps. A global optimization, Simulated annealing, is utilized as a generation technique and the spatial characteristics are formulated to its objective function. We apply the GFG to a case study at a Pliezhausen field block, which is a sandstone of a high fracture density. The generated fracture network from the GFG are compared with the statistically generated fracture network in term of structure and hydraulic behavior. As the GFG is based on a stochastic concept, several realizations of the same descriptions can be generated, hence, an overall behavior of the fracture-matrix system have to be investigated from various realizations which leads to a problem of computational demand. In order to overcome this problem, a streamline method for a solute transport in a fracture porous system is presented. The results obtained

  3. Estimating the Depth of Stratigraphic Units from Marine Seismic Profiles Using Nonstationary Geostatistics

    SciTech Connect

    Chihi, Hayet; Galli, Alain; Ravenne, Christian; Tesson, Michel; Marsily, Ghislain de

    2000-03-15

    The object of this study is to build a three-dimensional (3D) geometric model of the stratigraphic units of the margin of the Rhone River on the basis of geophysical investigations by a network of seismic profiles at sea. The geometry of these units is described by depth charts of each surface identified by seismic profiling, which is done by geostatistics. The modeling starts by a statistical analysis by which we determine the parameters that enable us to calculate the variograms of the identified surfaces. After having determined the statistical parameters, we calculate the variograms of the variable Depth. By analyzing the behavior of the variogram we then can deduce whether the situation is stationary and if the variable has an anisotropic behavior. We tried the following two nonstationary methods to obtain our estimates: (a) The method of universal kriging if the underlying variogram was directly accessible. (b) The method of increments if the underlying variogram was not directly accessible. After having modeled the variograms of the increments and of the variable itself, we calculated the surfaces by kriging the variable Depth on a small-mesh estimation grid. The two methods then are compared and their respective advantages and disadvantages are discussed, as well as their fields of application. These methods are capable of being used widely in earth sciences for automatic mapping of geometric surfaces or for variables such as a piezometric surface or a concentration, which are not 'stationary,' that is, essentially, possess a gradient or a tendency to develop systematically in space.

  4. Accounting for geophysical information in geostatistical characterization of unexploded ordnance (UXO) sites.

    SciTech Connect

    Saito, Hirotaka; Goovaerts, Pierre; McKenna, Sean Andrew

    2003-06-01

    Efficient and reliable unexploded ordnance (UXO) site characterization is needed for decisions regarding future land use. There are several types of data available at UXO sites and geophysical signal maps are one of the most valuable sources of information. Incorporation of such information into site characterization requires a flexible and reliable methodology. Geostatistics allows one to account for exhaustive secondary information (i.e.,, known at every location within the field) in many different ways. Kriging and logistic regression were combined to map the probability of occurrence of at least one geophysical anomaly of interest, such as UXO, from a limited number of indicator data. Logistic regression is used to derive the trend from a geophysical signal map, and kriged residuals are added to the trend to estimate the probabilities of the presence of UXO at unsampled locations (simple kriging with varying local means or SKlm). Each location is identified for further remedial action if the estimated probability is greater than a given threshold. The technique is illustrated using a hypothetical UXO site generated by a UXO simulator, and a corresponding geophysical signal map. Indicator data are collected along two transects located within the site. Classification performances are then assessed by computing proportions of correct classification, false positive, false negative, and Kappa statistics. Two common approaches, one of which does not take any secondary information into account (ordinary indicator kriging) and a variant of common cokriging (collocated cokriging), were used for comparison purposes. Results indicate that accounting for exhaustive secondary information improves the overall characterization of UXO sites if an appropriate methodology, SKlm in this case, is used.

  5. Using Geostatistical Data Fusion Techniques and MODIS Data to Upscale Simulated Wheat Yield

    NASA Astrophysics Data System (ADS)

    Castrignano, A.; Buttafuoco, G.; Matese, A.; Toscano, P.

    2014-12-01

    Population growth increases food request. Assessing food demand and predicting the actual supply for a given location are critical components of strategic food security planning at regional scale. Crop yield can be simulated using crop models because is site-specific and determined by weather, management, length of growing season and soil properties. Crop models require reliable location-specific data that are not generally available. Obtaining these data at a large number of locations is time-consuming, costly and sometimes simply not feasible. An upscaling method to extend coverage of sparse estimates of crop yield to an appropriate extrapolation domain is required. This work is aimed to investigate the applicability of a geostatistical data fusion approach for merging remote sensing data with the predictions of a simulation model of wheat growth and production using ground-based data. The study area is Capitanata plain (4000 km2) located in Apulia Region, mostly cropped with durum wheat. The MODIS EVI/NDVI data products for Capitanata plain were downloaded from the Land Processes Distributed Active Archive Center (LPDAAC) remote for the whole crop cycle of durum wheat. Phenological development, biomass growth and grain quantity of durum wheat were simulated by the Delphi system, based on a crop simulation model linked to a database including soil properties, agronomical and meteorological data. Multicollocated cokriging was used to integrate secondary exhaustive information (multi-spectral MODIS data) with primary variable (sparsely distributed biomass/yield model predictions of durum wheat). The model estimates looked strongly spatially correlated with the radiance data (red and NIR bands) and the fusion data approach proved to be quite suitable and flexible to integrate data of different type and support.

  6. Coupling geostatistical approaches with PCA and fuzzy optimal model (FOM) for the integrated assessment of sampling locations of water quality monitoring networks (WQMNs).

    PubMed

    Ou, Chunping; St-Hilaire, André; Ouarda, Taha B M J; Conly, F Malcolm; Armstrong, Nicole; Khalil, Bahaa; Proulx-McInnis, Sandra

    2012-12-01

    The assessment of the adequacy of sampling locations is an important aspect in the validation of an effective and efficient water quality monitoring network. Two geostatistical approaches (e.g., kriging and Moran's I) are presented to assess multiple sampling locations. A flexible and comprehensive framework was developed for the selection of multiple sampling locations of multiple variables which was accomplished by coupling geostatistical approaches with principal component analysis (PCA) and fuzzy optimal model (FOM). The FOM was used in the integrated assessment of both multiple principal components and multiple geostatistical approaches. These integrated methods were successfully applied to the assessment of two independent water quality monitoring networks (WQMNs) of Lake Winnipeg, Canada, which respectively included 14 and 30 stations from 2006 to 2010.

  7. Geostatistical Methods For Determination of Roughness, Topography, And Changes of Antarctic Ice Streams From SAR And Radar Altimeter Data

    NASA Technical Reports Server (NTRS)

    Herzfeld, Ute C.

    2002-01-01

    The central objective of this project has been the development of geostatistical methods fro mapping elevation and ice surface characteristics from satellite radar altimeter (RA) and Syntheitc Aperture Radar (SAR) data. The main results are an Atlas of elevation maps of Antarctica, from GEOSAT RA data and an Atlas from ERS-1 RA data, including a total of about 200 maps with 3 km grid resolution. Maps and digital terrain models are applied to monitor and study changes in Antarctic ice streams and glaciers, including Lambert Glacier/Amery Ice Shelf, Mertz and Ninnis Glaciers, Jutulstraumen Glacier, Fimbul Ice Shelf, Slessor Glacier, Williamson Glacier and others.

  8. Potential of deterministic and geostatistical rainfall interpolation under high rainfall variability and dry spells: case of Kenya's Central Highlands

    NASA Astrophysics Data System (ADS)

    Kisaka, M. Oscar; Mucheru-Muna, M.; Ngetich, F. K.; Mugwe, J.; Mugendi, D.; Mairura, F.; Shisanya, C.; Makokha, G. L.

    2016-04-01

    Drier parts of Kenya's Central Highlands endure persistent crop failure and declining agricultural productivity. These have, in part, attributed to high temperatures, prolonged dry spells and erratic rainfall. Understanding spatial-temporal variability of climatic indices such as rainfall at seasonal level is critical for optimal rain-fed agricultural productivity and natural resource management in the study area. However, the predominant setbacks in analysing hydro-meteorological events are occasioned by either lack, inadequate, or inconsistent meteorological data. Like in most other places, the sole sources of climatic data in the study region are scarce and only limited to single stations, yet with persistent missing/unrecorded data making their utilization a challenge. This study examined seasonal anomalies and variability in rainfall, drought occurrence and the efficacy of interpolation techniques in the drier regions of eastern Kenyan. Rainfall data from five stations (Machang'a, Kiritiri, Kiambere and Kindaruma and Embu) were sourced from both the Kenya Meteorology Department and on-site primary recording. Owing to some experimental work ongoing, automated recording for primary dailies in Machang'a have been ongoing since the year 2000 to date; thus, Machang'a was treated as reference (for period of record) station for selection of other stations in the region. The other stations had data sets of over 15 years with missing data of less than 10 % as required by the world meteorological organization whose quality check is subject to the Centre for Climate Systems Modeling (C2SM) through MeteoSwiss and EMPA bodies. The dailies were also subjected to homogeneity testing to evaluate whether they came from the same population. Rainfall anomaly index, coefficients of variance and probability were utilized in the analyses of rainfall variability. Spline, kriging and inverse distance weighting interpolation techniques were assessed using daily rainfall data and

  9. Genetic Geostatistical Framework for Spatial Analysis of Fine-Scale Genetic Heterogeneity in Modern Populations: Results from the KORA Study.

    PubMed

    Diaz-Lacava, A N; Walier, M; Holler, D; Steffens, M; Gieger, C; Furlanello, C; Lamina, C; Wichmann, H E; Becker, T

    2015-01-01

    Aiming to investigate fine-scale patterns of genetic heterogeneity in modern humans from a geographic perspective, a genetic geostatistical approach framed within a geographic information system is presented. A sample collected for prospective studies in a small area of southern Germany was analyzed. None indication of genetic heterogeneity was detected in previous analysis. Socio-demographic and genotypic data of German citizens were analyzed (212 SNPs; n = 728). Genetic heterogeneity was evaluated with observed heterozygosity (H O ). Best-fitting spatial autoregressive models were identified, using socio-demographic variables as covariates. Spatial analysis included surface interpolation and geostatistics of observed and predicted patterns. Prediction accuracy was quantified. Spatial autocorrelation was detected for both socio-demographic and genetic variables. Augsburg City and eastern suburban areas showed higher H O values. The selected model gave best predictions in suburban areas. Fine-scale patterns of genetic heterogeneity were observed. In accordance to literature, more urbanized areas showed higher levels of admixture. This approach showed efficacy for detecting and analyzing subtle patterns of genetic heterogeneity within small areas. It is scalable in number of loci, even up to whole-genome analysis. It may be suggested that this approach may be applicable to investigate the underlying genetic history that is, at least partially, embedded in geographic data.

  10. Using direct current resistivity sounding and geostatistics to aid in hydrogeological studies in the Choshuichi alluvial fan, Taiwan.

    PubMed

    Yang, Chieh-Hou; Lee, Wei-Feng

    2002-01-01

    Ground water reservoirs in the Choshuichi alluvial fan, central western Taiwan, were investigated using direct-current (DC) resistivity soundings at 190 locations, combined with hydrogeological measurements from 37 wells. In addition, attempts were made to calculate aquifer transmissivity from both surface DC resistivity measurements and geostatistically derived predictions of aquifer properties. DC resistivity sounding data are highly correlated to the hydraulic parameters in the Choshuichi alluvial fan. By estimating the spatial distribution of hydraulic conductivity from the kriged well data and the cokriged thickness of the correlative aquifer from both resistivity sounding data and well information, the transmissivity of the aquifer at each location can be obtained from the product of kriged hydraulic conductivity and computed thickness of the geoelectric layer. Thus, the spatial variation of the transmissivities in the study area is obtained. Our work is more comparable to Ahmed et al. (1988) than to the work of Niwas and Singhal (1981). The first "constraint" from Niwas and Singhal's work is a result of their use of linear regression. The geostatistical approach taken here (and by Ahmed et al. [1988]) is a natural improvement on the linear regression approach.

  11. Assimilation of Satellite Soil Moisture observation with the Particle Filter-Markov Chain Monte Carlo and Geostatistical Modeling

    NASA Astrophysics Data System (ADS)

    Moradkhani, Hamid; Yan, Hongxiang

    2016-04-01

    Soil moisture simulation and prediction are increasingly used to characterize agricultural droughts but the process suffers from data scarcity and quality. The satellite soil moisture observations could be used to improve model predictions with data assimilation. Remote sensing products, however, are typically discontinuous in spatial-temporal coverages; while simulated soil moisture products are potentially biased due to the errors in forcing data, parameters, and deficiencies of model physics. This study attempts to provide a detailed analysis of the joint and separate assimilation of streamflow and Advanced Scatterometer (ASCAT) surface soil moisture into a fully distributed hydrologic model, with the use of recently developed particle filter-Markov chain Monte Carlo (PF-MCMC) method. A geostatistical model is introduced to overcome the satellite soil moisture discontinuity issue where satellite data does not cover the whole study region or is significantly biased, and the dominant land cover is dense vegetation. The results indicate that joint assimilation of soil moisture and streamflow has minimal effect in improving the streamflow prediction, however, the surface soil moisture field is significantly improved. The combination of DA and geostatistical approach can further improve the surface soil moisture prediction.

  12. Random Vibrations

    NASA Technical Reports Server (NTRS)

    Messaro. Semma; Harrison, Phillip

    2010-01-01

    Ares I Zonal Random vibration environments due to acoustic impingement and combustion processes are develop for liftoff, ascent and reentry. Random Vibration test criteria for Ares I Upper Stage pyrotechnic components are developed by enveloping the applicable zonal environments where each component is located. Random vibration tests will be conducted to assure that these components will survive and function appropriately after exposure to the expected vibration environments. Methodology: Random Vibration test criteria for Ares I Upper Stage pyrotechnic components were desired that would envelope all the applicable environments where each component was located. Applicable Ares I Vehicle drawings and design information needed to be assessed to determine the location(s) for each component on the Ares I Upper Stage. Design and test criteria needed to be developed by plotting and enveloping the applicable environments using Microsoft Excel Spreadsheet Software and documenting them in a report Using Microsoft Word Processing Software. Conclusion: Random vibration liftoff, ascent, and green run design & test criteria for the Upper Stage Pyrotechnic Components were developed by using Microsoft Excel to envelope zonal environments applicable to each component. Results were transferred from Excel into a report using Microsoft Word. After the report is reviewed and edited by my mentor it will be submitted for publication as an attachment to a memorandum. Pyrotechnic component designers will extract criteria from my report for incorporation into the design and test specifications for components. Eventually the hardware will be tested to the environments I developed to assure that the components will survive and function appropriately after exposure to the expected vibration environments.

  13. Estimation of water table level and nitrate pollution based on geostatistical and multiple mass transport models

    NASA Astrophysics Data System (ADS)

    Matiatos, Ioannis; Varouhakis, Emmanouil A.; Papadopoulou, Maria P.

    2015-04-01

    level and nitrate concentrations were produced and compared with those obtained from groundwater and mass transport numerical models. Preliminary results showed similar efficiency of the spatiotemporal geostatistical method with the numerical models. However data requirements of the former model were significantly less. Advantages and disadvantages of the methods performance were analysed and discussed indicating the characteristics of the different approaches.

  14. Geostatistical modelling of soil-transmitted helminth infection in Cambodia: do socioeconomic factors improve predictions?

    PubMed

    Karagiannis-Voules, Dimitrios-Alexios; Odermatt, Peter; Biedermann, Patricia; Khieu, Virak; Schär, Fabian; Muth, Sinuon; Utzinger, Jürg; Vounatsou, Penelope

    2015-01-01

    Soil-transmitted helminth infections are intimately connected with poverty. Yet, there is a paucity of using socioeconomic proxies in spatially explicit risk profiling. We compiled household-level socioeconomic data pertaining to sanitation, drinking-water, education and nutrition from readily available Demographic and Health Surveys, Multiple Indicator Cluster Surveys and World Health Surveys for Cambodia and aggregated the data at village level. We conducted a systematic review to identify parasitological surveys and made every effort possible to extract, georeference and upload the data in the open source Global Neglected Tropical Diseases database. Bayesian geostatistical models were employed to spatially align the village-aggregated socioeconomic predictors with the soil-transmitted helminth infection data. The risk of soil-transmitted helminth infection was predicted at a grid of 1×1km covering Cambodia. Additionally, two separate individual-level spatial analyses were carried out, for Takeo and Preah Vihear provinces, to assess and quantify the association between soil-transmitted helminth infection and socioeconomic indicators at an individual level. Overall, we obtained socioeconomic proxies from 1624 locations across the country. Surveys focussing on soil-transmitted helminth infections were extracted from 16 sources reporting data from 238 unique locations. We found that the risk of soil-transmitted helminth infection from 2000 onwards was considerably lower than in surveys conducted earlier. Population-adjusted prevalences for school-aged children from 2000 onwards were 28.7% for hookworm, 1.5% for Ascaris lumbricoides and 0.9% for Trichuris trichiura. Surprisingly, at the country-wide analyses, we did not find any significant association between soil-transmitted helminth infection and village-aggregated socioeconomic proxies. Based also on the individual-level analyses we conclude that socioeconomic proxies might not be good predictors at an

  15. Determination of geostatistically representative sampling locations in Porsuk Dam Reservoir (Turkey)

    NASA Astrophysics Data System (ADS)

    Aksoy, A.; Yenilmez, F.; Duzgun, S.

    2013-12-01

    Several factors such as wind action, bathymetry and shape of a lake/reservoir, inflows, outflows, point and diffuse pollution sources result in spatial and temporal variations in water quality of lakes and reservoirs. The guides by the United Nations Environment Programme and the World Health Organization to design and implement water quality monitoring programs suggest that even a single monitoring station near the center or at the deepest part of a lake will be sufficient to observe long-term trends if there is good horizontal mixing. In stratified water bodies, several samples can be required. According to the guide of sampling and analysis under the Turkish Water Pollution Control Regulation, a minimum of five sampling locations should be employed to characterize the water quality in a reservoir or a lake. The European Union Water Framework Directive (2000/60/EC) states to select a sufficient number of monitoring sites to assess the magnitude and impact of point and diffuse sources and hydromorphological pressures in designing a monitoring program. Although existing regulations and guidelines include frameworks for the determination of sampling locations in surface waters, most of them do not specify a procedure in establishment of monitoring aims with representative sampling locations in lakes and reservoirs. In this study, geostatistical tools are used to determine the representative sampling locations in the Porsuk Dam Reservoir (PDR). Kernel density estimation and kriging were used in combination to select the representative sampling locations. Dissolved oxygen and specific conductivity were measured at 81 points. Sixteen of them were used for validation. In selection of the representative sampling locations, care was given to keep similar spatial structure in distributions of measured parameters. A procedure was proposed for that purpose. Results indicated that spatial structure was lost under 30 sampling points. This was as a result of varying water

  16. Use of geostatistics for assessing the concentration of heavy metals in a stretch of the River Apodi-Mossoro (Rio Grande do Norte State, Brazil).

    NASA Astrophysics Data System (ADS)

    Bezerra, J. M.; Siqueira, G. M.; Montenegro, A. A. A.; Silva, P. C. M.; Batista, R. O.

    2012-04-01

    The objective of this study was to assess the environmental changes with respect to the concentration of heavy metals in the sediment contained a stretch of the River Apodi-Mossoró (Rio Grande do Norte State, Brazil), considering changes in land use and soil. The sediment samples were collected at 30 points in the bed Apodi- Mossoró River in a section with features urban-rural town of Mossoró. The concentration of heavy metals in the sediment was determined using composite samples of surface sediments from the bottom with a depth of 20 cm, according to the methodology of APHAAWWA-WPCF (1998), where he subsequently held to determine the presence and quantity of metal concentration total by the technique of atomic absorption spectrometry, and analyzed the following heavy metals: aluminum(Al), cádmium (Cd), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb) and zinc (Zn). Data were analyzed using statistical and geostatistical. The geostatistical analysiswas performed by the construction of experimental semivariogramas self-assessment and adjustment by using the technique of Jack-kinifing. The elemento Cd was absent in the samples, which reduces the possibility of environmental contamination events. The average concentrations of the elements under study are within the limits proposed by the environmental legislation (National Environmental Council). However, for the elements Fe, Al and Mn no threshold values, because these are associated with the rocky material of geochemical origin. The elemento Fe had the highest range of values than the other, and all elements except for Zn and Cd showed the presence of outliers, suggesting the possibility that these points are listed as points liable to contribution by human activities. It was verified the presence of human influence, because the elements undergo an increase of concentration values from the point 11, which is located downstream of the urban bus consolidated. The experimental

  17. Geostatistical uncertainty of assessing air quality using high-spatial-resolution lichen data: A health study in the urban area of Sines, Portugal.

    PubMed

    Ribeiro, Manuel C; Pinho, P; Branquinho, C; Llop, Esteve; Pereira, Maria J

    2016-08-15

    In most studies correlating health outcomes with air pollution, personal exposure assignments are based on measurements collected at air-quality monitoring stations not coinciding with health data locations. In such cases, interpolators are needed to predict air quality in unsampled locations and to assign personal exposures. Moreover, a measure of the spatial uncertainty of exposures should be incorporated, especially in urban areas where concentrations vary at short distances due to changes in land use and pollution intensity. These studies are limited by the lack of literature comparing exposure uncertainty derived from distinct spatial interpolators. Here, we addressed these issues with two interpolation methods: regression Kriging (RK) and ordinary Kriging (OK). These methods were used to generate air-quality simulations with a geostatistical algorithm. For each method, the geostatistical uncertainty was drawn from generalized linear model (GLM) analysis. We analyzed the association between air quality and birth weight. Personal health data (n=227) and exposure data were collected in Sines (Portugal) during 2007-2010. Because air-quality monitoring stations in the city do not offer high-spatial-resolution measurements (n=1), we used lichen data as an ecological indicator of air quality (n=83). We found no significant difference in the fit of GLMs with any of the geostatistical methods. With RK, however, the models tended to fit better more often and worse less often. Moreover, the geostatistical uncertainty results showed a marginally higher mean and precision with RK. Combined with lichen data and land-use data of high spatial resolution, RK is a more effective geostatistical method for relating health outcomes with air quality in urban areas. This is particularly important in small cities, which generally do not have expensive air-quality monitoring stations with high spatial resolution. Further, alternative ways of linking human activities with their

  18. True Randomness from Big Data

    NASA Astrophysics Data System (ADS)

    Papakonstantinou, Periklis A.; Woodruff, David P.; Yang, Guang

    2016-09-01

    Generating random bits is a difficult task, which is important for physical systems simulation, cryptography, and many applications that rely on high-quality random bits. Our contribution is to show how to generate provably random bits from uncertain events whose outcomes are routinely recorded in the form of massive data sets. These include scientific data sets, such as in astronomics, genomics, as well as data produced by individuals, such as internet search logs, sensor networks, and social network feeds. We view the generation of such data as the sampling process from a big source, which is a random variable of size at least a few gigabytes. Our view initiates the study of big sources in the randomness extraction literature. Previous approaches for big sources rely on statistical assumptions about the samples. We introduce a general method that provably extracts almost-uniform random bits from big sources and extensively validate it empirically on real data sets. The experimental findings indicate that our method is efficient enough to handle large enough sources, while previous extractor constructions are not efficient enough to be practical. Quality-wise, our method at least matches quantum randomness expanders and classical world empirical extractors as measured by standardized tests.

  19. True Randomness from Big Data

    PubMed Central

    Papakonstantinou, Periklis A.; Woodruff, David P.; Yang, Guang

    2016-01-01

    Generating random bits is a difficult task, which is important for physical systems simulation, cryptography, and many applications that rely on high-quality random bits. Our contribution is to show how to generate provably random bits from uncertain events whose outcomes are routinely recorded in the form of massive data sets. These include scientific data sets, such as in astronomics, genomics, as well as data produced by individuals, such as internet search logs, sensor networks, and social network feeds. We view the generation of such data as the sampling process from a big source, which is a random variable of size at least a few gigabytes. Our view initiates the study of big sources in the randomness extraction literature. Previous approaches for big sources rely on statistical assumptions about the samples. We introduce a general method that provably extracts almost-uniform random bits from big sources and extensively validate it empirically on real data sets. The experimental findings indicate that our method is efficient enough to handle large enough sources, while previous extractor constructions are not efficient enough to be practical. Quality-wise, our method at least matches quantum randomness expanders and classical world empirical extractors as measured by standardized tests. PMID:27666514

  20. True Randomness from Big Data.

    PubMed

    Papakonstantinou, Periklis A; Woodruff, David P; Yang, Guang

    2016-09-26

    Generating random bits is a difficult task, which is important for physical systems simulation, cryptography, and many applications that rely on high-quality random bits. Our contribution is to show how to generate provably random bits from uncertain events whose outcomes are routinely recorded in the form of massive data sets. These include scientific data sets, such as in astronomics, genomics, as well as data produced by individuals, such as internet search logs, sensor networks, and social network feeds. We view the generation of such data as the sampling process from a big source, which is a random variable of size at least a few gigabytes. Our view initiates the study of big sources in the randomness extraction literature. Previous approaches for big sources rely on statistical assumptions about the samples. We introduce a general method that provably extracts almost-uniform random bits from big sources and extensively validate it empirically on real data sets. The experimental findings indicate that our method is efficient enough to handle large enough sources, while previous extractor constructions are not efficient enough to be practical. Quality-wise, our method at least matches quantum randomness expanders and classical world empirical extractors as measured by standardized tests.

  1. Random grammars

    NASA Astrophysics Data System (ADS)

    Malyshev, V. A.

    1998-04-01

    Contents § 1. Definitions1.1. Grammars1.2. Random grammars and L-systems1.3. Semigroup representations § 2. Infinite string dynamics2.1. Cluster expansion2.2. Cluster dynamics2.3. Local observer § 3. Large time behaviour: small perturbations3.1. Invariant measures3.2. Classification § 4. Large time behaviour: context free case4.1. Invariant measures for grammars4.2. L-systems4.3. Fractal correlation functions4.4. Measures on languages Bibliography

  2. IN SITU NON-INVASIVE SOIL CARBON ANALYSIS: SAMPLE SIZE AND GEOSTATISTICAL CONSIDERATIONS.

    SciTech Connect

    WIELOPOLSKI, L.

    2005-04-01

    I discuss a new approach for quantitative carbon analysis in soil based on INS. Although this INS method is not simple, it offers critical advantages not available with other newly emerging modalities. The key advantages of the INS system include the following: (1) It is a non-destructive method, i.e., no samples of any kind are taken. A neutron generator placed above the ground irradiates the soil, stimulating carbon characteristic gamma-ray emission that is counted by a detection system also placed above the ground. (2) The INS system can undertake multielemental analysis, so expanding its usefulness. (3) It can be used either in static or scanning modes. (4) The volume sampled by the INS method is large with a large footprint; when operating in a scanning mode, the sampled volume is continuous. (5) Except for a moderate initial cost of about $100,000 for the system, no additional expenses are required for its operation over two to three years after which a NG has to be replenished with a new tube at an approximate cost of $10,000, this regardless of the number of sites analyzed. In light of these characteristics, the INS system appears invaluable for monitoring changes in the carbon content in the field. For this purpose no calibration is required; by establishing a carbon index, changes in carbon yield can be followed with time in exactly the same location, thus giving a percent change. On the other hand, with calibration, it can be used to determine the carbon stock in the ground, thus estimating the soil's carbon inventory. However, this requires revising the standard practices for deciding upon the number of sites required to attain a given confidence level, in particular for the purposes of upward scaling. Then, geostatistical considerations should be incorporated in considering properly the averaging effects of the large volumes sampled by the INS system that would require revising standard practices in the field for determining the number of spots to be

  3. Building a geological reference platform using sequence stratigraphy combined with geostatistical tools

    NASA Astrophysics Data System (ADS)

    Bourgine, Bernard; Lasseur, Éric; Leynet, Aurélien; Badinier, Guillaume; Ortega, Carole; Issautier, Benoit; Bouchet, Valentin

    2015-04-01

    In 2012 BRGM launched an extensive program to build the new French Geological Reference platform (RGF). Among the objectives of this program is to provide the public with validated, reliable and 3D-consistent geological data, with estimation of uncertainty. Approx. 100,000 boreholes over the whole French national territory provide a preliminary interpretation in terms of depths of main geological interfaces, but with an unchecked, unknown and often low reliability. The aim of this paper is to present the procedure that has been tested on two areas in France, in order to validate (or not) these boreholes, with the aim of being generalized as much as possible to the nearly 100,000 boreholes waiting for validation. The approach is based on the following steps, and includes the management of uncertainty at different steps: (a) Selection of a loose network of boreholes owning a logging or coring information enabling a reliable interpretation. This first interpretation is based on the correlation of well log data and allows defining 3D sequence stratigraphic framework identifying isochronous surfaces. A litho-stratigraphic interpretation is also performed. Be "A" the collection of all boreholes used for this step (typically 3 % of the total number of holes to be validated) and "B" the other boreholes to validate, (b) Geostatistical analysis of characteristic geological interfaces. The analysis is carried out firstly on the "A" type data (to validate the variogram model), then on the "B" type data and at last on "B" knowing "A". It is based on cross-validation tests and evaluation of the uncertainty associated to each geological interface. In this step, we take into account inequality constraints provided by boreholes that do not intersect all interfaces, as well as the "litho-stratigraphic pile" defining the formations and their relationships (depositing surfaces or erosion). The goal is to identify quickly and semi-automatically potential errors among the data, up to

  4. Geostatistical characteristic of space-time variation in quality parameters in Klodzko water supply system (SW part of Poland)

    NASA Astrophysics Data System (ADS)

    Namysłowska-Wilczyńska, Barbara

    2015-04-01

    Selected results of research connected with the development of a (3D) geostatistical hydrogeochemical model of the Klodzko city area, dedicated to the spatial and time variation in the quality parameters in the Klodzko water supply system (SW part of Poland) will be presented. The research covers the period 2007 ÷ 2011. Spatial analyses of the variation in three different quality parameters, i.e. Fe iron [g/m3] content, Mn manganese [g/m3] content and NH4+ ammonium ion [g/m3] content, were carried out. Spatial and time variation in the parameters was analyzed on the basis of the data (2007 ÷ 2011). Thematic databases, containing original data on coordinates X, Y (latitude and longitude) and Z (time - years) and on regionalized variables, i.e. the water quality parameters in the Klodzko water supply system, were created. The input for the studies were the chemical determinations of the quality parameters of water samples taken in the Klodzko water supply system area in different periods of time. These data were subjected to spatial analyses using geostatistical methods. The geostatistical parameters of the assumed theoretical models of directional semivariograms functions of the studied water quality parameters, calculated for the time (years) interval, were used in the ordinary (block) kriging estimation. Generally, the behaviour of the quality parameters in the Klodzko water supply system has been found to vary in space and time. Thanks to the multidirectional spatial analyses some regularities in the variation in the water supply system in the Klodzko city area have been identified. In the considered time interval, the shapes of the directional Fe iron content semivariogram show a tendency to vary periodically. The courses of the directional semivariograms of Mn manganese content and NH4+ ammonium ion content show some tendencies towards directional variation over the passing years: distinctly expressed trends of variability for Mn content and stronger for NH4

  5. A novel geotechnical/geostatistical approach for exploration and production of natural gas from multiple geologic strata, Phase 1

    SciTech Connect

    Overbey, W.K. Jr.; Reeves, T.K.; Salamy, S.P.; Locke, C.D.; Johnson, H.R.; Brunk, R.; Hawkins, L. )

    1991-05-01

    This research program has been designed to develop and verify a unique geostatistical approach for finding natural gas resources. The project has been conducted by Beckley College, Inc., and BDM Engineering Services Company (BDMESC) under contract to the US Department of Energy (DOE), Morgantown Energy Technology Center (METC). This section, Volume II, contains a detailed discussion of the methodology used and the geological and production information collected and analyzed for this study. A companion document, Volume 1, provides an overview of the program, technique and results of the study. In combination, Volumes I and II cover the completion of the research undertaken under Phase I of this DOE project, which included the identification of five high-potential sites for natural gas production on the Eccles Quadrangle, Raleigh County, West Virginia. Each of these sites was selected for its excellent potential for gas production from both relatively shallow coalbeds and the deeper, conventional reservoir formations.

  6. Integrating geophysical data for mapping the contamination of industrial sites by polycyclic aromatic hydrocarbons: A geostatistical approach

    SciTech Connect

    Colin, P.; Nicoletis, S.; Froidevaux, R.; Garcia, M.

    1996-12-31

    A case study is presented of building a map showing the probability that the concentration in polycyclic aromatic hydrocarbon (PAH) exceeds a critical threshold. This assessment is based on existing PAH sample data (direct information) and on an electrical resistivity survey (indirect information). Simulated annealing is used to build a model of the range of possible values for PAH concentrations and of the bivariate relationship between PAH concentrations and electrical resistivity. The geostatistical technique of simple indicator kriging is then used, together with the probabilistic model, to infer, at each node of a grid, the range of possible values which the PAH concentration can take. The risk map is then extracted for this characterization of the local uncertainty. The difference between this risk map and a traditional iso-concentration map is then discussed in terms of decision-making.

  7. What have we learned from deterministic geostatistics at highly resolved field sites, as relevant to mass transport processes in sedimentary aquifers?

    NASA Astrophysics Data System (ADS)

    Ritzi, Robert W.; Soltanian, Mohamad Reza

    2015-12-01

    In the method of deterministic geostatistics (sensu Isaaks and Srivastava, 1988), highly-resolved data sets are used to compute sample spatial-bivariate statistics within a deterministic framework. The general goal is to observe what real, highly resolved, sample spatial-bivariate correlation looks like when it is well-quantified in naturally-occurring sedimentary aquifers. Furthermore, it is to understand how this correlation structure, (i.e. shape and correlation range) is related to independent and physically quantifiable attributes of the sedimentary architecture. The approach has evolved among work by Rubin (1995, 2003), Barrash and Clemo (2002), Ritzi et al. (2004, 2007, 2013), Dai et al. (2005), and Ramanathan et al. (2010). In this evolution, equations for sample statistics have been developed which allow tracking the facies types at the heads and tails of lag vectors. The goal is to observe and thereby understand how aspects of the sedimentary architecture affect the well-supported sample statistics. The approach has been used to study heterogeneity at a number of sites, representing a variety of depositional environments, with highly resolved data sets. What have we learned? We offer and support an opinion that the single most important insight derived from these studies is that the structure of spatial-bivariate correlation is essentially the cross-transition probability structure, determined by the sedimentary architecture. More than one scale of hierarchical sedimentary architecture has been represented in these studies, and a hierarchy of cross-transition probability structures was found to define the correlation structure in all cases. This insight allows decomposing contributions from different scales of the sedimentary architecture, and has led to a more fundamental understanding of mass transport processes including mechanical dispersion of solutes within aquifers, and the time-dependent retardation of reactive solutes. These processes can now be

  8. The distribution of arsenic in shallow alluvial groundwater under agricultural land in central Portugal: insights from multivariate geostatistical modeling.

    PubMed

    Andrade, A I A S S; Stigter, T Y

    2013-04-01

    In this study multivariate and geostatistical methods are jointly applied to model the spatial and temporal distribution of arsenic (As) concentrations in shallow groundwater as a function of physicochemical, hydrogeological and land use parameters, as well as to assess the related uncertainty. The study site is located in the Mondego River alluvial body in Central Portugal, where maize, rice and some vegetable crops dominate. In a first analysis scatter plots are used, followed by the application of principal component analysis to two different data matrices, of 112 and 200 samples, with the aim of detecting associations between As levels and other quantitative parameters. In the following phase explanatory models of As are created through factorial regression based on correspondence analysis, integrating both quantitative and qualitative parameters. Finally, these are combined with indicator-geostatistical techniques to create maps indicating the predicted probability of As concentrations in groundwater exceeding the current global drinking water guideline of 10 μg/l. These maps further allow assessing the uncertainty and representativeness of the monitoring network. A clear effect of the redox state on the presence of As is observed, and together with significant correlations with dissolved oxygen, nitrate, sulfate, iron, manganese and alkalinity, points towards the reductive dissolution of Fe (hydr)oxides as the essential mechanism of As release. The association of high As values with rice crop, known to promote reduced environments due to ponding, further corroborates this hypothesis. An additional source of As from fertilizers cannot be excluded, as the correlation with As is higher where rice is associated with vegetables, normally associated with higher fertilization rates. The best explanatory model of As occurrence integrates the parameters season, crop type, well and water depth, nitrate and Eh, though a model without the last two parameters also gives

  9. Geostatistics and multivariate analysis as a tool to characterize volcaniclastic deposits: Application to Nevado de Toluca volcano, Mexico

    NASA Astrophysics Data System (ADS)

    Bellotti, F.; Capra, L.; Sarocchi, D.; D'Antonio, M.

    2010-03-01

    Grain size analysis of volcaniclastic deposits is mainly used to study flow transport and depositional processes, in most cases by comparing some statistical parameters and how they change with distance from the source. In this work the geospatial and multivariate analyses are presented as a strong adaptable geostatistical tool applied to volcaniclastic deposits in order to provide an effective and relatively simple methodology for texture description, deposit discrimination and interpretation of depositional processes. We choose the case of Nevado de Toluca volcano (Mexico) due to existing knowledge of its geological evolution, stratigraphic succession and spatial distribution of volcaniclastic units. Grain size analyses and frequency distribution curves have been carried out to characterize and compare the 28-ka block-and-ash flow deposit associated to a dome destruction episode, and the El Morral debris avalanche deposit originated from the collapse of the south-eastern sector of the volcano. The geostatistical interpolation of sedimentological data allows to realize bidimensional maps draped over the volcano topography, showing the granulometric distribution, sorting and fine material concentration into the whole deposit with respect to topographic changes. In this way, it is possible to analyze a continuous surface of the grain size distribution of volcaniclastic deposits and better understand flow transport processes. The application of multivariate statistic analysis (discriminant function) indicates that this methodology could be useful in discriminating deposits with different origin or different depositional lithofacies within the same deposit. The proposed methodology could be an interesting approach to sustain more classical analysis of volcaniclastic deposits, especially where a clear field classification appears problematic because of a homogeneous texture of the deposits or their scarce and discontinuous outcrops. Our study is an example of the

  10. Characterization of Ambient Air Pollution Measurement Error in a Time-Series Health Study using a Geostatistical Simulation Approach.

    PubMed

    Goldman, Gretchen T; Mulholland, James A; Russell, Armistead G; Gass, Katherine; Strickland, Matthew J; Tolbert, Paige E

    2012-09-01

    In recent years, geostatistical modeling has been used to inform air pollution health studies. In this study, distributions of daily ambient concentrations were modeled over space and time for 12 air pollutants. Simulated pollutant fields were produced for a 6-year time period over the 20-county metropolitan Atlanta area using the Stanford Geostatistical Modeling Software (SGeMS). These simulations incorporate the temporal and spatial autocorrelation structure of ambient pollutants, as well as season and day-of-week temporal and spatial trends; these fields were considered to be the true ambient pollutant fields for the purposes of the simulations that followed. Simulated monitor data at the locations of actual monitors were then generated that contain error representative of instrument imprecision. From the simulated monitor data, four exposure metrics were calculated: central monitor and unweighted, population-weighted, and area-weighted averages. For each metric, the amount and type of error relative to the simulated pollutant fields are characterized and the impact of error on an epidemiologic time-series analysis is predicted. The amount of error, as indicated by a lack of spatial autocorrelation, is greater for primary pollutants than for secondary pollutants and is only moderately reduced by averaging across monitors; more error will result in less statistical power in the epidemiologic analysis. The type of error, as indicated by the correlations of error with the monitor data and with the true ambient concentration, varies with exposure metric, with error in the central monitor metric more of the classical type (i.e., independent of the monitor data) and error in the spatial average metrics more of the Berkson type (i.e., independent of the true ambient concentration). Error type will affect the bias in the health risk estimate, with bias toward the null and away from the null predicted depending on the exposure metric; population-weighting yielded the

  11. Geostatistical validation and cross-validation of magnetometric measurements of soil pollution with Potentially Toxic Elements in problematic areas

    NASA Astrophysics Data System (ADS)

    Fabijańczyk, Piotr; Zawadzki, Jarosław

    2016-04-01

    Field magnetometry is fast method that was previously effectively used to assess the potential soil pollution. One of the most popular devices that are used to measure the soil magnetic susceptibility on the soil surface is a MS2D Bartington. Single reading using MS2D device of soil magnetic susceptibility is low time-consuming but often characterized by considerable errors related to the instrument or environmental and lithogenic factors. In this connection, measured values of soil magnetic susceptibility have to be usually validated using more precise, but also much more expensive, chemical measurements. The goal of this study was to analyze validation methods of magnetometric measurements using chemical analyses of a concentration of elements in soil. Additionally, validation of surface measurements of soil magnetic susceptibility was performed using selected parameters of a distribution of magnetic susceptibility in a soil profile. Validation was performed using selected geostatistical measures of cross-correlation. The geostatistical approach was compared with validation performed using the classic statistics. Measurements were performed at selected areas located in the Upper Silesian Industrial Area in Poland, and in the selected parts of Norway. In these areas soil magnetic susceptibility was measured on the soil surface using a MS2D Bartington device and in the soil profile using MS2C Bartington device. Additionally, soil samples were taken in order to perform chemical measurements. Acknowledgment The research leading to these results has received funding from the Polish-Norwegian Research Programme operated by the National Centre for Research and Development under the Norwegian Financial Mechanism 2009-2014 in the frame of Project IMPACT - Contract No Pol-Nor/199338/45/2013.

  12. Is random access memory random?

    NASA Technical Reports Server (NTRS)

    Denning, P. J.

    1986-01-01

    Most software is contructed on the assumption that the programs and data are stored in random access memory (RAM). Physical limitations on the relative speeds of processor and memory elements lead to a variety of memory organizations that match processor addressing rate with memory service rate. These include interleaved and cached memory. A very high fraction of a processor's address requests can be satified from the cache without reference to the main memory. The cache requests information from main memory in blocks that can be transferred at the full memory speed. Programmers who organize algorithms for locality can realize the highest performance from these computers.

  13. Use of USLE/GIS technology integrated with geostatistics to assess soil erosion risk in different land uses of Indagi Mountain Pass—Çankırı, Turkey

    NASA Astrophysics Data System (ADS)

    Ozcan, A. Ugur; Erpul, Gunay; Basaran, Mustafa; Erdogan, H. Emrah

    2008-02-01

    The universal soil loss equation (USLE) is an erosion model to estimate average soil loss that would generally result from splash, sheet, and rill erosion from agricultural plots. Recently, use of USLE has been extended as a useful tool predicting soil losses and planning control practices by the effective integration of the GIS-based procedures to estimate the factor values on a grid cell basis. This study was performed for five different lands uses of Indağı Mountain Pass, Cankırı to predict the soil erosion risk by the USLE/GIS methodology for planning conservation measures in the site. Of the USLE factors, rainfall-runoff erosivity factor (USLE-R) and topographic factor (USLE-LS) were greatly involved in GIS. These were surfaced by correcting USLE-R site-specifically using DEM and climatic data and by evaluating USLE-LS by the flow accumulation tool using DEM and watershed delineation tool to consider the topographical and hydrological effects on the soil loss. The study assessed the soil erodibility factor (USLE-K) by randomly sampled field properties by geostatistical analysis. Crop management factor for different land-use/land cover type and land use (USLE-C) was assigned to the numerical values from crop and flora type, canopy and density of five different land uses, which are plantation, recreational land, cropland, forest and grassland, by means of reclassifying digital land use map available for the site. Support practice factor (USLE-P) was taken as a unit assuming no erosion control practices. USLE/GIS technology together with the geostatistics combined these major erosion factors to predict average soil loss per unit area per unit time. Resulting soil loss map revealed that spatial average soil loss in terms of the land uses were 1.99, 1.29, 1.21, 1.20, 0.89 t ha-1 year-1 for the cropland, grassland, recreation, plantation and forest, respectively. Since the rate of soil formation was expected to be so slow in Central Anatolia of Turkey and any

  14. Random sequential adsorption on fractals.

    PubMed

    Ciesla, Michal; Barbasz, Jakub

    2012-07-28

    Irreversible adsorption of spheres on flat collectors having dimension d < 2 is studied. Molecules are adsorbed on Sierpinski's triangle and carpet-like fractals (1 < d < 2), and on general Cantor set (d < 1). Adsorption process is modeled numerically using random sequential adsorption (RSA) algorithm. The paper concentrates on measurement of fundamental properties of coverages, i.e., maximal random coverage ratio and density autocorrelation function, as well as RSA kinetics. Obtained results allow to improve phenomenological relation between maximal random coverage ratio and collector dimension. Moreover, simulations show that, in general, most of known dimensional properties of adsorbed monolayers are valid for non-integer dimensions.

  15. A cluster randomized trial of standard quality improvement versus patient-centered interventions to enhance depression care for African Americans in the primary care setting: study protocol NCT00243425

    PubMed Central

    2010-01-01

    Background Several studies document disparities in access to care and quality of care for depression for African Americans. Research suggests that patient attitudes and clinician communication behaviors may contribute to these disparities. Evidence links patient-centered care to improvements in mental health outcomes; therefore, quality improvement interventions that enhance this dimension of care are promising strategies to improve treatment and outcomes of depression among African Americans. This paper describes the design of the BRIDGE (Blacks Receiving Interventions for Depression and Gaining Empowerment) Study. The goal of the study is to compare the effectiveness of two interventions for African-American patients with depression--a standard quality improvement program and a patient-centered quality improvement program. The main hypothesis is that patients in the patient-centered group will have a greater reduction in their depression symptoms, higher rates of depression remission, and greater improvements in mental health functioning at six, twelve, and eighteen months than patients in the standard group. The study also examines patient ratings of care and receipt of guideline-concordant treatment for depression. Methods/Design A total of 36 primary care clinicians and 132 of their African-American patients with major depressive disorder were recruited into a cluster randomized trial. The study uses intent-to-treat analyses to compare the effectiveness of standard quality improvement interventions (academic detailing about depression guidelines for clinicians and disease-oriented care management for their patients) and patient-centered quality improvement interventions (communication skills training to enhance participatory decision-making for clinicians and care management focused on explanatory models, socio-cultural barriers, and treatment preferences for their patients) for improving outcomes over 12 months of follow-up. Discussion The BRIDGE Study

  16. Geostatistical analysis of disease data: accounting for spatial support and population density in the isopleth mapping of cancer mortality risk using area-to-point Poisson kriging

    PubMed Central

    Goovaerts, Pierre

    2006-01-01

    Background Geostatistical techniques that account for spatially varying population sizes and spatial patterns in the filtering of choropleth maps of cancer mortality were recently developed. Their implementation was facilitated by the initial assumption that all geographical units are the same size and shape, which allowed the use of geographic centroids in semivariogram estimation and kriging. Another implicit assumption was that the population at risk is uniformly distributed within each unit. This paper presents a generalization of Poisson kriging whereby the size and shape of administrative units, as well as the population density, is incorporated into the filtering of noisy mortality rates and the creation of isopleth risk maps. An innovative procedure to infer the point-support semivariogram of the risk from aggregated rates (i.e. areal data) is also proposed. Results The novel methodology is applied to age-adjusted lung and cervix cancer mortality rates recorded for white females in two contrasted county geographies: 1) state of Indiana that consists of 92 counties of fairly similar size and shape, and 2) four states in the Western US (Arizona, California, Nevada and Utah) forming a set of 118 counties that are vastly different geographical units. Area-to-point (ATP) Poisson kriging produces risk surfaces that are less smooth than the maps created by a naïve point kriging of empirical Bayesian smoothed rates. The coherence constraint of ATP kriging also ensures that the population-weighted average of risk estimates within each geographical unit equals the areal data for this unit. Simulation studies showed that the new approach yields more accurate predictions and confidence intervals than point kriging of areal data where all counties are simply collapsed into their respective polygon centroids. Its benefit over point kriging increases as the county geography becomes more heterogeneous. Conclusion A major limitation of choropleth maps is the common biased

  17. Quantum to classical randomness extractors

    NASA Astrophysics Data System (ADS)

    Wehner, Stephanie; Berta, Mario; Fawzi, Omar

    2013-03-01

    The goal of randomness extraction is to distill (almost) perfect randomness from a weak source of randomness. When the source yields a classical string X, many extractor constructions are known. Yet, when considering a physical randomness source, X is itself ultimately the result of a measurement on an underlying quantum system. When characterizing the power of a source to supply randomness it is hence a natural question to ask, how much classical randomness we can extract from a quantum system. To tackle this question we here introduce the notion of quantum-to-classical randomness extractors (QC-extractors). We identify an entropic quantity that determines exactly how much randomness can be obtained. Furthermore, we provide constructions of QC-extractors based on measurements in a full set of mutually unbiased bases (MUBs), and certain single qubit measurements. As the first application, we show that any QC-extractor gives rise to entropic uncertainty relations with respect to quantum side information. Such relations were previously only known for two measurements. As the second application, we resolve the central open question in the noisy-storage model [Wehner et al., PRL 100, 220502 (2008)] by linking security to the quantum capacity of the adversary's storage device.

  18. Site characterization methodology for aquifers in support of bioreclamation activities. Volume 2: Borehole flowmeter technique, tracer tests, geostatistics and geology. Final report, August 1987-September 1989

    SciTech Connect

    Young, S.C.

    1993-08-01

    This report discusses a field demonstration of a methodology for characterizing an aquifer's geohydrology in the detail required to design an optimum network of wells and/or infiltration galleries for bioreclamation systems. The project work was conducted on a 1-hectare test site at Columbus AFB, Mississippi. The technical report is divided into two volumes. Volume I describes the test site and the well network, the assumptions, and the application of equations that define groundwater flow to a well, the results of three large-scale aquifer tests, and the results of 160 single-pump tests. Volume II describes the bore hole flowmeter tests, the tracer tests, the geological investigations, the geostatistical analysis and the guidelines for using groundwater models to design bioreclamation systems. Site characterization, Hydraulic conductivity, Groundwater flow, Geostatistics, Geohydrology, Monitoring wells.

  19. [Multivariate geostatistics and GIS-based approach to study the spatial distribution and sources of heavy metals in agricultural soil in the Pearl River Delta, China].

    PubMed

    Cai, Li-mei; Ma, Jin; Zhou, Yong-zhang; Huang, Lan-chun; Dou, Lei; Zhang, Cheng-bo; Fu, Shan-ming

    2008-12-01

    One hundred and eighteen surface soil samples were collected from the Dongguan City, and analyzed for concentration of Cu, Zn, Ni, Cr, Pb, Cd, As, Hg, pH and OM. The spatial distribution and sources of soil heavy metals were studied using multivariate geostatistical methods and GIS technique. The results indicated concentrations of Cu, Zn, Ni, Pb, Cd and Hg were beyond the soil background content in Guangdong province, and especially concentrations of Pb, Cd and Hg were greatly beyond the content. The results of factor analysis group Cu, Zn, Ni, Cr and As in Factor 1, Pb and Hg in Factor 2 and Cd in Factor 3. The spatial maps based on geostatistical analysis show definite association of Factor 1 with the soil parent material, Factor 2 was mainly affected by industries. The spatial distribution of Factor 3 was attributed to anthropogenic influence.

  20. Modeling the effect of clay drapes on pumping test response in a cross-bedded aquifer using multiple-point geostatistics

    NASA Astrophysics Data System (ADS)

    Huysmans, Marijke; Dassargues, Alain

    2012-07-01

    SummaryThis study investigates whether fine-scale clay drapes can cause an anisotropic pumping test response at a much larger scale. A pumping test was performed in a sandbar deposit consisting of cross-bedded units composed of materials with different grain sizes and hydraulic conductivities. The measured drawdown values in the different observation wells reveal an anisotropic or elliptically-shaped pumping cone. The major axis of the pumping ellipse is parallel with the strike of cm to m-scale clay drapes that are observed in several outcrops. To determine (1) whether this large-scale anisotropy can be the result of fine-scale clay drapes and (2) whether application of multiple-point geostatistics can improve interpretation of pumping tests, this pumping test is analyzed with a local 3D groundwater model in which fine-scale sedimentary heterogeneity is modelled using multiple-point geostatistics. To reduce CPU and RAM demand of the multiple-point geostatistical simulation step, edge properties indicating the presence of irregularly-shaped surfaces are directly simulated. Results show that the anisotropic pumping cone can be attributed to the presence of the clay drapes. Incorporating fine-scale clay drapes results in a better fit between observed and calculated drawdowns. These results thus show that fine-scale clay drapes can cause an anisotropic pumping test response at a much larger scale and that the combined approach of multiple-point geostatistics and cell edge properties is an efficient method for integrating fine-scale features in larger scale models.

  1. Geostatistical analysis of space variation in underground water various quality parameters in Kłodzko water intake area (SW part of Poland)

    NASA Astrophysics Data System (ADS)

    Namysłowska-Wilczyńska, Barbara

    2016-09-01

    This paper presents selected results of research connected with the development of a (3D) geostatistical hydrogeochemical model of the Kłodzko Drainage Basin, dedicated to the spatial variation in the different quality parameters of underground water in the water intake area (SW part of Poland). The research covers the period 2011-2012. Spatial analyses of the variation in various quality parameters, i.e., contents of: iron, manganese, ammonium ion, nitrate ion, phosphate ion, total organic carbon, pH redox potential and temperature, were carried out on the basis of the chemical determinations of the quality parameters of underground water samples taken from the wells in the water intake area. Spatial variation in the parameters was analyzed on the basis of data obtained (November 2011) from tests of water taken from 14 existing wells with a depth ranging from 9.5 to 38.0 m b.g.l. The latest data (January 2012) were obtained (gained) from 3 new piezometers, made in other locations in the relevant area. A depth of these piezometers amounts to 9-10 m. Data derived from 14 wells (2011) and 14 wells + 3 piezometers (2012) were subjected to spatial analyses using geostatistical methods. The evaluation of basic statistics of the quality parameters, including their histograms of distributions, scatter diagrams and correlation coefficient values r were presented. The directional semivariogram function γ(h) and the ordinary (block) kriging procedure were used to build the 3D geostatistical model. The geostatistical parameters of the theoretical models of directional semivariograms of the water quality parameters under study, calculated along the wells depth (taking into account the terrain elevation), were used in the ordinary (block) kriging estimation. The obtained results of estimation, i.e., block diagrams allowed us to determine the levels of increased values of estimated averages Z* of underground water quality parameters.

  2. Geostatistical Analysis of Population Density and the Change of Land Cover and Land Use in the Komadugu-Yobe River Basin in Nigeria

    NASA Astrophysics Data System (ADS)

    Tobar, I.; Lee, J.; Black, F. W.; Babamaaji, R. A.

    2014-12-01

    The Komadugu-Yobe River Basin in northeastern Nigeria is an important tributary of Lake Chad and has experienced significant changes in population density and land cover in recent decades. The present study focuses on the application of geostatistical methods to examine the land cover and population density dynamics in the river basin. The geostatistical methods include spatial autocorrelation, overlapping neighborhood statistics with Pearson's correlation coefficient, Moran's I index analysis, and indicator variogram analysis with rose diagram. The land cover and land use maps were constructed from USGS Landsat images and Globcover images from the European Space Agency. The target years of the analysis are 1970, 1986, 2000, 2005, and 2009. The calculation of net changes in land cover indicates significant variation in the changes of rainfed cropland, mosaic cropland, and grassland. Spatial autocorrelation analysis and Moran I index analysis showed that the distribution of land cover is highly clustered. A new GIS geostatistical tool was designed to calculate the overlapping neighborhood statistics with Pearson's correlation coefficient between the land use/land cover and population density datasets. The 10x10 neighborhood cell unit showed a clear correlation between the variables in certain zones of the study area. The ranges calculated from the indicator variograms of land use and land cover and population density showed that the cropland and sparse vegetation are most closely related to the spatial change of population density.

  3. Coupling geostatistics to detailed reservoir description allows better visualization and more accurate characterization/simulation of turbidite reservoirs: Elk Hills oil field, California

    SciTech Connect

    Allan, M.E.; Wilson, M.L.; Wightman, J. )

    1996-01-01

    The Elk Hills giant oilfield, located in the southern San Joaquin Valley of California, has produced 1.1 billion barrels of oil from Miocene and shallow Pliocene reservoirs. 65% of the current 64,000 BOPD production is from the pressure-supported, deeper Miocene turbidite sands. In the turbidite sands of the 31 S structure, large porosity permeability variations in the Main Body B and Western 31 S sands cause problems with the efficiency of the waterflooding. These variations have now been quantified and visualized using geostatistics. The end result is a more detailed reservoir characterization for simulation. Traditional reservoir descriptions based on marker correlations, cross-sections and mapping do not provide enough detail to capture the short-scale stratigraphic heterogeneity needed for adequate reservoir simulation. These deterministic descriptions are inadequate to tie with production data as the thinly bedded sand/shale sequences blur into a falsely homogenous picture. By studying the variability of the geologic petrophysical data vertically within each wellbore and spatially from well to well, a geostatistical reservoir description has been developed. It captures the natural variability of the sands and shales that was lacking from earlier work. These geostatistical studies allow the geologic and petrophysical characteristics to be considered in a probabilistic model. The end-product is a reservoir description that captures the variability of the reservoir sequences and can be used as a more realistic starting point for history matching and reservoir simulation.

  4. Coupling geostatistics to detailed reservoir description allows better visualization and more accurate characterization/simulation of turbidite reservoirs: Elk Hills oil field, California

    SciTech Connect

    Allan, M.E.; Wilson, M.L.; Wightman, J.

    1996-12-31

    The Elk Hills giant oilfield, located in the southern San Joaquin Valley of California, has produced 1.1 billion barrels of oil from Miocene and shallow Pliocene reservoirs. 65% of the current 64,000 BOPD production is from the pressure-supported, deeper Miocene turbidite sands. In the turbidite sands of the 31 S structure, large porosity & permeability variations in the Main Body B and Western 31 S sands cause problems with the efficiency of the waterflooding. These variations have now been quantified and visualized using geostatistics. The end result is a more detailed reservoir characterization for simulation. Traditional reservoir descriptions based on marker correlations, cross-sections and mapping do not provide enough detail to capture the short-scale stratigraphic heterogeneity needed for adequate reservoir simulation. These deterministic descriptions are inadequate to tie with production data as the thinly bedded sand/shale sequences blur into a falsely homogenous picture. By studying the variability of the geologic & petrophysical data vertically within each wellbore and spatially from well to well, a geostatistical reservoir description has been developed. It captures the natural variability of the sands and shales that was lacking from earlier work. These geostatistical studies allow the geologic and petrophysical characteristics to be considered in a probabilistic model. The end-product is a reservoir description that captures the variability of the reservoir sequences and can be used as a more realistic starting point for history matching and reservoir simulation.

  5. Spatial heterogeneity and risk factors for stunting among children under age five in Ethiopia: A Bayesian geo-statistical model

    PubMed Central

    Hagos, Seifu; Hailemariam, Damen; WoldeHanna, Tasew; Lindtjørn, Bernt

    2017-01-01

    Background Understanding the spatial distribution of stunting and underlying factors operating at meso-scale is of paramount importance for intervention designing and implementations. Yet, little is known about the spatial distribution of stunting and some discrepancies are documented on the relative importance of reported risk factors. Therefore, the present study aims at exploring the spatial distribution of stunting at meso- (district) scale, and evaluates the effect of spatial dependency on the identification of risk factors and their relative contribution to the occurrence of stunting and severe stunting in a rural area of Ethiopia. Methods A community based cross sectional study was conducted to measure the occurrence of stunting and severe stunting among children aged 0–59 months. Additionally, we collected relevant information on anthropometric measures, dietary habits, parent and child-related demographic and socio-economic status. Latitude and longitude of surveyed households were also recorded. Local Anselin Moran's I was calculated to investigate the spatial variation of stunting prevalence and identify potential local pockets (hotspots) of high prevalence. Finally, we employed a Bayesian geo-statistical model, which accounted for spatial dependency structure in the data, to identify potential risk factors for stunting in the study area. Results Overall, the prevalence of stunting and severe stunting in the district was 43.7% [95%CI: 40.9, 46.4] and 21.3% [95%CI: 19.5, 23.3] respectively. We identified statistically significant clusters of high prevalence of stunting (hotspots) in the eastern part of the district and clusters of low prevalence (cold spots) in the western. We found out that the inclusion of spatial structure of the data into the Bayesian model has shown to improve the fit for stunting model. The Bayesian geo-statistical model indicated that the risk of stunting increased as the child’s age increased (OR 4.74; 95% Bayesian credible

  6. Identification of hydraulic conductivity structure in sand and gravel aquifers: Cape Cod data set

    USGS Publications Warehouse

    Eggleston, J.R.; Rojstaczer, S.A.; Peirce, J.J.

    1996-01-01

    This study evaluates commonly used geostatistical methods to assess reproduction of hydraulic conductivity (K) structure and sensitivity under limiting amounts of data. Extensive conductivity measurements from the Cape Cod sand and gravel aquifer are used to evaluate two geostatistical estimation methods, conditional mean as an estimate and ordinary kriging, and two stochastic simulation methods, simulated annealing and sequential Gaussian simulation. Our results indicate that for relatively homogeneousand and gravel aquifers such as the Cape Cod aquifer, neither estimation methods nor stochastic simulation methods give highly accurate point predictions of hydraulic conductivity despite the high density of collected data. Although the stochastic simulation methods yielded higher errors than the estimation methods, the stochastic simulation methods yielded better reproduction of the measured In (K) distribution and better reproduction of local contrasts in In (K). The inability of kriging to reproduce high In (K) values, as reaffirmed by this study, provides a strong instigation for choosing stochastic simulation methods to generate conductivity fields when performing fine-scale contaminant transport modeling. Results also indicate that estimation error is relatively insensitive to the number of hydraulic conductivity measurementso long as more than a threshold number of data are used to condition the realizations. This threshold occurs for the Cape Cod site when there are approximately three conductivity measurements per integral volume. The lack of improvement with additional data suggests that although fine-scale hydraulic conductivity structure is evident in the variogram, it is not accurately reproduced by geostatistical estimation methods. If the Cape Cod aquifer spatial conductivity characteristics are indicative of other sand and gravel deposits, then the results on predictive error versus data collection obtained here have significant practical consequences

  7. Common Randomness Principles of Secrecy

    ERIC Educational Resources Information Center

    Tyagi, Himanshu

    2013-01-01

    This dissertation concerns the secure processing of distributed data by multiple terminals, using interactive public communication among themselves, in order to accomplish a given computational task. In the setting of a probabilistic multiterminal source model in which several terminals observe correlated random signals, we analyze secure…

  8. Quantifying randomness in real networks

    PubMed Central

    Orsini, Chiara; Dankulov, Marija M.; Colomer-de-Simón, Pol; Jamakovic, Almerima; Mahadevan, Priya; Vahdat, Amin; Bassler, Kevin E.; Toroczkai, Zoltán; Boguñá, Marián; Caldarelli, Guido; Fortunato, Santo; Krioukov, Dmitri

    2015-01-01

    Represented as graphs, real networks are intricate combinations of order and disorder. Fixing some of the structural properties of network models to their values observed in real networks, many other properties appear as statistical consequences of these fixed observables, plus randomness in other respects. Here we employ the dk-series, a complete set of basic characteristics of the network structure, to study the statistical dependencies between different network properties. We consider six real networks—the Internet, US airport network, human protein interactions, technosocial web of trust, English word network, and an fMRI map of the human brain—and find that many important local and global structural properties of these networks are closely reproduced by dk-random graphs whose degree distributions, degree correlations and clustering are as in the corresponding real network. We discuss important conceptual, methodological, and practical implications of this evaluation of network randomness, and release software to generate dk-random graphs. PMID:26482121

  9. Quantifying randomness in real networks

    NASA Astrophysics Data System (ADS)

    Orsini, Chiara; Dankulov, Marija M.; Colomer-de-Simón, Pol; Jamakovic, Almerima; Mahadevan, Priya; Vahdat, Amin; Bassler, Kevin E.; Toroczkai, Zoltán; Boguñá, Marián; Caldarelli, Guido; Fortunato, Santo; Krioukov, Dmitri

    2015-10-01

    Represented as graphs, real networks are intricate combinations of order and disorder. Fixing some of the structural properties of network models to their values observed in real networks, many other properties appear as statistical consequences of these fixed observables, plus randomness in other respects. Here we employ the dk-series, a complete set of basic characteristics of the network structure, to study the statistical dependencies between different network properties. We consider six real networks--the Internet, US airport network, human protein interactions, technosocial web of trust, English word network, and an fMRI map of the human brain--and find that many important local and global structural properties of these networks are closely reproduced by dk-random graphs whose degree distributions, degree correlations and clustering are as in the corresponding real network. We discuss important conceptual, methodological, and practical implications of this evaluation of network randomness, and release software to generate dk-random graphs.

  10. Quantifying randomness in real networks.

    PubMed

    Orsini, Chiara; Dankulov, Marija M; Colomer-de-Simón, Pol; Jamakovic, Almerima; Mahadevan, Priya; Vahdat, Amin; Bassler, Kevin E; Toroczkai, Zoltán; Boguñá, Marián; Caldarelli, Guido; Fortunato, Santo; Krioukov, Dmitri

    2015-10-20

    Represented as graphs, real networks are intricate combinations of order and disorder. Fixing some of the structural properties of network models to their values observed in real networks, many other properties appear as statistical consequences of these fixed observables, plus randomness in other respects. Here we employ the dk-series, a complete set of basic characteristics of the network structure, to study the statistical dependencies between different network properties. We consider six real networks--the Internet, US airport network, human protein interactions, technosocial web of trust, English word network, and an fMRI map of the human brain--and find that many important local and global structural properties of these networks are closely reproduced by dk-random graphs whose degree distributions, degree correlations and clustering are as in the corresponding real network. We discuss important conceptual, methodological, and practical implications of this evaluation of network randomness, and release software to generate dk-random graphs.

  11. Random distributed feedback fibre lasers

    NASA Astrophysics Data System (ADS)

    Turitsyn, Sergei K.; Babin, Sergey A.; Churkin, Dmitry V.; Vatnik, Ilya D.; Nikulin, Maxim; Podivilov, Evgenii V.

    2014-09-01

    of a stationary near-Gaussian beam with a narrow spectrum. A random distributed feedback fibre laser has efficiency and performance that are comparable to and even exceed those of similar conventional fibre lasers. The key features of the generated radiation of random distributed feedback fibre lasers include: a stationary narrow-band continuous modeless spectrum that is free of mode competition, nonlinear power broadening, and an output beam with a Gaussian profile in the fundamental transverse mode (generated both in single mode and multi-mode fibres). This review presents the current status of research in the field of random fibre lasers and shows their potential and perspectives. We start with an introductory overview of conventional distributed feedback lasers and traditional random lasers to set the stage for discussion of random fibre lasers. We then present a theoretical analysis and experimental studies of various random fibre laser configurations, including widely tunable, multi-wavelength, narrow-band generation, and random fibre lasers operating in different spectral bands in the 1-1.6 μm range. Then we discuss existing and future applications of random fibre lasers, including telecommunication and distributed long reach sensor systems. A theoretical description of random lasers is very challenging and is strongly linked with the theory of disordered systems and kinetic theory. We outline two key models governing the generation of random fibre lasers: the average power balance model and the nonlinear Schrödinger equation based model. Recently invented random distributed feedback fibre lasers represent a new and exciting field of research that brings together such diverse areas of science as laser physics, the theory of disordered systems, fibre optics and nonlinear science. Stable random generation in optical fibre opens up new possibilities for research on wave transport and localization in disordered media. We hope that this review will provide

  12. Random matrix techniques in quantum information theory

    SciTech Connect

    Collins, Benoît; Nechita, Ion

    2016-01-15

    The purpose of this review is to present some of the latest developments using random techniques, and in particular, random matrix techniques in quantum information theory. Our review is a blend of a rather exhaustive review and of more detailed examples—coming mainly from research projects in which the authors were involved. We focus on two main topics, random quantum states and random quantum channels. We present results related to entropic quantities, entanglement of typical states, entanglement thresholds, the output set of quantum channels, and violations of the minimum output entropy of random channels.

  13. 76 FR 1448 - Random Drug Testing Rate for Covered Crewmembers

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-01-10

    ... SECURITY Coast Guard Random Drug Testing Rate for Covered Crewmembers AGENCY: Coast Guard, DHS. ACTION: Notice of minimum random drug testing rate. SUMMARY: The Coast Guard has set the calendar year 2011 minimum random drug testing rate at 50 percent of covered crewmembers. DATES: The minimum random...

  14. 76 FR 79204 - Random Drug Testing Rate for Covered Crewmembers

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-12-21

    ... SECURITY Coast Guard Random Drug Testing Rate for Covered Crewmembers AGENCY: Coast Guard, DHS. ACTION: Notice of minimum random drug testing rate. SUMMARY: The Coast Guard has set the calendar year 2012 minimum random drug testing rate at 50 percent of covered crewmembers. DATES: The minimum random...

  15. GY SAMPLING THEORY AND GEOSTATISTICS: ALTERNATE MODELS OF VARIABILITY IN CONTINUOUS MEDIA

    EPA Science Inventory



    In the sampling theory developed by Pierre Gy, sample variability is modeled as the sum of a set of seven discrete error components. The variogram used in geostatisties provides an alternate model in which several of Gy's error components are combined in a continuous mode...

  16. Detection of terrain indices related to soil salinity and mapping salt-affected soils using remote sensing and geostatistical techniques.

    PubMed

    Triki Fourati, Hela; Bouaziz, Moncef; Benzina, Mourad; Bouaziz, Samir

    2017-04-01

    Traditional surveying methods of soil properties over landscapes are dramatically cost and time-consuming. Thus, remote sensing is a proper choice for monitoring environmental problem. This research aims to study the effect of environmental factors on soil salinity and to map the spatial distribution of this salinity over the southern east part of Tunisia by means of remote sensing and geostatistical techniques. For this purpose, we used Advanced Spaceborne Thermal Emission and Reflection Radiometer data to depict geomorphological parameters: elevation, slope, plan curvature (PLC), profile curvature (PRC), and aspect. Pearson correlation between these parameters and soil electrical conductivity (ECsoil) showed that mainly slope and elevation affect the concentration of salt in soil. Moreover, spectral analysis illustrated the high potential of short-wave infrared (SWIR) bands to identify saline soils. To map soil salinity in southern Tunisia, ordinary kriging (OK), minimum distance (MD) classification, and simple regression (SR) were used. The findings showed that ordinary kriging technique provides the most reliable performances to identify and classify saline soils over the study area with a root mean square error of 1.83 and mean error of 0.018.

  17. Estimating the volume and age of water stored in global lakes using a geo-statistical approach

    NASA Astrophysics Data System (ADS)

    Messager, Mathis Loïc; Lehner, Bernhard; Grill, Günther; Nedeva, Irena; Schmitt, Oliver

    2016-12-01

    Lakes are key components of biogeochemical and ecological processes, thus knowledge about their distribution, volume and residence time is crucial in understanding their properties and interactions within the Earth system. However, global information is scarce and inconsistent across spatial scales and regions. Here we develop a geo-statistical model to estimate the volume of global lakes with a surface area of at least 10 ha based on the surrounding terrain information. Our spatially resolved database shows 1.42 million individual polygons of natural lakes with a total surface area of 2.67 × 106 km2 (1.8% of global land area), a total shoreline length of 7.2 × 106 km (about four times longer than the world's ocean coastline) and a total volume of 181.9 × 103 km3 (0.8% of total global non-frozen terrestrial water stocks). We also compute mean and median hydraulic residence times for all lakes to be 1,834 days and 456 days, respectively.

  18. Sediment distribution pattern mapped from the combination of objective analysis and geostatistics in the large shallow Taihu Lake, China.

    PubMed

    Luo, Lian-Cong; Qin, Bo-Qiang; Zhu, Guang-Wei

    2004-01-01

    Investigation was made into sediment depth at 723 irregularly scattered measurement points which cover all the regions in Taihu Lake, China. The combination of successive correction scheme and geostatistical method was used to get all the values of recent sediment thickness at the 69 x 69 grids in the whole lake. The results showed that there is the sig