Model Selection for Geostatistical Models
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.
Model selection for geostatistical models.
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.
Geostatistical Modeling of Pore Velocity
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.
NASA Astrophysics Data System (ADS)
Nowak, W.; de Barros, F. P. J.; Rubin, Y.
2009-04-01
Geostatistical optimal design optimizes subsurface exploration for maximum information towards task-specific prediction goals. Until recently, geostatistical design studies have assumed that the geostatistical description (i.e., the mean, trends, covariance models and their parameters) is given a priori, even if only few or no data offer support for such assumptions. This is in contradiction with the fact that the bulk of data acquisition is merely being planned at this stage. We believe that geostatistical design should comply with the following four guidelines: 1. Avoid unjustified a priori assumptions on the geostatistical description such as claiming certainty in the geostatistical model, but to acknowledge the inevitable uncertainty of geostatistical descriptions, 2. Reduce geostatistical model uncertainty as secondary design objective, 3. Rate this secondary objective optimal for the overall prediction goal and 4. Be robust even under inaccurate geostatistical assumptions. Bayesian Geostatistical Design (Diggle und Lophaven, 2006) follows the above four guidelines by considering uncertain covariance model parameters. These authors considered a kriging-like prediction task, using the spatial average of the estimation variance as objective function for the design. We transfer their concept from kriging-like applications to geostatistical inverse problems, thus generalizing towards arbitrary hydrogeological or geophysical data and prediction goals. A remaining concern is that we deem it inappropriate to consider parametric uncertainty only within a single covariance model. The Matérn family of covariance functions has an additional shape parameter, and so allows for uncertain smoothness and shape of the covariance function (Zhang and Rubin, submitted to WRR). Controlling model shape by a parameter converts covariance model selection to parameter identification and resembles Bayesian model averaging over a continuous spectrum of covariance models. We illustrate
A PC-Windows-Based program for geostatistical modeling application
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.
Geostatistics: models and tools for the earth sciences
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.
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.
[Geostatistical modeling of Ascaris lumbricoides infection].
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.
Preferential sampling and Bayesian geostatistics: Statistical modeling and examples.
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.
Random spatial processes and geostatistical models for soil variables
NASA Astrophysics Data System (ADS)
Lark, R. M.
2009-04-01
Geostatistical models of soil variation have been used to considerable effect to facilitate efficient and powerful prediction of soil properties at unsampled sites or over partially sampled regions. Geostatistical models can also be used to investigate the scaling behaviour of soil process models, to design sampling strategies and to account for spatial dependence in the random effects of linear mixed models for spatial variables. However, most geostatistical models (variograms) are selected for reasons of mathematical convenience (in particular, to ensure positive definiteness of the corresponding variables). They assume some underlying spatial mathematical operator which may give a good description of observed variation of the soil, but which may not relate in any clear way to the processes that we know give rise to that observed variation in the real world. In this paper I shall argue that soil scientists should pay closer attention to the underlying operators in geostatistical models, with a view to identifying, where ever possible, operators that reflect our knowledge of processes in the soil. I shall illustrate how this can be done in the case of two problems. The first exemplar problem is the definition of operators to represent statistically processes in which the soil landscape is divided into discrete domains. This may occur at disparate scales from the landscape (outcrops, catchments, fields with different landuse) to the soil core (aggregates, rhizospheres). The operators that underly standard geostatistical models of soil variation typically describe continuous variation, and so do not offer any way to incorporate information on processes which occur in discrete domains. I shall present the Poisson Voronoi Tessellation as an alternative spatial operator, examine its corresponding variogram, and apply these to some real data. The second exemplar problem arises from different operators that are equifinal with respect to the variograms of the
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.
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...
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...
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
Fractal and geostatistical methods for modeling of a fracture network
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.
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.
3D vadose zone modeling using geostatistical inferences
Knutson, C.F.; Lee, C.B.
1991-01-01
In developing a 3D model of the 600 ft thick interbedded basalt and sediment complex that constitutes the vadose zone at the Radioactive Waste Management Complex (RWMC) at the Idaho National Engineering Laboratory (INEL) geostatistical data were captured for 12--15 parameters (e.g. permeability, porosity, saturation, etc. and flow height, flow width, flow internal zonation, etc.). This two scale data set was generated from studies of subsurface core and geophysical log suites at RWMC and from surface outcrop exposures located at the Box Canyon of the Big Lost River and from Hell's Half Acre lava field all located in the general RWMC area. Based on these currently available data, it is possible to build a 3D stochastic model that utilizes: cumulative distribution functions obtained from the geostatistical data; backstripping and rebuilding of stratigraphic units; an expert'' system that incorporates rules based on expert geologic analysis and experimentally derived geostatistics for providing: (a) a structural and isopach map of each layer, (b) a realization of the flow geometry of each basalt flow unit, and (c) a realization of the internal flow parameters (eg permeability, porosity, and saturation) for each flow. 10 refs., 4 figs., 1 tab.
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
Analysis of dengue fever risk using geostatistics model in bone regency
NASA Astrophysics Data System (ADS)
Amran, Stang, Mallongi, Anwar
2017-03-01
This research aim is to analysis of dengue fever risk based on Geostatistics model in Bone Regency. Risk levels of dengue fever are denoted by parameter of Binomial distribution. Effect of temperature, rainfalls, elevation, and larvae abundance are investigated through Geostatistics model. Bayesian hierarchical method is used in estimation process. Using dengue fever data in eleven locations this research shows that temperature and rainfall have significant effect of dengue fever risk in Bone regency.
Bayesian Geostatistical Modeling of Leishmaniasis Incidence in Brazil
Karagiannis-Voules, Dimitrios-Alexios; Scholte, Ronaldo G. C.; Guimarães, Luiz H.; Utzinger, Jürg; Vounatsou, Penelope
2013-01-01
Background Leishmaniasis is endemic in 98 countries with an estimated 350 million people at risk and approximately 2 million cases annually. Brazil is one of the most severely affected countries. Methodology We applied Bayesian geostatistical negative binomial models to analyze reported incidence data of cutaneous and visceral leishmaniasis in Brazil covering a 10-year period (2001–2010). Particular emphasis was placed on spatial and temporal patterns. The models were fitted using integrated nested Laplace approximations to perform fast approximate Bayesian inference. Bayesian variable selection was employed to determine the most important climatic, environmental, and socioeconomic predictors of cutaneous and visceral leishmaniasis. Principal Findings For both types of leishmaniasis, precipitation and socioeconomic proxies were identified as important risk factors. The predicted number of cases in 2010 were 30,189 (standard deviation [SD]: 7,676) for cutaneous leishmaniasis and 4,889 (SD: 288) for visceral leishmaniasis. Our risk maps predicted the highest numbers of infected people in the states of Minas Gerais and Pará for visceral and cutaneous leishmaniasis, respectively. Conclusions/Significance Our spatially explicit, high-resolution incidence maps identified priority areas where leishmaniasis control efforts should be targeted with the ultimate goal to reduce disease incidence. PMID:23675545
Bayesian Geostatistical Modeling of Malaria Indicator Survey Data in Angola
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
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.
Use of geostatistical modeling to capture complex geology in finite-element analyses
Rautman, C.A.; Longenbaugh, R.S.; Ryder, E.E.
1995-12-01
This paper summarizes a number of transient thermal analyses performed for a representative two-dimensional cross section of volcanic tuffs at Yucca Mountain using the finite element, nonlinear heat-conduction code COYOTE-II. In addition to conventional design analyses, in which material properties are formulated as a uniform single material and as horizontally layered, internally uniform matters, an attempt was made to increase the resemblance of the thermal property field to the actual geology by creating two fairly complex, geologically realistic models. The first model was created by digitizing an existing two-dimensional geologic cross section of Yucca Mountain. The second model was created using conditional geostatistical simulation. Direct mapping of geostatistically generated material property fields onto finite element computational meshes was demonstrated to yield temperature fields approximately equivalent to those generated through more conventional procedures. However, the ability to use the geostatistical models offers a means of simplifying the physical-process analyses.
Geostatistical modeling of a heterogeneous site bordering the Venice lagoon, Italy.
Trevisani, Sebastiano; Fabbri, Paolo
2010-01-01
Geostatistical methods are well suited for analyzing the local and spatial uncertainties that accompany the modeling of highly heterogeneous three-dimensional (3D) geological architectures. The spatial modeling of 3D hydrogeological architectures is crucial for polluted site characterization, in regards to both groundwater modeling and planning remediation procedures. From this perspective, the polluted site of Porto Marghera, located on the periphery of the Venice lagoon, represents an interesting example. For this site, the available dense spatial sampling network, with 769 boreholes over an area of 6 km(2), allows us to evaluate the high geological heterogeneity by means of indicator kriging and sequential indicator simulation. We show that geostatistical methodologies and ad hoc post processing of geostatistical analysis results allow us to effectively analyze the high hydrogeological heterogeneity of the studied site.
Brain lesion detection in MRI with fuzzy and geostatistical models.
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.
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.
Integrated geostatistics for modeling fluid contacts and shales in Prudhoe Bay
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.
Monte Carlo Analysis of Reservoir Models Using Seismic Data and Geostatistical Models
NASA Astrophysics Data System (ADS)
Zunino, A.; Mosegaard, K.; Lange, K.; Melnikova, Y.; Hansen, T. M.
2013-12-01
We present a study on the analysis of petroleum reservoir models consistent with seismic data and geostatistical constraints performed on a synthetic reservoir model. Our aim is to invert directly for structure and rock bulk properties of the target reservoir zone. To infer the rock facies, porosity and oil saturation seismology alone is not sufficient but a rock physics model must be taken into account, which links the unknown properties to the elastic parameters. We then combine a rock physics model with a simple convolutional approach for seismic waves to invert the "measured" seismograms. To solve this inverse problem, we employ a Markov chain Monte Carlo (MCMC) method, because it offers the possibility to handle non-linearity, complex and multi-step forward models and provides realistic estimates of uncertainties. However, for large data sets the MCMC method may be impractical because of a very high computational demand. To face this challenge one strategy is to feed the algorithm with realistic models, hence relying on proper prior information. To address this problem, we utilize an algorithm drawn from geostatistics to generate geologically plausible models which represent samples of the prior distribution. The geostatistical algorithm learns the multiple-point statistics from prototype models (in the form of training images), then generates thousands of different models which are accepted or rejected by a Metropolis sampler. To further reduce the computation time we parallelize the software and run it on multi-core machines. The solution of the inverse problem is then represented by a collection of reservoir models in terms of facies, porosity and oil saturation, which constitute samples of the posterior distribution. We are finally able to produce probability maps of the properties we are interested in by performing statistical analysis on the collection of solutions.
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.
A mixed-model moving-average approach to geostatistical modeling in stream networks.
Peterson, Erin E; Ver Hoef, Jay M
2010-03-01
Spatial autocorrelation is an intrinsic characteristic in freshwater stream environments where nested watersheds and flow connectivity may produce patterns that are not captured by Euclidean distance. Yet, many common autocovariance functions used in geostatistical models are statistically invalid when Euclidean distance is replaced with hydrologic distance. We use simple worked examples to illustrate a recently developed moving-average approach used to construct two types of valid autocovariance models that are based on hydrologic distances. These models were designed to represent the spatial configuration, longitudinal connectivity, discharge, and flow direction in a stream network. They also exhibit a different covariance structure than Euclidean models and represent a true difference in the way that spatial relationships are represented. Nevertheless, the multi-scale complexities of stream environments may not be fully captured using a model based on one covariance structure. We advocate using a variance component approach, which allows a mixture of autocovariance models (Euclidean and stream models) to be incorporated into a single geostatistical model. As an example, we fit and compare "mixed models," based on multiple covariance structures, for a biological indicator. The mixed model proves to be a flexible approach because many sources of information can be incorporated into a single model.
Geostatistical applications in ground-water modeling in south-central Kansas
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
Validation and comparison of geostatistical and spline models for spatial stream networks.
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.
A geostatistical methodology to assess the accuracy of unsaturated flow models
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.
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.
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.
A conceptual sedimentological-geostatistical model of aquifer heterogeneity based on outcrop studies
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.
2D Forward Modeling of Gravity Data Using Geostatistically Generated Subsurface Density Variations
NASA Astrophysics Data System (ADS)
Phelps, G. A.
2015-12-01
Two-dimensional (2D) forward models of synthetic gravity anomalies are calculated and compared to observed gravity anomalies using geostatistical models of density variations in the subsurface, constrained by geologic data. These models have an advantage over forward gravity models generated using polygonal bodies of homogeneous density because the homogeneous density restriction is relaxed, allowing density variations internal to geologic bodies to be considered. By discretizing the subsurface and calculating the cumulative gravitational effect of each cell, multiple forward models can be generated for a given geologic body, which expands the exploration of the solution space. Furthermore, the stochastic models can be designed to match the observed statistical properties of the internal densities of the geologic units being modeled. The results of such stochastically generated forward gravity models can then be compared with the observed data. To test this modeling approach, we compared stochastic forward gravity models of 2D geologic cross-sections to gravity data collected along a profile across the Vaca Fault near Fairfield, California. Three conceptual geologic models were created, each representing a distinct fault block scenario (normal, strike-slip, reverse) with four rock units in each model. Using fixed rock unit boundaries, the units were populated with geostatistically generated density values, characterized by their respective histogram and vertical variogram. The horizontal variogram could not be estimated because of lack of data, and was therefore left as a free parameter. Each fault block model had multiple geostatistical realizations of density associated with it. Forward models of gravity were then generated from the fault block model realizations, and rejection sampling was used to determine viable fault block density models. Given the constraints on subsurface density, the normal and strike-slip fault model were the most likely.
NASA Astrophysics Data System (ADS)
Muthusamy, Manoranjan; Schellart, Alma; Tait, Simon; Heuvelink, Gerard B. M.
2017-04-01
Geostatistical methods have been used to analyse the spatial correlation structure of rainfall at various spatial scales, but its application to estimate the level of uncertainty in rainfall upscaling has not been fully explored mainly due to its inherent complexity and demanding data requirements. In this study we presented a method to overcome these challenges and predict AARI together with associated uncertainty using geostatistical upscaling. Rainfall data collected from a cluster of eight paired rain gauges in a 400 × 200 sq. 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. This study shows that for small time and space scales the use of a single geostatistical model based on a single variogram is not appropriate and a distinction between rainfall intensity classes and length of temporal averaging intervals should be made
Chonggang Xu; Hong S. He; Yuanman Hu; Yu Chang; Xiuzhen Li; Rencang Bu
2005-01-01
Geostatistical stochastic simulation is always combined with Monte Carlo method to quantify the uncertainty in spatial model simulations. However, due to the relatively long running time of spatially explicit forest models as a result of their complexity, it is always infeasible to generate hundreds or thousands of Monte Carlo simulations. Thus, it is of great...
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.
NASA Astrophysics Data System (ADS)
Yan, Hongxiang; Moradkhani, Hamid; Abbaszadeh, Peyman
2017-04-01
Assimilation of satellite soil moisture and streamflow data into hydrologic models using has received increasing attention over the past few years. Currently, these observations are increasingly used to improve the model streamflow and soil moisture predictions. However, the performance of this land data assimilation (DA) system still suffers from two limitations: 1) satellite data scarcity and quality; and 2) particle weight degeneration. In order to overcome these two limitations, we propose two possible solutions in this study. First, the general Gaussian geostatistical approach is proposed to overcome the limitation in the space/time resolution of satellite soil moisture products thus improving their accuracy at uncovered/biased grid cells. Secondly, an evolutionary PF approach based on Genetic Algorithm (GA) and Markov Chain Monte Carlo (MCMC), the so-called EPF-MCMC, is developed to further reduce weight degeneration and improve the robustness of the land DA system. This study provides a detailed analysis of the joint and separate assimilation of streamflow and satellite soil moisture into a distributed Sacramento Soil Moisture Accounting (SAC-SMA) model, with the use of recently developed EPF-MCMC and the general Gaussian geostatistical approach. Performance is assessed over several basins in the USA selected from Model Parameter Estimation Experiment (MOPEX) and located in different climate regions. The results indicate that: 1) the general Gaussian approach can predict the soil moisture at uncovered grid cells within the expected satellite data quality threshold; 2) assimilation of satellite soil moisture inferred from the general Gaussian model can significantly improve the soil moisture predictions; and 3) in terms of both deterministic and probabilistic measures, the EPF-MCMC can achieve better streamflow predictions. These results recommend that the geostatistical model is a helpful tool to aid the remote sensing technique and the EPF-MCMC is a
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
A Gibbs sampler for inequality-constrained geostatistical interpolation and inverse modeling
NASA Astrophysics Data System (ADS)
Michalak, Anna M.
2008-09-01
Interpolation and inverse modeling problems are ubiquitous in environmental sciences. In many applications, the parameters being estimated or mapped have physical constraints, such as nonnegativity (e.g. concentration, hydraulic conductivity), solubility limits, censored data (e.g. due to dry wells or detection limits), and other physical boundaries or missing data. Geostatistical interpolation and inverse modeling techniques have often been applied for estimating such parameters, but these methods typically cannot enforce physical constraints. This paper describes a statistically rigorous and computationally efficient Gibbs sampler, a Markov chain Monte Carlo technique, based on an a priori truncated Gaussian distribution model, which allows for multiple and variable physical constraints to be enforced within a geostatistical framework. Sample interpolation and inverse modeling applications confirm that estimates, uncertainty bounds and conditional simulations reflect the specified constraints, leading to conclusions that are more consistent with the underlying conceptual model, and provide a more accurate measure of the posterior uncertainty of the parameters being estimated. In addition, especially in inverse modeling applications, a posteriori confidence bounds are narrower even in areas where constraints are not imposed. The method is applicable in multiple dimensions, for data with or without measurement error, and with any variogram model.
Building on crossvalidation for increasing the quality of geostatistical modeling
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.
Joint space-time geostatistical model for air quality surveillance
NASA Astrophysics Data System (ADS)
Russo, A.; Soares, A.; Pereira, M. J.
2009-04-01
Air pollution and peoples' generalized concern about air quality are, nowadays, considered to be a global problem. Although the introduction of rigid air pollution regulations has reduced pollution from industry and power stations, the growing number of cars on the road poses a new pollution problem. Considering the characteristics of the atmospheric circulation and also the residence times of certain pollutants in the atmosphere, a generalized and growing interest on air quality issues led to research intensification and publication of several articles with quite different levels of scientific depth. As most natural phenomena, air quality can be seen as a space-time process, where space-time relationships have usually quite different characteristics and levels of uncertainty. As a result, the simultaneous integration of space and time is not an easy task to perform. This problem is overcome by a variety of methodologies. The use of stochastic models and neural networks to characterize space-time dispersion of air quality is becoming a common practice. The main objective of this work is to produce an air quality model which allows forecasting critical concentration episodes of a certain pollutant by means of a hybrid approach, based on the combined use of neural network models and stochastic simulations. A stochastic simulation of the spatial component with a space-time trend model is proposed to characterize critical situations, taking into account data from the past and a space-time trend from the recent past. To identify near future critical episodes, predicted values from neural networks are used at each monitoring station. In this paper, we describe the design of a hybrid forecasting tool for ambient NO2 concentrations in Lisbon, Portugal.
PCTO-SIM: Multiple-point geostatistical modeling using parallel conditional texture optimization
NASA Astrophysics Data System (ADS)
Pourfard, Mohammadreza; Abdollahifard, Mohammad J.; Faez, Karim; Motamedi, Sayed Ahmad; Hosseinian, Tahmineh
2017-05-01
Multiple-point Geostatistics is a well-known general statistical framework by which complex geological phenomena have been modeled efficiently. Pixel-based and patch-based are two major categories of these methods. In this paper, the optimization-based category is used which has a dual concept in texture synthesis as texture optimization. Our extended version of texture optimization uses the energy concept to model geological phenomena. While honoring the hard point, the minimization of our proposed cost function forces simulation grid pixels to be as similar as possible to training images. Our algorithm has a self-enrichment capability and creates a richer training database from a sparser one through mixing the information of all surrounding patches of the simulation nodes. Therefore, it preserves pattern continuity in both continuous and categorical variables very well. It also shows a fuzzy result in its every realization similar to the expected result of multi realizations of other statistical models. While the main core of most previous Multiple-point Geostatistics methods is sequential, the parallel main core of our algorithm enabled it to use GPU efficiently to reduce the CPU time. One new validation method for MPS has also been proposed in this paper.
Del Monego, Maurici; Ribeiro, Paulo Justiniano; Ramos, Patrícia
2015-04-01
In this work, kriging with covariates is used to model and map the spatial distribution of salinity measurements gathered by an autonomous underwater vehicle in a sea outfall monitoring campaign aiming to distinguish the effluent plume from the receiving waters and characterize its spatial variability in the vicinity of the discharge. Four different geostatistical linear models for salinity were assumed, where the distance to diffuser, the west-east positioning, and the south-north positioning were used as covariates. Sample variograms were fitted by the Matèrn models using weighted least squares and maximum likelihood estimation methods as a way to detect eventual discrepancies. Typically, the maximum likelihood method estimated very low ranges which have limited the kriging process. So, at least for these data sets, weighted least squares showed to be the most appropriate estimation method for variogram fitting. The kriged maps show clearly the spatial variation of salinity, and it is possible to identify the effluent plume in the area studied. The results obtained show some guidelines for sewage monitoring if a geostatistical analysis of the data is in mind. It is important to treat properly the existence of anomalous values and to adopt a sampling strategy that includes transects parallel and perpendicular to the effluent dispersion.
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.
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
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
Modelling ambient ozone in an urban area using an objective model and geostatistical algorithms
NASA Astrophysics Data System (ADS)
Moral, Francisco J.; Rebollo, Francisco J.; Valiente, Pablo; López, Fernando; Muñoz de la Peña, Arsenio
2012-12-01
Ground-level tropospheric ozone is one of the air pollutants of most concern. Ozone levels continue to exceed both target values and the long-term objectives established in EU legislation to protect human health and prevent damage to ecosystems, agricultural crops and materials. Researchers or decision-makers frequently need information about atmospheric pollution patterns in urbanized areas. The preparation of this type of information is a complex task, due to the influence of several factors and their variability over time. In this work, some results of urban ozone distribution patterns in the city of Badajoz, which is the largest (140,000 inhabitants) and most industrialized city in Extremadura region (southwest Spain) are shown. Twelve sampling campaigns, one per month, were carried out to measure ambient air ozone concentrations, during periods that were selected according to favourable conditions to ozone production, using an automatic portable analyzer. Later, to evaluate the overall ozone level at each sampling location during the time interval considered, the measured ozone data were analysed using a new methodology based on the formulation of the Rasch model. As a result, a measure of overall ozone level which consolidates the monthly ground-level ozone measurements was obtained, getting moreover information about the influence on the overall ozone level of each monthly ozone measure. Finally, overall ozone level at locations where no measurements were available was estimated with geostatistical techniques and hazard assessment maps based on the spatial distribution of ozone were also generated.
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
Geostatistical three-dimensional modeling of oolite shoals, St. Louis Limestone, southwest Kansas
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.
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.
Malaria risk in Nigeria: Bayesian geostatistical modelling of 2010 malaria indicator survey data.
Adigun, Abbas B; Gajere, Efron N; Oresanya, Olusola; Vounatsou, Penelope
2015-04-14
In 2010, the National Malaria Control Programme with the support of Roll Back Malaria partners implemented a nationally representative Malaria Indicator Survey (MIS), which assembled malaria burden and control intervention related data. The MIS data were analysed to produce a contemporary smooth map of malaria risk and evaluate the control interventions effects on parasitaemia risk after controlling for environmental/climatic, demographic and socioeconomic characteristics. A Bayesian geostatistical logistic regression model was fitted on the observed parasitological prevalence data. Important environmental/climatic risk factors of parasitaemia were identified by applying Bayesian variable selection within geostatistical model. The best model was employed to predict the disease risk over a grid of 4 km(2) resolution. Validation was carried out to assess model predictive performance. Various measures of control intervention coverage were derived to estimate the effects of interventions on parasitaemia risk after adjusting for environmental, socioeconomic and demographic factors. Normalized difference vegetation index and rainfall were identified as important environmental/climatic predictors of malaria risk. The population adjusted risk estimates ranges from 6.46% in Lagos state to 43.33% in Borno. Interventions appear to not have important effect on malaria risk. The odds of parasitaemia appears to be on downward trend with improved socioeconomic status and living in rural areas increases the odds of testing positive to malaria parasites. Older children also have elevated risk of malaria infection. The produced maps and estimates of parasitaemic children give an important synoptic view of current parasite prevalence in the country. Control activities will find it a useful tool in identifying priority areas for intervention.
Model-Based Geostatistical Mapping of the Prevalence of Onchocerca volvulus in West Africa
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
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.
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.
Schur, Nadine; Hürlimann, Eveline; Stensgaard, Anna-Sofie; Chimfwembe, Kingford; Mushinge, Gabriel; Simoonga, Christopher; Kabatereine, Narcis B; Kristensen, Thomas K; Utzinger, Jürg; Vounatsou, Penelope
2013-11-01
Schistosomiasis remains one of the most prevalent parasitic diseases in the tropics and subtropics, but current statistics are outdated due to demographic and ecological transformations and ongoing control efforts. Reliable risk estimates are important to plan and evaluate interventions in a spatially explicit and cost-effective manner. We analysed a large ensemble of georeferenced survey data derived from an open-access neglected tropical diseases database to create smooth empirical prevalence maps for Schistosoma mansoni and Schistosoma haematobium for a total of 13 countries of eastern Africa. Bayesian geostatistical models based on climatic and other environmental data were used to account for potential spatial clustering in spatially structured exposures. Geostatistical variable selection was employed to reduce the set of covariates. Alignment factors were implemented to combine surveys on different age-groups and to acquire separate estimates for individuals aged ≤20 years and entire communities. Prevalence estimates were combined with population statistics to obtain country-specific numbers of Schistosoma infections. We estimate that 122 million individuals in eastern Africa are currently infected with either S. mansoni, or S. haematobium, or both species concurrently. Country-specific population-adjusted prevalence estimates range between 12.9% (Uganda) and 34.5% (Mozambique) for S. mansoni and between 11.9% (Djibouti) and 40.9% (Mozambique) for S. haematobium. Our models revealed that infection risk in Burundi, Eritrea, Ethiopia, Kenya, Rwanda, Somalia and Sudan might be considerably higher than previously reported, while in Mozambique and Tanzania, the risk might be lower than current estimates suggest. Our empirical, large-scale, high-resolution infection risk estimates for S. mansoni and S. haematobium in eastern Africa can guide future control interventions and provide a benchmark for subsequent monitoring and evaluation activities. Copyright © 2011
Forward modeling of gravity data using geostatistically generated subsurface density variations
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.
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.
Gething, Peter W.; Noor, Abdisalan M.; Gikandi, Priscilla W.; Hay, Simon I.; Nixon, Mark S.; Snow, Robert W.; Atkinson, Peter M.
2009-01-01
Basic health system data such as the number of patients utilising different health facilities and the types of illness for which they are being treated are critical for managing service provision. These data requirements are generally addressed with some form of national Health Management Information System (HMIS) which coordinates the routine collection and compilation of data from national health facilities. HMIS in most developing countries are characterised by widespread under-reporting. Here we present a method to adjust incomplete data to allow prediction of national outpatient treatment burdens. We demonstrate this method with the example of outpatient treatments for malaria within the Kenyan HMIS. Three alternative modelling frameworks were developed and tested in which space-time geostatistical prediction algorithms were used to predict the monthly tally of treatments for presumed malaria cases (MC) at facilities where such records were missing. Models were compared by a cross-validation exercise and the model found to most accurately predict MC incorporated available data on the total number of patients visiting each facility each month. A space-time stochastic simulation framework to accompany this model was developed and tested in order to provide estimates of both local and regional prediction uncertainty. The level of accuracy provided by the predictive model, and the accompanying estimates of uncertainty around the predictions, demonstrate how this tool can mitigate the uncertainties caused by missing data, substantially enhancing the utility of existing HMIS data to health-service decision-makers. PMID:19325928
NASA Astrophysics Data System (ADS)
Dai, Heng; Chen, Xingyuan; Ye, Ming; Song, Xuehang; Zachara, John M.
2017-05-01
Sensitivity analysis is an important tool for development and improvement of mathematical models, especially for complex systems with a high dimension of spatially correlated parameters. Variance-based global sensitivity analysis has gained popularity because it can quantify the relative contribution of uncertainty from different sources. However, its computational cost increases dramatically with the complexity of the considered model and the dimension of model parameters. In this study, we developed a new sensitivity analysis method that integrates the concept of variance-based method with a hierarchical uncertainty quantification framework. Different uncertain inputs are grouped and organized into a multilayer framework based on their characteristics and dependency relationships to reduce the dimensionality of the sensitivity analysis. A set of new sensitivity indices are defined for the grouped inputs using the variance decomposition method. Using this methodology, we identified the most important uncertainty source for a dynamic groundwater flow and solute transport model at the Department of Energy (DOE) Hanford site. The results indicate that boundary conditions and permeability field contribute the most uncertainty to the simulated head field and tracer plume, respectively. The relative contribution from each source varied spatially and temporally. By using a geostatistical approach to reduce the number of realizations needed for the sensitivity analysis, the computational cost of implementing the developed method was reduced to a practically manageable level. The developed sensitivity analysis method is generally applicable to a wide range of hydrologic and environmental problems that deal with high-dimensional spatially distributed input variables.
Geostatistical Procedures for Developing Three-Dimensional Aquifer Models from Drillers' Logs
NASA Astrophysics Data System (ADS)
Bohling, G.; Helm, C.
2013-12-01
The Hydrostratigraphic Drilling Record Assessment (HyDRA) project is developing procedures for employing the vast but highly qualitative hydrostratigraphic information contained in drillers' logs in the development of quantitative three-dimensional (3D) depictions of subsurface properties for use in flow and transport models to support groundwater management practices. One of the project's objectives is to develop protocols for 3D interpolation of lithological data from drillers' logs, properly accounting for the categorical nature of these data. This poster describes the geostatistical procedures developed to accomplish this objective. Using a translation table currently containing over 62,000 unique sediment descriptions encountered during the transcription of over 15,000 logs in the Kansas High Plains aquifer, the sediment descriptions are translated into 71 standardized terms, which are then mapped into a small number of categories associated with different representative property (e.g., hydraulic conductivity [K]) values. Each log is partitioned into regular intervals and the proportion of each K category within each interval is computed. To properly account for their compositional nature, a logratio transform is applied to the proportions. The transformed values are then kriged to the 3D model grid and backtransformed to determine the proportion of each category within each model cell. Various summary measures can then be computed from the proportions, including a proportion-weighted average K and an entropy measure representing the degree of mixing of categories within each cell. We also describe a related cross-validation procedure for assessing log quality.
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.
Predictive risk mapping of schistosomiasis in Brazil using Bayesian geostatistical models.
Scholte, Ronaldo G C; Gosoniu, Laura; Malone, John B; Chammartin, Frédérique; Utzinger, Jürg; Vounatsou, Penelope
2014-04-01
Schistosomiasis is one of the most common parasitic diseases in tropical and subtropical areas, including Brazil. A national control programme was initiated in Brazil in the mid-1970s and proved successful in terms of morbidity control, as the number of cases with hepato-splenic involvement was reduced significantly. To consolidate control and move towards elimination, there is a need for reliable maps on the spatial distribution of schistosomiasis, so that interventions can target communities at highest risk. The purpose of this study was to map the distribution of Schistosoma mansoni in Brazil. We utilized readily available prevalence data from the national schistosomiasis control programme for the years 2005-2009, derived remotely sensed climatic and environmental data and obtained socioeconomic data from various sources. Data were collated into a geographical information system and Bayesian geostatistical models were developed. Model-based maps identified important risk factors related to the transmission of S. mansoni and confirmed that environmental variables are closely associated with indices of poverty. Our smoothed predictive risk map, including uncertainty, highlights priority areas for intervention, namely the northern parts of North and Southeast regions and the eastern part of Northeast region. Our predictive risk map provides a useful tool for to strengthen existing surveillance-response mechanisms. Copyright © 2014. Published by Elsevier B.V.
Warnery, E; Ielsch, G; Lajaunie, C; Cale, E; Wackernagel, H; Debayle, C; Guillevic, J
2015-01-01
information, which is exhaustive throughout France, could help in estimating the telluric gamma dose rates. Such an approach is possible using multivariate geostatistics and cokriging. Multi-collocated cokriging has been performed on 1*1 km(2) cells over the domain. This model used gamma dose rate measurement results and GUP classes. Our results provide useful information on the variability of the natural terrestrial gamma radiation in France ('natural background') and exposure data for epidemiological studies and risk assessment from low dose chronic exposures. Copyright © 2014 Elsevier Ltd. All rights reserved.
NASA Astrophysics Data System (ADS)
Vasquez, D. A.; Swift, J. N.; Tan, S.; Darrah, T. H.
2013-12-01
The integration of precise geochemical analyses with quantitative engineering modeling into an interactive GIS system allows for a sophisticated and efficient method of reservoir engineering and characterization. Geographic Information Systems (GIS) is utilized as an advanced technique for oil field reservoir analysis by combining field engineering and geological/geochemical spatial datasets with the available systematic modeling and mapping methods to integrate the information into a spatially correlated first-hand approach in defining surface and subsurface characteristics. Three key methods of analysis include: 1) Geostatistical modeling to create a static and volumetric 3-dimensional representation of the geological body, 2) Numerical modeling to develop a dynamic and interactive 2-dimensional model of fluid flow across the reservoir and 3) Noble gas geochemistry to further define the physical conditions, components and history of the geologic system. Results thus far include using engineering algorithms for interpolating electrical well log properties across the field (spontaneous potential, resistivity) yielding a highly accurate and high-resolution 3D model of rock properties. Results so far also include using numerical finite difference methods (crank-nicholson) to solve for equations describing the distribution of pressure across field yielding a 2D simulation model of fluid flow across reservoir. Ongoing noble gas geochemistry results will also include determination of the source, thermal maturity and the extent/style of fluid migration (connectivity, continuity and directionality). Future work will include developing an inverse engineering algorithm to model for permeability, porosity and water saturation.This combination of new and efficient technological and analytical capabilities is geared to provide a better understanding of the field geology and hydrocarbon dynamics system with applications to determine the presence of hydrocarbon pay zones (or
Sampling design optimisation for rainfall prediction using a non-stationary geostatistical model
NASA Astrophysics Data System (ADS)
Wadoux, Alexandre M. J.-C.; Brus, Dick J.; Rico-Ramirez, Miguel A.; Heuvelink, Gerard B. M.
2017-09-01
The accuracy of spatial predictions of rainfall by merging rain-gauge and radar data is partly determined by the sampling design of the rain-gauge network. Optimising the locations of the rain-gauges may increase the accuracy of the predictions. Existing spatial sampling design optimisation methods are based on minimisation of the spatially averaged prediction error variance under the assumption of intrinsic stationarity. Over the past years, substantial progress has been made to deal with non-stationary spatial processes in kriging. Various well-documented geostatistical models relax the assumption of stationarity in the mean, while recent studies show the importance of considering non-stationarity in the variance for environmental processes occurring in complex landscapes. We optimised the sampling locations of rain-gauges using an extension of the Kriging with External Drift (KED) model for prediction of rainfall fields. The model incorporates both non-stationarity in the mean and in the variance, which are modelled as functions of external covariates such as radar imagery, distance to radar station and radar beam blockage. Spatial predictions are made repeatedly over time, each time recalibrating the model. The space-time averaged KED variance was minimised by Spatial Simulated Annealing (SSA). The methodology was tested using a case study predicting daily rainfall in the north of England for a one-year period. Results show that (i) the proposed non-stationary variance model outperforms the stationary variance model, and (ii) a small but significant decrease of the rainfall prediction error variance is obtained with the optimised rain-gauge network. In particular, it pays off to place rain-gauges at locations where the radar imagery is inaccurate, while keeping the distribution over the study area sufficiently uniform.
The applications of model-based geostatistics in helminth epidemiology and control.
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.
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
NASA Astrophysics Data System (ADS)
Trainor, W. J.; Knight, R. J.; Caers, J. K.
2007-12-01
In order to effectively manage groundwater resources, water agencies have begun to incorporate precipitation, temperature, stream-gauge, land-cover and groundwater level data into their aquifer models. For Western States in particular, stored groundwater is an important provider for agriculture and human consumption. But the estimates of groundwater quantity are arguably the most uncertain in the water balance equation. Current practice in constructing subsurface models relies on substandard and incomplete data due due, in large part, to budgetary constraints. Once a final model has been developed, the possible inaccuracies in the geological scenarios are rarely examined or investigated. How wrong can the subsurface model be while still giving accurate prediction results? How sensitive is the model response to perturbations in the subsurface parameters and long-term irrigation, precipitation and recharge conditions? This study examines these questions through a sensitivity analysis. The "working" aquifers of California's agricultural central coast were used as analog systems for the construction of this sensitivity study. The fluvial geologic interpretations of these coastal aquifer systems were used in Boolean (object-oriented) and multiple-point geostatistical algorithms to create many alternative permeability fields, reflecting the uncertainty in the spatial distribution and geological scenario of the subsurface permeability field. Two sets of models were created using SNESIM, a multiple-point geostatistical algorithm. SNESIM is able to generate a stochastic facies realization using a training image (TI -- a conceptual idea of geologic system) with rotation and affinity maps. The first set of models are higher entropy, representing less continuous clay layers. These were created from a TI of clay ellipses (which was created using GSLIB Ellipsim program). The second set of models are more heterogeneous by using a fluvial TI within SNESIM. All the realizations
Sedda, Luigi; Mweempwa, Cornelius; Ducheyne, Els; De Pus, Claudia; Hendrickx, Guy; Rogers, David J.
2014-01-01
For the first time a Bayesian geostatistical version of the Moran Curve, a logarithmic form of the Ricker stock recruitment curve, is proposed that is able to give an estimate of net change in population demographic rates considering components such as fertility and density dependent and density independent mortalities. The method is applied to spatio-temporally referenced count data of tsetse flies obtained from fly-rounds. The model is a linear regression with three components: population rate of change estimated from the Moran curve, an explicit spatio-temporal covariance, and the observation error optimised within a Bayesian framework. The model was applied to the three main climate seasons of Zambia (rainy – January to April, cold-dry – May to August, and hot-dry – September to December) taking into account land surface temperature and (seasonally changing) cattle distribution. The model shows a maximum positive net change during the hot-dry season and a minimum between the rainy and cold-dry seasons. Density independent losses are correlated positively with day-time land surface temperature and negatively with night-time land surface temperature and cattle distribution. The inclusion of density dependent mortality increases considerably the goodness of fit of the model. Cross validation with an independent dataset taken from the same area resulted in a very accurate estimate of tsetse catches. In general, the overall framework provides an important tool for vector control and eradication by identifying vector population concentrations and local vector demographic rates. It can also be applied to the case of sustainable harvesting of natural populations. PMID:24755848
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.
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.
Hubbard, W. B.; Militzer, B.
2016-03-20
In anticipation of new observational results for Jupiter's axial moment of inertia and gravitational zonal harmonic coefficients from the forthcoming Juno orbiter, we present a number of preliminary Jupiter interior models. We combine results from ab initio computer simulations of hydrogen–helium mixtures, including immiscibility calculations, with a new nonperturbative calculation of Jupiter's zonal harmonic coefficients, to derive a self-consistent model for the planet's external gravity and moment of inertia. We assume helium rain modified the interior temperature and composition profiles. Our calculation predicts zonal harmonic values to which measurements can be compared. Although some models fit the observed (pre-Juno) second- and fourth-order zonal harmonics to within their error bars, our preferred reference model predicts a fourth-order zonal harmonic whose absolute value lies above the pre-Juno error bars. This model has a dense core of about 12 Earth masses and a hydrogen–helium-rich envelope with approximately three times solar metallicity.
NASA Astrophysics Data System (ADS)
Cecinati, Francesca; Rico-Ramirez, Miguel Angel; Heuvelink, Gerard B. M.; Han, Dawei
2017-05-01
The application of radar quantitative precipitation estimation (QPE) to hydrology and water quality models can be preferred to interpolated rainfall point measurements because of the wide coverage that radars can provide, together with a good spatio-temporal resolutions. Nonetheless, it is often limited by the proneness of radar QPE to a multitude of errors. Although radar errors have been widely studied and techniques have been developed to correct most of them, residual errors are still intrinsic in radar QPE. An estimation of uncertainty of radar QPE and an assessment of uncertainty propagation in modelling applications is important to quantify the relative importance of the uncertainty associated to radar rainfall input in the overall modelling uncertainty. A suitable tool for this purpose is the generation of radar rainfall ensembles. An ensemble is the representation of the rainfall field and its uncertainty through a collection of possible alternative rainfall fields, produced according to the observed errors, their spatial characteristics, and their probability distribution. The errors are derived from a comparison between radar QPE and ground point measurements. The novelty of the proposed ensemble generator is that it is based on a geostatistical approach that assures a fast and robust generation of synthetic error fields, based on the time-variant characteristics of errors. The method is developed to meet the requirement of operational applications to large datasets. The method is applied to a case study in Northern England, using the UK Met Office NIMROD radar composites at 1 km resolution and at 1 h accumulation on an area of 180 km by 180 km. The errors are estimated using a network of 199 tipping bucket rain gauges from the Environment Agency. 183 of the rain gauges are used for the error modelling, while 16 are kept apart for validation. The validation is done by comparing the radar rainfall ensemble with the values recorded by the validation rain
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
Viswanathan, Hari S; Robinson, Bruce A; Gable, Carl W; Carey, James W
2003-01-01
Retardation of certain radionuclides due to sorption to zeolitic minerals is considered one of the major barriers to contaminant transport in the unsaturated zone of Yucca Mountain. However, zeolitically altered areas are lower in permeability than unaltered regions, which raises the possibility that contaminants might bypass the sorptive zeolites. The relationship between hydrologic and chemical properties must be understood to predict the transport of radionuclides through zeolitically altered areas. In this study, we incorporate mineralogical information into an unsaturated zone transport model using geostatistical techniques to correlate zeolitic abundance to hydrologic and chemical properties. Geostatistical methods are used to develop variograms, kriging maps, and conditional simulations of zeolitic abundance. We then investigate, using flow and transport modeling on a heterogeneous field, the relationship between percent zeolitic alteration, permeability changes due to alteration, sorption due to alteration, and their overall effect on radionuclide transport. We compare these geostatistical simulations to a simplified threshold method in which each spatial location in the model is assigned either zeolitic or vitric properties based on the zeolitic abundance at that location. A key conclusion is that retardation due to sorption predicted by using the continuous distribution is larger than the retardation predicted by the threshold method. The reason for larger retardation when using the continuous distribution is a small but significant sorption at locations with low zeolitic abundance. If, for practical reasons, models with homogeneous properties within each layer are used, we recommend setting nonzero K(d)s in the vitric tuffs to mimic the more rigorous continuous distribution simulations. Regions with high zeolitic abundance may not be as effective in retarding radionuclides such as Neptunium since these rocks are lower in permeability and contaminants can
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.
Geostatistical simulations for radon indoor with a nested model including the housing factor.
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.
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.
Geostatistics and petroleum geology
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.
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
A Practical Primer on Geostatistics
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
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.
Preliminary reference Earth model
NASA Astrophysics Data System (ADS)
Dziewonski, Adam M.; Anderson, Don L.
1981-06-01
A large data set consisting of about 1000 normal mode periods, 500 summary travel time observations, 100 normal mode Q values, mass and moment of inertia have been inverted to obtain the radial distribution of elastic properties, Q values and density in the Earth's interior. The data set was supplemented with a special study of 12 years of ISC phase data which yielded an additional 1.75 × 10 6 travel time observations for P and S waves. In order to obtain satisfactory agreement with the entire data set we were required to take into account anelastic dispersion. The introduction of transverse isotropy into the outer 220 km of the mantle was required in order to satisfy the shorter period fundamental toroidal and spheroidal modes. This anisotropy also improved the fit of the larger data set. The horizontal and vertical velocities in the upper mantle differ by 2-4%, both for P and S waves. The mantle below 220 km is not required to be anisotropic. Mantle Rayleigh waves are surprisingly sensitive to compressional velocity in the upper mantle. High S n velocities, low P n velocities and a pronounced low-velocity zone are features of most global inversion models that are suppressed when anisotropy is allowed for in the inversion. The Preliminary Reference Earth Model, PREM, and auxiliary tables showing fits to the data are presented.
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
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
Implementation of the Iterative Proportion Fitting Algorithm for Geostatistical Facies Modeling
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.
Application of geostatistics to risk assessment.
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.
Reservoir studies with geostatistics to forecast performance
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.
Geostatistics and petroleum geology
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.
A practical primer on geostatistics
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
NASA Astrophysics Data System (ADS)
Chahal, M. K.; Brown, D. J.; Brooks, E. S.; Campbell, C.; Cobos, D. R.; Vierling, L. A.
2012-12-01
Estimating soil moisture content continuously over space and time using geo-statistical techniques supports the refinement of process-based watershed hydrology models and the application of soil process models (e.g. biogeochemical models predicting greenhouse gas fluxes) to complex landscapes. In this study, we model soil profile volumetric moisture content for five agricultural fields with loess soils in the Palouse region of Eastern Washington and Northern Idaho. Using a combination of stratification and space-filling techniques, we selected 42 representative and distributed measurement locations in the Cook Agronomy Farm (Pullman, WA) and 12 locations each in four additional grower fields that span the precipitation gradient across the Palouse. At each measurement location, soil moisture was measured on an hourly basis at five different depths (30, 60, 90, 120, and 150 cm) using Decagon 5-TE/5-TM soil moisture sensors (Decagon Devices, Pullman, WA, USA). This data was collected over three years for the Cook Agronomy Farm and one year for each of the grower fields. In addition to ordinary kriging, we explored the correlation of volumetric water content with external, spatially exhaustive indices derived from terrain models, optical remote sensing imagery, and proximal soil sensing data (electromagnetic induction and VisNIR penetrometer)
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
Todd, M. Jason; Lowrance, R. Richard; Goovaerts, Pierre; Vellidis, George; Pringle, Catherine M.
2010-01-01
Blackwater streams are found throughout the Coastal Plain of the southeastern United States and are characterized by a series of instream floodplain swamps that play a critical role in determining the water quality of these systems. Within the state of Georgia, many of these streams are listed in violation of the state’s dissolved oxygen (DO) standard. Previous work has shown that sediment oxygen demand (SOD) is elevated in instream floodplain swamps and due to these areas of intense oxygen demand, these locations play a major role in determining the oxygen balance of the watershed as a whole. This work also showed SOD rates to be positively correlated with the concentration of total organic carbon. This study builds on previous work by using geostatistics and Sequential Gaussian Simulation to investigate the patchiness and distribution of total organic carbon (TOC) at the reach scale. This was achieved by interpolating TOC observations and simulated SOD rates based on a linear regression. Additionally, this study identifies areas within the stream system prone to high SOD at representative 3rd and 5th order locations. Results show that SOD was spatially correlated with the differences in distribution of TOC at both locations and that these differences in distribution are likely a result of the differing hydrologic regime and watershed position. Mapping of floodplain soils at the watershed scale shows that areas of organic sediment are widespread and become more prevalent in higher order streams. DO dynamics within blackwater systems are a complicated mix of natural and anthropogenic influences, but this paper illustrates the importance of instream swamps in enhancing SOD at the watershed scale. Moreover, our study illustrates the influence of instream swamps on oxygen demand while providing support that many of these systems are naturally low in DO. PMID:20938491
Todd, M Jason; Lowrance, R Richard; Goovaerts, Pierre; Vellidis, George; Pringle, Catherine M
2010-10-15
Blackwater streams are found throughout the Coastal Plain of the southeastern United States and are characterized by a series of instream floodplain swamps that play a critical role in determining the water quality of these systems. Within the state of Georgia, many of these streams are listed in violation of the state's dissolved oxygen (DO) standard. Previous work has shown that sediment oxygen demand (SOD) is elevated in instream floodplain swamps and due to these areas of intense oxygen demand, these locations play a major role in determining the oxygen balance of the watershed as a whole. This work also showed SOD rates to be positively correlated with the concentration of total organic carbon. This study builds on previous work by using geostatistics and Sequential Gaussian Simulation to investigate the patchiness and distribution of total organic carbon (TOC) at the reach scale. This was achieved by interpolating TOC observations and simulated SOD rates based on a linear regression. Additionally, this study identifies areas within the stream system prone to high SOD at representative 3rd and 5th order locations. Results show that SOD was spatially correlated with the differences in distribution of TOC at both locations and that these differences in distribution are likely a result of the differing hydrologic regime and watershed position. Mapping of floodplain soils at the watershed scale shows that areas of organic sediment are widespread and become more prevalent in higher order streams. DO dynamics within blackwater systems are a complicated mix of natural and anthropogenic influences, but this paper illustrates the importance of instream swamps in enhancing SOD at the watershed scale. Moreover, our study illustrates the influence of instream swamps on oxygen demand while providing support that many of these systems are naturally low in DO.
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).
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
Wang, F.P.; Dai, J.; Kerans, C.
1998-11-01
In part 1 of this paper, the authors discussed the rock-fabric/petrophysical classes for dolomitized carbonate-ramp rocks, the effects of rock fabric and pore type on petrophysical properties, petrophysical models for analyzing wireline logs, the critical scales for defining geologic framework, and 3-D geologic modeling. Part 2 focuses on geophysical and engineering characterizations, including seismic modeling, reservoir geostatistics, stochastic modeling, and reservoir simulation. Synthetic seismograms of 30 to 200 Hz were generated to study the level of seismic resolution required to capture the high-frequency geologic features in dolomitized carbonate-ramp reservoirs. Outcrop data were collected to investigate effects of sampling interval and scale-up of block size on geostatistical parameters. Semivariogram analysis of outcrop data showed that the sill of log permeability decreases and the correlation length increases with an increase of horizontal block size. Permeability models were generated using conventional linear interpolation, stochastic realizations without stratigraphic constraints, and stochastic realizations with stratigraphic constraints. Simulations of a fine-scale Lawyer Canyon outcrop model were used to study the factors affecting waterflooding performance. Simulation results show that waterflooding performance depends strongly on the geometry and stacking pattern of the rock-fabric units and on the location of production and injection wells.
Kara G. Eby
2010-08-01
At the Idaho National Laboratory (INL) Cs-137 concentrations above the U.S. Environmental Protection Agency risk-based threshold of 0.23 pCi/g may increase the risk of human mortality due to cancer. As a leader in nuclear research, the INL has been conducting nuclear activities for decades. Elevated anthropogenic radionuclide levels including Cs-137 are a result of atmospheric weapons testing, the Chernobyl accident, and nuclear activities occurring at the INL site. Therefore environmental monitoring and long-term surveillance of Cs-137 is required to evaluate risk. However, due to the large land area involved, frequent and comprehensive monitoring is limited. Developing a spatial model that predicts Cs-137 concentrations at unsampled locations will enhance the spatial characterization of Cs-137 in surface soils, provide guidance for an efficient monitoring program, and pinpoint areas requiring mitigation strategies. The predictive model presented herein is based on applied geostatistics using a Bayesian analysis of environmental characteristics across the INL site, which provides kriging spatial maps of both Cs-137 estimates and prediction errors. Comparisons are presented of two different kriging methods, showing that the use of secondary information (i.e., environmental characteristics) can provide improved prediction performance in some areas of the INL site.
Niazi, Nabeel K; Bishop, Thomas F A; Singh, Balwant
2011-12-15
This study investigated the spatial variability of total and phosphate-extractable arsenic (As) concentrations in soil adjacent to a cattle-dip site, employing a linear mixed model-based geostatistical approach. The soil samples in the study area (n = 102 in 8.1 m(2)) were taken at the nodes of a 0.30 × 0.35 m grid. The results showed that total As concentration (0-0.2 m depth) and phosphate-extractable As concentration (at depths of 0-0.2, 0.2-0.4, and 0.4-0.6 m) in soil adjacent to the dip varied greatly. Both total and phosphate-extractable soil As concentrations significantly (p = 0.004-0.048) increased toward the cattle-dip. Using the linear mixed model, we suggest that 5 samples are sufficient to assess a dip site for soil (As) contamination (95% confidence interval of ±475.9 mg kg(-1)), but 15 samples (95% confidence interval of ±212.3 mg kg(-1)) is desirable baseline when the ultimate goal is to evaluate the effects of phytoremediation. Such guidelines on sampling requirements are crucial for the assessment of As contamination levels at other cattle-dip sites, and to determine the effect of phytoremediation on soil As.
Hedge, L H; Dafforn, K A; Simpson, S L; Johnston, E L
2017-06-30
Infrastructure associated with coastal communities is likely to not only directly displace natural systems, but also leave environmental footprints' that stretch over multiple scales. Some coastal infrastructure will, there- fore, generate a hidden layer of habitat heterogeneity in sediment systems that is not immediately observable in classical impact assessment frameworks. We examine the hidden heterogeneity associated with one of the most ubiquitous coastal modifications; dense swing moorings fields. Using a model based geo-statistical framework we highlight the variation in sedimentology throughout mooring fields and reference locations. Moorings were correlated with patches of sediment with larger particle sizes, and associated metal(loid) concentrations in these patches were depressed. Our work highlights two important ideas i) mooring fields create a mosaic of habitat in which contamination decreases and grain sizes increase close to moorings, and ii) model- based frameworks provide an information rich, easy-to-interpret way to communicate complex analyses to stakeholders. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.
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.
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
Highfield, Linda; Ward, Michael P; Laffan, Shawn W
2008-01-01
Modeling potential disease spread in wildlife populations is important for predicting, responding to and recovering from a foreign animal disease incursion. To make spatial epidemic predictions, the target animal species of interest must first be represented in space. We conducted a series of simulation experiments to determine how estimates of the spatial distribution of white-tailed deer impact the predicted magnitude and distribution of foot-and-mouth disease (FMD) outbreaks. Outbreaks were simulated using a susceptible-infected-recovered geographic automata model. The study region was a 9-county area (24 000 km(2)) of southern Texas. Methods used for creating deer distributions included dasymetric mapping, kriging and remotely sensed image analysis. The magnitudes and distributions of the predicted outbreaks were evaluated by comparing the median number of deer infected and median area affected (km(2)), respectively. The methods were further evaluated for similar predictive power by comparing the model predicted outputs with unweighted pair group method with arithmetic mean (UPGMA) clustering. There were significant differences in the estimated number of deer in the study region, based on the geostatistical estimation procedure used (range: 385 939-768 493). There were also substantial differences in the predicted magnitude of the FMD outbreaks (range: 1 563-8 896) and land area affected (range: 56-447 km(2)) for the different estimated animal distributions. UPGMA clustering indicated there were two main groups of distributions, and one outlier. We recommend that one distribution from each of these two groups be used to model the range of possible outbreaks. Methods included in cluster 1 (such as county-level disaggregation) could be used in conjunction with any of the methods in cluster 2, which included kriging, NDVI split by ecoregion, or disaggregation at the regional level, to represent the variability in the model predicted outbreak distributions. How
NASA Astrophysics Data System (ADS)
Koike, Katsuaki; Kubo, Taiki; Liu, Chunxue; Masoud, Alaa; Amano, Kenji; Kurihara, Arata; Matsuoka, Toshiyuki; Lanyon, Bill
2015-10-01
This study integrates 3D models of rock fractures from different sources and hydraulic properties aimed at identifying relationships between fractures and permeability. The Tono area in central Japan, chiefly overlain by Cretaceous granite, was examined because of the availability of a unique dataset from deep borehole data at 26 sites. A geostatistical method (GEOFRAC) that can incorporate orientations of sampled data was applied to 50,900 borehole fractures for spatial modeling of fractures over a 12 km by 8 km area, to a depth of 1.5 km. GEOFRAC produced a plausible 3D fracture model, in that the orientations of simulated fractures correspond to those of the sample data and the continuous fractures appeared near a known fault. Small-scale fracture distributions with dominant orientations were also characterized around the two shafts using fracture data from the shaft walls. By integrating the 3D model of hydraulic conductivity using sequential Gaussian simulation with the GEOFRAC fractures from the borehole data, the fracture sizes and directions that strongly affect permeable features were identified. Four fracture-related elements: lineaments from a shaded 10-m DEM, GEOFRAC fractures using the borehole and shaft data, and microcracks from SEM images, were used for correlating fracture attributes at different scales. The consistency of the semivariogram models of distribution densities was identified. Using an experimental relationship between hydraulic conductivity and fracture length, the fractures that typically affect the hydraulic properties at the drift scale were surmised to be in the range 100-200 m. These results are useful for a comprehensive understanding of rock fracture systems and their hydraulic characteristics at multiple scales in a target area.
Preliminary ECLSS waste water model
NASA Technical Reports Server (NTRS)
Carter, Donald L.; Holder, Donald W., Jr.; Alexander, Kevin; Shaw, R. G.; Hayase, John K.
1991-01-01
A preliminary waste water model for input to the Space Station Freedom (SSF) Environmental Control and Life Support System (ECLSS) Water Processor (WP) has been generated for design purposes. Data have been compiled from various ECLSS tests and flight sample analyses. A discussion of the characterization of the waste streams comprising the model is presented, along with a discussion of the waste water model and the rationale for the inclusion of contaminants in their respective concentrations. The major objective is to establish a methodology for the development of a waste water model and to present the current state of that model.
Preliminary ECLSS waste water model
NASA Technical Reports Server (NTRS)
Carter, Donald L.; Holder, Donald W., Jr.; Alexander, Kevin; Shaw, R. G.; Hayase, John K.
1991-01-01
A preliminary waste water model for input to the Space Station Freedom (SSF) Environmental Control and Life Support System (ECLSS) Water Processor (WP) has been generated for design purposes. Data have been compiled from various ECLSS tests and flight sample analyses. A discussion of the characterization of the waste streams comprising the model is presented, along with a discussion of the waste water model and the rationale for the inclusion of contaminants in their respective concentrations. The major objective is to establish a methodology for the development of a waste water model and to present the current state of that model.
Scholte, Ronaldo G C; Schur, Nadine; Bavia, Maria E; Carvalho, Edgar M; Chammartin, Frédérique; Utzinger, Jürg; Vounatsou, Penelope
2013-11-01
Soil-transmitted helminths (Ascaris lumbricoides, Trichuris trichiura and hookworm) negatively impact the health and wellbeing of hundreds of millions of people, particularly in tropical and subtropical countries, including Brazil. Reliable maps of the spatial distribution and estimates of the number of infected people are required for the control and eventual elimination of soil-transmitted helminthiasis. We used advanced Bayesian geostatistical modelling, coupled with geographical information systems and remote sensing to visualize the distribution of the three soil-transmitted helminth species in Brazil. Remotely sensed climatic and environmental data, along with socioeconomic variables from readily available databases were employed as predictors. Our models provided mean prevalence estimates for A. lumbricoides, T. trichiura and hookworm of 15.6%, 10.1% and 2.5%, respectively. By considering infection risk and population numbers at the unit of the municipality, we estimate that 29.7 million Brazilians are infected with A. lumbricoides, 19.2 million with T. trichiura and 4.7 million with hookworm. Our model-based maps identified important risk factors related to the transmission of soiltransmitted helminths and confirm that environmental variables are closely associated with indices of poverty. Our smoothed risk maps, including uncertainty, highlight areas where soil-transmitted helminthiasis control interventions are most urgently required, namely in the North and along most of the coastal areas of Brazil. We believe that our predictive risk maps are useful for disease control managers for prioritising control interventions and for providing a tool for more efficient surveillance-response mechanisms.
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
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
Piel, Frédéric B; Patil, Anand P; Howes, Rosalind E; Nyangiri, Oscar A; Gething, Peter W; Dewi, Mewahyu; Temperley, William H; Williams, Thomas N; Weatherall, David J; Hay, Simon I
2013-01-01
Summary Background Reliable estimates of populations affected by diseases are necessary to guide efficient allocation of public health resources. Sickle haemoglobin (HbS) is the most common and clinically significant haemoglobin structural variant, but no contemporary estimates exist of the global populations affected. Moreover, the precision of available national estimates of heterozygous (AS) and homozygous (SS) neonates is unknown. We aimed to provide evidence-based estimates at various scales, with uncertainty measures. Methods Using a database of sickle haemoglobin surveys, we created a contemporary global map of HbS allele frequency distribution within a Bayesian geostatistical model. The pairing of this map with demographic data enabled calculation of global, regional, and national estimates of the annual number of AS and SS neonates. Subnational estimates were also calculated in data-rich areas. Findings Our map shows subnational spatial heterogeneities and high allele frequencies across most of sub-Saharan Africa, the Middle East, and India, as well as gene flow following migrations to western Europe and the eastern coast of the Americas. Accounting for local heterogeneities and demographic factors, we estimated that the global number of neonates affected by HbS in 2010 included 5 476 000 (IQR 5 291 000–5 679 000) AS neonates and 312 000 (294 000–330 000) SS neonates. These global estimates are higher than previous conservative estimates. Important differences predicted at the national level are discussed. Interpretation HbS will have an increasing effect on public health systems. Our estimates can help countries and the international community gauge the need for appropriate diagnoses and genetic counselling to reduce the number of neonates affected. Similar mapping and modelling methods could be used for other inherited disorders. Funding The Wellcome Trust. PMID:23103089
Piel, Frédéric B; Patil, Anand P; Howes, Rosalind E; Nyangiri, Oscar A; Gething, Peter W; Dewi, Mewahyu; Temperley, William H; Williams, Thomas N; Weatherall, David J; Hay, Simon I
2013-01-12
Reliable estimates of populations affected by diseases are necessary to guide efficient allocation of public health resources. Sickle haemoglobin (HbS) is the most common and clinically significant haemoglobin structural variant, but no contemporary estimates exist of the global populations affected. Moreover, the precision of available national estimates of heterozygous (AS) and homozygous (SS) neonates is unknown. We aimed to provide evidence-based estimates at various scales, with uncertainty measures. Using a database of sickle haemoglobin surveys, we created a contemporary global map of HbS allele frequency distribution within a Bayesian geostatistical model. The pairing of this map with demographic data enabled calculation of global, regional, and national estimates of the annual number of AS and SS neonates. Subnational estimates were also calculated in data-rich areas. Our map shows subnational spatial heterogeneities and high allele frequencies across most of sub-Saharan Africa, the Middle East, and India, as well as gene flow following migrations to western Europe and the eastern coast of the Americas. Accounting for local heterogeneities and demographic factors, we estimated that the global number of neonates affected by HbS in 2010 included 5,476,000 (IQR 5,291,000-5,679,000) AS neonates and 312,000 (294,000-330,000) SS neonates. These global estimates are higher than previous conservative estimates. Important differences predicted at the national level are discussed. HbS will have an increasing effect on public health systems. Our estimates can help countries and the international community gauge the need for appropriate diagnoses and genetic counselling to reduce the number of neonates affected. Similar mapping and modelling methods could be used for other inherited disorders. The Wellcome Trust. Copyright © 2013 Elsevier Ltd. All rights reserved.
New GNSS velocity field and preliminary velocity model for Ecuador
NASA Astrophysics Data System (ADS)
Luna-Ludeña, Marco P.; Staller, Alejandra; Gaspar-Escribano, Jorge M.; Belén Benito, M.
2016-04-01
In this work, we present a new preliminary velocity model of Ecuador based on the GNSS data of the REGME network (continuous monitoring GNSS network). To date, there is no velocity model available for the country. The only existing model in the zone is the regional model VEMOS2009 for South America and Caribbean (Drewes and Heidbach, 2012). This model was developed from the SIRGAS station positions, the velocities of the SIRGAS-CON stations, and several geodynamics projects performed in the region. Just two continuous GNSS (cGNSS) stations of Ecuador were taking into account in the VEMOS2009 model. The first continuous station of the REGME network was established in 2008. At present, it is composed by 32 continuous GNSS stations, covering the country. All the stations provided data during at least two years. We processed the data of the 32 GNSS stations of REGME for the 2008-2014 period, as well as 20 IGS stations in order to link to the global reference frame IGb08 (ITRF2008). GPS data were processed using Bernese 5.0 software (Dach et al., 2007). We obtained and analyzed the GNSS coordinate time series of the 32 REGME stations and we calculated the GPS-derived horizontal velocity field of the country. Velocities in ITRF2008 were transformed into a South American fixed reference frame, using the Euler pole calculated from 8 cGNSS stations throughout this plate. Our velocity field is consistent with the tectonics of the country and contributes to a better understanding of it. From the horizontal velocity field, we determined a preliminary model using the kriging geostatistical technique. To check the results we use the cross-validation method. The differences between the observed and estimated values range from ± 5 mm. This is a new velocity model obtained from GNSS data for Ecuador.
Mapping malaria risk among children in Côte d'Ivoire using Bayesian geo-statistical models.
Raso, Giovanna; Schur, Nadine; Utzinger, Jürg; Koudou, Benjamin G; Tchicaya, Emile S; Rohner, Fabian; N'goran, Eliézer K; Silué, Kigbafori D; Matthys, Barbara; Assi, Serge; Tanner, Marcel; Vounatsou, Penelope
2012-05-09
In Côte d'Ivoire, an estimated 767,000 disability-adjusted life years are due to malaria, placing the country at position number 14 with regard to the global burden of malaria. Risk maps are important to guide control interventions, and hence, the aim of this study was to predict the geographical distribution of malaria infection risk in children aged <16 years in Côte d'Ivoire at high spatial resolution. Using different data sources, a systematic review was carried out to compile and geo-reference survey data on Plasmodium spp. infection prevalence in Côte d'Ivoire, focusing on children aged <16 years. The period from 1988 to 2007 was covered. A suite of Bayesian geo-statistical logistic regression models was fitted to analyse malaria risk. Non-spatial models with and without exchangeable random effect parameters were compared to stationary and non-stationary spatial models. Non-stationarity was modelled assuming that the underlying spatial process is a mixture of separate stationary processes in each ecological zone. The best fitting model based on the deviance information criterion was used to predict Plasmodium spp. infection risk for entire Côte d'Ivoire, including uncertainty. Overall, 235 data points at 170 unique survey locations with malaria prevalence data for individuals aged <16 years were extracted. Most data points (n = 182, 77.4%) were collected between 2000 and 2007. A Bayesian non-stationary regression model showed the best fit with annualized rainfall and maximum land surface temperature identified as significant environmental covariates. This model was used to predict malaria infection risk at non-sampled locations. High-risk areas were mainly found in the north-central and western area, while relatively low-risk areas were located in the north at the country border, in the north-east, in the south-east around Abidjan, and in the central-west between two high prevalence areas. The malaria risk map at high spatial resolution gives an
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.
NASA Astrophysics Data System (ADS)
Jensen, K. H.; He, X.; Sonnenborg, T. O.; Jørgensen, F.
2016-12-01
Multiple-point geostatistical simulation (MPS) of the geological structure has become popular in recent years in groundwater modeling. The method derives multi-point based structural information from a training image (TI) and as such is superior to the traditional two-point based geostatistical approach. Its application in 3D simulations has been constrained by the difficulty of constructing 3D TI. High resolution 3D electromagnetic data can be used for defining a TI but the data can also be used as secondary data for soft conditioning. An alternative approach for derived a TI is to use the object-based unconditional simulation program TiGenerator. In this study we present different MPS simulations of the geological structure for a site in Denmark based on different scenarios regarding TI and soft conditioning. The generated geostatistical realizations are used for developing groundwater models based on MODFLOW and each of these models is calibrated against hydraulic head measurements using the inversion code PEST. Based on the calibrated flow models the particle tracking code MODPATH is used to simulate probabilistic capture zones for abstraction wells. By comparing simulations of groundwater flow and probabilistic capture zone, comparable results are obtained based on TI directly derived from high resolution geophysical data and generated by theTiGenerator even for the probabilistic capture zones, which are highly sensitive to the geological structure. The study further suggests that soft conditioning in MPS is an effective way of integrating secondary data such as 3D airborne electromagnetic data (SkyTEM) leading to improved estimations of the geological structure as evidenced by the resulting hydraulic parameter values. However, care should be taken when the same data source is used for defining the TI and for soft conditioning as this may lead reduction in the uncertainty estimation.
GY SAMPLING THEORY AND GEOSTATISTICS: ALTERNATE MODELS OF VARIABILITY IN CONTINUOUS MEDIA
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...
GY SAMPLING THEORY AND GEOSTATISTICS: ALTERNATE MODELS OF VARIABILITY IN CONTINUOUS MEDIA
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...
Mapping soil organic carbon stocks by robust geostatistical and boosted regression models
NASA Astrophysics Data System (ADS)
Nussbaum, Madlene; Papritz, Andreas; Baltensweiler, Andri; Walthert, Lorenz
2013-04-01
Carbon (C) sequestration in forests offsets greenhouse gas emissions. Therefore, quantifying C stocks and fluxes in forest ecosystems is of interest for greenhouse gas reporting according to the Kyoto protocol. In Switzerland, the National Forest Inventory offers comprehensive data to quantify the aboveground forest biomass and its change in time. Estimating stocks of soil organic C (SOC) in forests is more difficult because the variables needed to quantify stocks vary strongly in space and precise quantification of some of them is very costly. Based on data from 1'033 plots we modeled SOC stocks of the organic layer and the mineral soil to depths of 30 cm and 100 cm for the Swiss forested area. For the statistical modeling a broad range of covariates were available: Climate data (e. g. precipitation, temperature), two elevation models (resolutions 25 and 2 m) with respective terrain attributes and spectral reflectance data representing vegetation. Furthermore, the main mapping units of an overview soil map and a coarse scale geological map were used to coarsely represent the parent material of the soils. The selection of important covariates for SOC stocks modeling out of a large set was a major challenge for the statistical modeling. We used two approaches to deal with this problem: 1) A robust restricted maximum likelihood method to fit linear regression model with spatially correlated errors. The large number of covariates was first reduced by LASSO (Least Absolute Shrinkage and Selection Operator) and then further narrowed down to a parsimonious set of important covariates by cross-validation of the robustly fitted model. To account for nonlinear dependencies of the response on the covariates interaction terms of the latter were included in model if this improved the fit. 2) A boosted structured regression model with componentwise linear least squares or componentwise smoothing splines as base procedures. The selection of important covariates was done by the
2013-01-01
Background Soil-transmitted helminth infections affect tens of millions of individuals in the People’s Republic of China (P.R. China). There is a need for high-resolution estimates of at-risk areas and number of people infected to enhance spatial targeting of control interventions. However, such information is not yet available for P.R. China. Methods A geo-referenced database compiling surveys pertaining to soil-transmitted helminthiasis, carried out from 2000 onwards in P.R. China, was established. Bayesian geostatistical models relating the observed survey data with potential climatic, environmental and socioeconomic predictors were developed and used to predict at-risk areas at high spatial resolution. Predictors were extracted from remote sensing and other readily accessible open-source databases. Advanced Bayesian variable selection methods were employed to develop a parsimonious model. Results Our results indicate that the prevalence of soil-transmitted helminth infections in P.R. China considerably decreased from 2005 onwards. Yet, some 144 million people were estimated to be infected in 2010. High prevalence (>20%) of the roundworm Ascaris lumbricoides infection was predicted for large areas of Guizhou province, the southern part of Hubei and Sichuan provinces, while the northern part and the south-eastern coastal-line areas of P.R. China had low prevalence (<5%). High infection prevalence (>20%) with hookworm was found in Hainan, the eastern part of Sichuan and the southern part of Yunnan provinces. High infection prevalence (>20%) with the whipworm Trichuris trichiura was found in a few small areas of south P.R. China. Very low prevalence (<0.1%) of hookworm and whipworm infections were predicted for the northern parts of P.R. China. Conclusions We present the first model-based estimates for soil-transmitted helminth infections throughout P.R. China at high spatial resolution. Our prediction maps provide useful information for the spatial targeting of
Schur, Nadine; Hürlimann, Eveline; Garba, Amadou; Traoré, Mamadou S.; Ndir, Omar; Ratard, Raoult C.; Tchuem Tchuenté, Louis-Albert; Kristensen, Thomas K.; Utzinger, Jürg; Vounatsou, Penelope
2011-01-01
Background Schistosomiasis is a water-based disease that is believed to affect over 200 million people with an estimated 97% of the infections concentrated in Africa. However, these statistics are largely based on population re-adjusted data originally published by Utroska and colleagues more than 20 years ago. Hence, these estimates are outdated due to large-scale preventive chemotherapy programs, improved sanitation, water resources development and management, among other reasons. For planning, coordination, and evaluation of control activities, it is essential to possess reliable schistosomiasis prevalence maps. Methodology We analyzed survey data compiled on a newly established open-access global neglected tropical diseases database (i) to create smooth empirical prevalence maps for Schistosoma mansoni and S. haematobium for individuals aged ≤20 years in West Africa, including Cameroon, and (ii) to derive country-specific prevalence estimates. We used Bayesian geostatistical models based on environmental predictors to take into account potential clustering due to common spatially structured exposures. Prediction at unobserved locations was facilitated by joint kriging. Principal Findings Our models revealed that 50.8 million individuals aged ≤20 years in West Africa are infected with either S. mansoni, or S. haematobium, or both species concurrently. The country prevalence estimates ranged between 0.5% (The Gambia) and 37.1% (Liberia) for S. mansoni, and between 17.6% (The Gambia) and 51.6% (Sierra Leone) for S. haematobium. We observed that the combined prevalence for both schistosome species is two-fold lower in Gambia than previously reported, while we found an almost two-fold higher estimate for Liberia (58.3%) than reported before (30.0%). Our predictions are likely to overestimate overall country prevalence, since modeling was based on children and adolescents up to the age of 20 years who are at highest risk of infection. Conclusion/Significance We
Oluwole, Akinola S; Ekpo, Uwem F; Karagiannis-Voules, Dimitrios-Alexios; Abe, Eniola M; Olamiju, Francisca O; Isiyaku, Sunday; Okoronkwo, Chukwu; Saka, Yisa; Nebe, Obiageli J; Braide, Eka I; Mafiana, Chiedu F; Utzinger, Jürg; Vounatsou, Penelope
2015-04-01
The acceleration of the control of soil-transmitted helminth (STH) infections in Nigeria, emphasizing preventive chemotherapy, has become imperative in light of the global fight against neglected tropical diseases. Predictive risk maps are an important tool to guide and support control activities. STH infection prevalence data were obtained from surveys carried out in 2011 using standard protocols. Data were geo-referenced and collated in a nationwide, geographic information system database. Bayesian geostatistical models with remotely sensed environmental covariates and variable selection procedures were utilized to predict the spatial distribution of STH infections in Nigeria. We found that hookworm, Ascaris lumbricoides, and Trichuris trichiura infections are endemic in 482 (86.8%), 305 (55.0%), and 55 (9.9%) locations, respectively. Hookworm and A. lumbricoides infection co-exist in 16 states, while the three species are co-endemic in 12 states. Overall, STHs are endemic in 20 of the 36 states of Nigeria, including the Federal Capital Territory of Abuja. The observed prevalence at endemic locations ranged from 1.7% to 51.7% for hookworm, from 1.6% to 77.8% for A. lumbricoides, and from 1.0% to 25.5% for T. trichiura. Model-based predictions ranged from 0.7% to 51.0% for hookworm, from 0.1% to 82.6% for A. lumbricoides, and from 0.0% to 18.5% for T. trichiura. Our models suggest that day land surface temperature and dense vegetation are important predictors of the spatial distribution of STH infection in Nigeria. In 2011, a total of 5.7 million (13.8%) school-aged children were predicted to be infected with STHs in Nigeria. Mass treatment at the local government area level for annual or bi-annual treatment of the school-aged population in Nigeria in 2011, based on World Health Organization prevalence thresholds, were estimated at 10.2 million tablets. The predictive risk maps and estimated deworming needs presented here will be helpful for escalating the control
Oluwole, Akinola S.; Ekpo, Uwem F.; Karagiannis-Voules, Dimitrios-Alexios; Abe, Eniola M.; Olamiju, Francisca O.; Isiyaku, Sunday; Okoronkwo, Chukwu; Saka, Yisa; Nebe, Obiageli J.; Braide, Eka I.; Mafiana, Chiedu F.; Utzinger, Jürg; Vounatsou, Penelope
2015-01-01
Background The acceleration of the control of soil-transmitted helminth (STH) infections in Nigeria, emphasizing preventive chemotherapy, has become imperative in light of the global fight against neglected tropical diseases. Predictive risk maps are an important tool to guide and support control activities. Methodology STH infection prevalence data were obtained from surveys carried out in 2011 using standard protocols. Data were geo-referenced and collated in a nationwide, geographic information system database. Bayesian geostatistical models with remotely sensed environmental covariates and variable selection procedures were utilized to predict the spatial distribution of STH infections in Nigeria. Principal Findings We found that hookworm, Ascaris lumbricoides, and Trichuris trichiura infections are endemic in 482 (86.8%), 305 (55.0%), and 55 (9.9%) locations, respectively. Hookworm and A. lumbricoides infection co-exist in 16 states, while the three species are co-endemic in 12 states. Overall, STHs are endemic in 20 of the 36 states of Nigeria, including the Federal Capital Territory of Abuja. The observed prevalence at endemic locations ranged from 1.7% to 51.7% for hookworm, from 1.6% to 77.8% for A. lumbricoides, and from 1.0% to 25.5% for T. trichiura. Model-based predictions ranged from 0.7% to 51.0% for hookworm, from 0.1% to 82.6% for A. lumbricoides, and from 0.0% to 18.5% for T. trichiura. Our models suggest that day land surface temperature and dense vegetation are important predictors of the spatial distribution of STH infection in Nigeria. In 2011, a total of 5.7 million (13.8%) school-aged children were predicted to be infected with STHs in Nigeria. Mass treatment at the local government area level for annual or bi-annual treatment of the school-aged population in Nigeria in 2011, based on World Health Organization prevalence thresholds, were estimated at 10.2 million tablets. Conclusions/Significance The predictive risk maps and estimated
Lemouzy, P.
1997-08-01
In field delineation phase, uncertainty in hydrocarbon reservoir descriptions is large. To quickly examine the impact of this uncertainty on production performance, it is necessary to evaluate a large number of descriptions in relation to possible production methods (well spacing, injection rate, etc.). The method of using coarse upscaled models was first proposed by Ballin. Unlike other methods (connectivity analysis, tracer simulations), it considers parameters such as PVT, well management, etc. After a detailed review of upscaling issues, applications to water-injection cases (either with balance or imbalance of production, with or without aquifer) and to depletion of an oil reservoir with aquifer coning are presented. Much more important than the method of permeability upscaling far from wells, the need of correct upscaling of numerical well representation is pointed out Methods are proposed to accurately represent fluids volumes in coarse models. Simple methods to upscale relative permeabilities, and methods to efficiently correct numerical dispersion are proposed. Good results are obtained for water injection. The coarse upscaling method allows the performance of sensitivity analyses on model parameters at a much lower CPU cost than comprehensive simulations. Models representing extreme behaviors can be easily distinguished. For depletion of an oil reservoir showing aquifer coning, however, the method did not work property. It is our opinion that further research is required for upscaling close to wells. We therefore recombined this method for practical use in the case of water injection.
Howes, Rosalind E.; Piel, Frédéric B.; Patil, Anand P.; Nyangiri, Oscar A.; Gething, Peter W.; Dewi, Mewahyu; Hogg, Mariana M.; Battle, Katherine E.; Padilla, Carmencita D.; Baird, J. Kevin; Hay, Simon I.
2012-01-01
Background Primaquine is a key drug for malaria elimination. In addition to being the only drug active against the dormant relapsing forms of Plasmodium vivax, primaquine is the sole effective treatment of infectious P. falciparum gametocytes, and may interrupt transmission and help contain the spread of artemisinin resistance. However, primaquine can trigger haemolysis in patients with a deficiency in glucose-6-phosphate dehydrogenase (G6PDd). Poor information is available about the distribution of individuals at risk of primaquine-induced haemolysis. We present a continuous evidence-based prevalence map of G6PDd and estimates of affected populations, together with a national index of relative haemolytic risk. Methods and Findings Representative community surveys of phenotypic G6PDd prevalence were identified for 1,734 spatially unique sites. These surveys formed the evidence-base for a Bayesian geostatistical model adapted to the gene's X-linked inheritance, which predicted a G6PDd allele frequency map across malaria endemic countries (MECs) and generated population-weighted estimates of affected populations. Highest median prevalence (peaking at 32.5%) was predicted across sub-Saharan Africa and the Arabian Peninsula. Although G6PDd prevalence was generally lower across central and southeast Asia, rarely exceeding 20%, the majority of G6PDd individuals (67.5% median estimate) were from Asian countries. We estimated a G6PDd allele frequency of 8.0% (interquartile range: 7.4–8.8) across MECs, and 5.3% (4.4–6.7) within malaria-eliminating countries. The reliability of the map is contingent on the underlying data informing the model; population heterogeneity can only be represented by the available surveys, and important weaknesses exist in the map across data-sparse regions. Uncertainty metrics are used to quantify some aspects of these limitations in the map. Finally, we assembled a database of G6PDd variant occurrences to inform a national-level index of
NASA Astrophysics Data System (ADS)
Panagos, Panos; Ballabio, Cristiano; Borrelli, Pasquale; Meusburger, Katrin; Alewell, Christine
2015-04-01
Rainfall erosivity (R-factor) is among the 6 input factors in estimating soil erosion risk by using the empirical Revised Universal Soil Loss Equation (RUSLE). R-factor is a driving force for soil erosion modelling and potentially can be used in flood risk assessments, landslides susceptibility, post-fire damage assessment, application of agricultural management practices and climate change modelling. The rainfall erosivity is extremely difficult to model at large scale (national, European) due to lack of high temporal resolution precipitation data which cover long-time series. In most cases, R-factor is estimated based on empirical equations which take into account precipitation volume. The Rainfall Erosivity Database on the European Scale (REDES) is the output of an extensive data collection of high resolution precipitation data in the 28 Member States of the European Union plus Switzerland taking place during 2013-2014 in collaboration with national meteorological/environmental services. Due to different temporal resolutions of the data (5, 10, 15, 30, 60 minutes), conversion equations have been applied in order to homogenise the database at 30-minutes interval. The 1,541 stations included in REDES have been interpolated using the Gaussian Process Regression (GPR) model using as covariates the climatic data (monthly precipitation, monthly temperature, wettest/driest month) from WorldClim Database, Digital Elevation Model and latitude/longitude. GPR has been selected among other candidate models (GAM, Regression Kriging) due the best performance both in cross validation (R2=0.63) and in fitting dataset (R2=0.72). The highest uncertainty has been noticed in North-western Scotland, North Sweden and Finland due to limited number of stations in REDES. Also, in highlands such as Alpine arch and Pyrenees the diversity of environmental features forced relatively high uncertainty. The rainfall erosivity map of Europe available at 500m resolution plus the standard error
Geostatistical modeling of riparian forest microclimate and its implications for sampling
Eskelson, B.N.I.; Anderson, P.D.; Hagar, J.C.; Temesgen, H.
2011-01-01
Predictive models of microclimate under various site conditions in forested headwater stream - riparian areas are poorly developed, and sampling designs for characterizing underlying riparian microclimate gradients are sparse. We used riparian microclimate data collected at eight headwater streams in the Oregon Coast Range to compare ordinary kriging (OK), universal kriging (UK), and kriging with external drift (KED) for point prediction of mean maximum air temperature (Tair). Several topographic and forest structure characteristics were considered as site-specific parameters. Height above stream and distance to stream were the most important covariates in the KED models, which outperformed OK and UK in terms of root mean square error. Sample patterns were optimized based on the kriging variance and the weighted means of shortest distance criterion using the simulated annealing algorithm. The optimized sample patterns outperformed systematic sample patterns in terms of mean kriging variance mainly for small sample sizes. These findings suggest methods for increasing efficiency of microclimate monitoring in riparian areas.
NASA Astrophysics Data System (ADS)
Clark, J. S.; Rizzo, D. M.; Hession, W. C.; Watzin, M. C.; Laible, J. P.
2006-05-01
A two-dimensional hydrodynamic model (River2D) was utilized to evaluate the relationship between geomorphic conditions (as estimated using an existing rapid assessment protocol) and instream habitat quality in small Vermont streams. Six stream reaches ranging in geomorphic condition from good to poor according to the protocols were utilized for this study. We conducted detailed topographic surveys, quantified bed substrate, and measured velocity and discharge values during baseflow conditions. The reach models were calibrated with realistic roughness values based on field observations and pebble counts. After calibration, the weighted usable area (WUA) of habitat was calculated for each stream at three flows (7Q 10, median, and bankfull) using modeled parameters and habitat suitability curves for specific fish species and life stage. Brown trout (Salmo trutta), white sucker (Catostomus commersoni), and common shiner (Notropis cornutus) habitats were predicted using habitat parameters of velocity, depth, and channel substrate type for adult, juvenile, and fry stages. The predictions of reach-averaged WUA show a negative correlation to the geomorphic condition scores, indicating that the often-used rapid protocols, may not directly relate to habitat conditions at the reach spatial scale. However, the areas of high WUA are distributed in a patchy nature throughout the stream. This fluctuation of physical habitat conditions may be more important to classifying habitat than a single reach-averaged WUA score. The spatial distribution of habitat variables is not captured using either the reach-averaged WUA or geomorphic assessment scores to classify streams. Spatial analyses will be used to further evaluate the patchy nature of WUA distributions, and actual data on species distributions in the study streams will be compared to modeled habitat parameters and their spatial patterns.
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
Welhan, John A.; Farabaugh, Renee L.; Merrick, Melissa J.; Anderson, Steven R.
2007-01-01
The spatial distribution of sediment in the eastern Snake River Plain aquifer was evaluated and modeled to improve the parameterization of hydraulic conductivity (K) for a subregional-scale ground-water flow model being developed by the U.S. Geological Survey. The aquifer is hosted within a layered series of permeable basalts within which intercalated beds of fine-grained sediment constitute local confining units. These sediments have K values as much as six orders of magnitude lower than the most permeable basalt, and previous flow-model calibrations have shown that hydraulic conductivity is sensitive to the proportion of intercalated sediment. Stratigraphic data in the form of sediment thicknesses from 333 boreholes in and around the Idaho National Laboratory were evaluated as grouped subsets of lithologic units (composite units) corresponding to their relative time-stratigraphic position. The results indicate that median sediment abundances of the stratigraphic units below the water table are statistically invariant (stationary) in a spatial sense and provide evidence of stationarity across geologic time, as well. Based on these results, the borehole data were kriged as two-dimensional spatial data sets representing the sediment content of the layers that discretize the ground-water flow model in the uppermost 300 feet of the aquifer. Multiple indicator kriging (mIK) was used to model the geographic distribution of median sediment abundance within each layer by defining the local cumulative frequency distribution (CFD) of sediment via indicator variograms defined at multiple thresholds. The mIK approach is superior to ordinary kriging because it provides a statistically best estimate of sediment abundance (the local median) drawn from the distribution of local borehole data, independent of any assumption of normality. A methodology is proposed for delineating and constraining the assignment of hydraulic conductivity zones for parameter estimation, based on the
Development of novel geostatistical tools in space-time modelling of aquifer level
NASA Astrophysics Data System (ADS)
Theodoridou, Giota; Varouchakis, Emmanouil A.; Karatzas, George P.
2017-04-01
Space-Time Residual-Kriging (STRK) is a reliable method for spatiotemporal interpolation. In this work STRK is applied to combine the estimated trend and interpolated residuals for the final prediction of the groundwater level in an unconfined aquifer. However, the proposed methodology examines apart from the lag distance, the hydraulic gradient for the calculation of the experimental space-time variogram. Spatiotemporal trend is approximated using a combined component based on a physical law that governs groundwater flow in an aquifer under pumping conditions. A Bayesian approach based on the bootstrap idea is employed to determine the uncertainty of the spatiotemporal model parameters (trend and covariance) and estimations. The interdependence of the spatiotemporal residuals is modeled using a new space-time variogram based on the product-sum approach that involves the hydraulic conductivity anisotropic ratio within the exponential function and the Spartan covariance family. The proposed methodology is applied to predict spatiotemporal groundwater level fluctuations and to investigate the uncertainty of estimations at a sparsely gauged basin on the island of Crete, Greece.
NASA Astrophysics Data System (ADS)
Liao, Chujiang
2015-08-01
On different degrees of desertification land, there exists different vegetation communities, and spatial structure differences are obvious among different vegetation communities. This study implemented variogram calculation using typical sample selected from the image, adopting a common global optimization method to fit them into the spherical model. The results showed that the difference is obvious among different vegetation communities for the sill and range, such as, the sill and range are smaller for sample variogram of Artemisia halodendron and Salix flavida community than that of Artemisia halodendron and Caragana microphylla community, and the range for sample variogram of Agriophyllum arenarium community is bigger than that of Artemisia halodendron and Salix flavida community, but smaller than that of Artemisia halodendron and Caragana microphylla community. Incorporating the difference of the spatial structure characterization into the vegetation classification can improve sample separation, thereby increasing the overall classification accuracy.
NASA Astrophysics Data System (ADS)
Royer, J. J.; Litaudon, J.; Filippov, L. O.; Lyubimova, T.; Maximovich, N.
2017-07-01
This work results from a cooperative scientific program between the Perm State University (Russia) and the University of Lorraine (France). Its objectives are to integrate modern 3D geomodeling in order to improve sustainable mining extraction, especially for predicting and avoiding the formation of sinkholes disaster potential zones. Systematic exploration drill holes performed in the Verkhnekamskoye potash deposit (Perm region, Russia) have been used to build a comprehensive 3D model for better understanding the spatial repartition of the ore quality (geometallurgy). A precise modelling of the mineralized layers allows an estimation of the in-situ ore reserves after interpolating by kriging the potassium (K) and magnesium (Mg) contents at the node of a regular centred grid (over a million cells). Total resources in potassium vary according to the cut-off between 4.7Gt @ 16.1 % K2O; 0.32 % MgCl2 for a cut-off grade at 13.1% K2O and 2.06 Gt @ 18.2 % K2O; 0.32 % MgCl2 at a cut-off of 16.5% K2O. Most of reserves are located in the KPI, KPII and KPIII layers, the KPI being the richest, and KPIII the largest in terms of tonnage. A systematic study of the curvature calculated along the roof of the mineralized layers points out the location of potential main faults which play a major role in the formation of sinkhole during exploitation. A risk map is then derived from this attribute.
Peterson, Erin E; Urquhart, N Scott
2006-10-01
In the United States, probability-based water quality surveys are typically used to meet the requirements of Section 305(b) of the Clean Water Act. The survey design allows an inference to be generated concerning regional stream condition, but it cannot be used to identify water quality impaired stream segments. Therefore, a rapid and cost-efficient method is needed to locate potentially impaired stream segments throughout large areas. We fit a set of geostatistical models to 312 samples of dissolved organic carbon (DOC) collected in 1996 for the Maryland Biological Stream Survey using coarse-scale watershed characteristics. The models were developed using two distance measures, straight-line distance (SLD) and weighted asymmetric hydrologic distance (WAHD). We used the Corrected Spatial Akaike Information Criterion and the mean square prediction error to compare models. The SLD models predicted more variability in DOC than models based on WAHD for every autocovariance model except the spherical model. The SLD model based on the Mariah autocovariance model showed the best fit (r(2) = 0.72). DOC demonstrated a positive relationship with the watershed attributes percent water, percent wetlands, and mean minimum temperature, but was negatively correlated to percent felsic rock type. We used universal kriging to generate predictions and prediction variances for 3083 stream segments throughout Maryland. The model predicted that 90.2% of stream kilometers had DOC values less than 5 mg/l, 6.7% were between 5 and 8 mg/l, and 3.1% of streams produced values greater than 8 mg/l. The geostatistical model generated more accurate DOC predictions than previous models, but did not fit the data equally well throughout the state. Consequently, it may be necessary to develop more than one geostatistical model to predict stream DOC throughout Maryland. Our methodology is an improvement over previous methods because additional field sampling is not necessary, inferences about regional
NASA Astrophysics Data System (ADS)
Popova, Olga H.
Dental hygiene students must embody effective critical thinking skills in order to provide evidence-based comprehensive patient care. The problem addressed in this study it was not known if and to what extent concept mapping and reflective journaling activities embedded in a curriculum over a 4-week period, impacted the critical thinking skills of 22 first and second-year dental hygiene students attending a community college in the Midwest. The overarching research questions were: what is the effect of concept mapping, and what is the effect of reflective journaling on the level of critical thinking skills of first and second year dental hygiene students? This quantitative study employed a quasi-experimental, pretest-posttest design. Analysis of Covariance (ANCOVA) assessed students' mean scores of critical thinking on the California Critical Thinking Skills Test (CCTST) pretest and posttest for the concept mapping and reflective journaling treatment groups. The results of the study found an increase in CCTST posttest scores with the use of both concept mapping and reflective journaling. However, the increase in scores was not found to be statistically significant. Hence, this study identified concept mapping using Ausubel's assimilation theory and reflective journaling incorporating Johns's revision of Carper's patterns of knowing as potential instructional strategies and theoretical models to enhance undergraduate students' critical thinking skills. More research is required in this area to draw further conclusions. Keywords: Critical thinking, critical thinking development, critical thinking skills, instructional strategies, concept mapping, reflective journaling, dental hygiene, college students.
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.
Wang, Meng; Sampson, Paul D; Hu, Jianlin; Kleeman, Michael; Keller, Joshua P; Olives, Casey; Szpiro, Adam A; Vedal, Sverre; Kaufman, Joel D
2016-05-17
Assessments of long-term air pollution exposure in population studies have commonly employed land-use regression (LUR) or chemical transport modeling (CTM) techniques. Attempts to incorporate both approaches in one modeling framework are challenging. We present a novel geostatistical modeling framework, incorporating CTM predictions into a spatiotemporal LUR model with spatial smoothing to estimate spatiotemporal variability of ozone (O3) and particulate matter with diameter less than 2.5 μm (PM2.5) from 2000 to 2008 in the Los Angeles Basin. The observations include over 9 years' data from more than 20 routine monitoring sites and specific monitoring data at over 100 locations to provide more comprehensive spatial coverage of air pollutants. Our composite modeling approach outperforms separate CTM and LUR models in terms of root-mean-square error (RMSE) assessed by 10-fold cross-validation in both temporal and spatial dimensions, with larger improvement in the accuracy of predictions for O3 (RMSE [ppb] for CTM, 6.6; LUR, 4.6; composite, 3.6) than for PM2.5 (RMSE [μg/m(3)] CTM: 13.7, LUR: 3.2, composite: 3.1). Our study highlights the opportunity for future exposure assessment to make use of readily available spatiotemporal modeling methods and auxiliary gridded data that takes chemical reaction processes into account to improve the accuracy of predictions in a single spatiotemporal modeling framework.
Multivariable geostatistics in S: the gstat package
NASA Astrophysics Data System (ADS)
Pebesma, Edzer J.
2004-08-01
This paper discusses advantages and shortcomings of the S environment for multivariable geostatistics, in particular when extended with the gstat package, an extension package for the S environments (R, S-Plus). The gstat S package provides multivariable geostatistical modelling, prediction and simulation, as well as several visualisation functions. In particular, it makes the calculation, simultaneous fitting, and visualisation of a large number of direct and cross (residual) variograms very easy. Gstat was started 10 years ago and was released under the GPL in 1996; gstat.org was started in 1998. Gstat was not initially written for teaching purposes, but for research purposes, emphasising flexibility, scalability and portability. It can deal with a large number of practical issues in geostatistics, including change of support (block kriging), simple/ordinary/universal (co)kriging, fast local neighbourhood selection, flexible trend modelling, variables with different sampling configurations, and efficient simulation of large spatially correlated random fields, indicator kriging and simulation, and (directional) variogram and cross variogram modelling. The formula/models interface of the S language is used to define multivariable geostatistical models. This paper introduces the gstat S package, and discusses a number of design and implementation issues. It also draws attention to a number of papers on integration of spatial statistics software, GIS and the S environment that were presented on the spatial statistics workshop and sessions during the conference Distributed Statistical Computing 2003.
Thompson, James A; Bissett, Wesley T; Sweeney, Anne M
2014-06-07
The first step in evaluating potential geographic clusters of disease calls for an evaluation of the disease risk comparing the risk in a defined location to the risk in neighboring locations. Environmental exposures, however, represent continuous exposure levels across space not an exposure with a distinct boundary. The objectives of the current study were to adapt, apply and evaluate a geostatistical approach for identifying disease clusters. The exceedance probability for very low birth weight (VLBW; < 1.5 kg) infants was mapped using an Intrinsic Conditional Autoregressive model. The data were applied to a 20 by 20 grid of 1 km2 pixels centered on each of the 13 National Priority List Superfund Sites in Harris County, Texas. Large clusters of VLBW were identified in close proximity to four of the 13 Superfund Sites. Three of the Superfund Sites, associated with disease clusters, were located close together in central Houston and these sites may have been surrounded by a single, confluent disease cluster. Geostatistical modeling of the exceedance probability for very low birth weights identified disease clusters of varying size, shape and statistical certainty near Superfund Sites in Harris County, Texas. The approach offers considerable potential as the first step for investigating potential disease clusters.
NASA Astrophysics Data System (ADS)
Namysłowska-Wilczyńska, Barbara
2013-03-01
The paper presents the first stage of research on a geostatistical hydrogeochemical 3D model dedicated to the horizontal and vertical spatial and time variation in the topographical, hydrological and quality parameters of underground water in the Kłodzko water intake area. The research covers the period 1977-2012. For this purpose various thematic databases, containing original data on coordinates X, Y (latitude and longitude) and Z (terrain elevation and time - years) and on regionalized variables, i.e., the underground water quality parameters in the Kłodzko water intake area determined for different analytical configurations (22 wells, 14 wells, 14 wells + 3 piezometers), were created. The data were subjected to spatial analyses using statistical methods. The input for the studies was the chemical determination of the quality parameters of underground water samples taken from the wells in the water intake area in different periods of time. Both archival data (acquired in the years 1977-1999, 1977-2011) and the latest data (collected in November 2011 and in January 2012) were analyzed. First, the underground water intake area with 22 wells was investigated. Then in order to assess the current quality of the underground water, 14 wells out of the 22 wells were selected for further chemical analyses and a collection siphon wall was included. Recently, three new piezometers were installed in the water intake area and so new water samples were taken, whereby the databases were supplemented with new chemical determinations. The variation in the topographical parameter (terrain elevation) and in the hydrogeological parameters: water abstraction level Z (with and without the land layout being taken into account) and the depth of occurrence of the water table, was examined. Subsequently, the variation in quality parameters was studied on the basis of data coming from 22 wells, then 14 wells and finally from 14 wells and 3 piezometers. The variation in: Fe, Mn, ammonium
Random vectors and spatial analysis by geostatistics for geotechnical applications
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.
Very preliminary reference Moon model
NASA Astrophysics Data System (ADS)
Garcia, Raphaël F.; Gagnepain-Beyneix, Jeannine; Chevrot, Sébastien; Lognonné, Philippe
2011-09-01
The deep structure of the Moon is a missing piece to understand the formation and evolution of the Earth-Moon system. Despite the great amount of information brought by the Apollo passive seismic experiment (ALSEP), the lunar structure below deep moonquakes, which occur around 900 km depth, remains largely unknown. We construct a reference Moon model which incorporates physical constraints, and fits both geodesic (lunar mass and polar moment of inertia, and Love numbers) and seismological (body wave arrivals measured by Apollo network) data. In this model, the core radius is constrained by the detection of S waves reflected from the core. In a first step, for each core radius, a radial model of the lunar interior, including P and S wave velocities and density, is inverted from seismic and geodesic data. In a second step, the core radius is determined from the detection of shear waves reflected on the lunar core by waveform stacking of deep moonquake Apollo records. This detection has been made possible by careful data selection and processing, including a correction of the gain of horizontal sensors based on the principle of energy equipartition inside the coda of lunar seismic records, and a precise alignment of SH waveforms by a non-linear inversion method. The Very Preliminary REference MOON model (VPREMOON) obtained here has a core radius of 380 ± 40 km and an average core mass density of 5200 ± 1000 kg/m 3. The large error bars on these estimates are due to the poorly constrained S-wave velocity profile at the base of the mantle and to mislocation errors of deep moonquakes. The detection of horizontally polarized S waves reflected from the core and the absence of detection of vertically polarized S waves favour a liquid state in the outermost part of the core. All these results are consistent, within their error bars, with previous estimates based on lunar rotation dissipation ( Williams et al., 2001) and on lunar induced magnetic moment ( Hood et al., 1999).
Geostatistical applications in environmental remediation
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.
Applications of Geostatistics in Plant Nematology
Wallace, M. K.; Hawkins, D. M.
1994-01-01
The application of geostatistics to plant nematology was made by evaluating soil and nematode data acquired from 200 soil samples collected from the Ap 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. PMID:19279938
Applications of geostatistics in plant nematology.
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.
NASA Astrophysics Data System (ADS)
Scradeanu, D.; Pagnejer, M.
2012-04-01
The purpose of the works is to evaluate the uncertainty of the hydrodynamic model for a multilayered geological structure, a potential trap for carbon dioxide storage. The hydrodynamic model is based on a conceptual model of the multilayered hydrostructure with three components: 1) spatial model; 2) parametric model and 3) energy model. The necessary data to achieve the three components of the conceptual model are obtained from: 240 boreholes explored by geophysical logging and seismic investigation, for the first two components, and an experimental water injection test for the last one. The hydrodinamic model is a finite difference numerical model based on a 3D stratigraphic model with nine stratigraphic units (Badenian and Oligocene) and a 3D multiparameter model (porosity, permeability, hydraulic conductivity, storage coefficient, leakage etc.). The uncertainty of the two 3D models was evaluated using multivariate geostatistical tools: a)cross-semivariogram for structural analysis, especially the study of anisotropy and b)cokriging to reduce estimation variances in a specific situation where is a cross-correlation between a variable and one or more variables that are undersampled. It has been identified important differences between univariate and bivariate anisotropy. The minimised uncertainty of the parametric model (by cokriging) was transferred to hydrodynamic model. The uncertainty distribution of the pressures generated by the water injection test has been additional filtered by the sensitivity of the numerical model. The obtained relative errors of the pressure distribution in the hydrodynamic model are 15-20%. The scientific research was performed in the frame of the European FP7 project "A multiple space and time scale approach for the quantification of deep saline formation for CO2 storage(MUSTANG)".
Karacan, C. Özgen; Olea, Ricardo A.; Goodman, Gerrit
2015-01-01
Determination of the size of the gas emission zone, the locations of gas sources within, and especially the amount of gas retained in those zones is one of the most important steps for designing a successful methane control strategy and an efficient ventilation system in longwall coal mining. The formation of the gas emission zone and the potential amount of gas-in-place (GIP) that might be available for migration into a mine are factors of local geology and rock properties that usually show spatial variability in continuity and may also show geometric anisotropy. Geostatistical methods are used here for modeling and prediction of gas amounts and for assessing their associated uncertainty in gas emission zones of longwall mines for methane control. This study used core data obtained from 276 vertical exploration boreholes drilled from the surface to the bottom of the Pittsburgh coal seam in a mining district in the Northern Appalachian basin. After identifying important coal and non-coal layers for the gas emission zone, univariate statistical and semivariogram analyses were conducted for data from different formations to define the distribution and continuity of various attributes. Sequential simulations performed stochastic assessment of these attributes, such as gas content, strata thickness, and strata displacement. These analyses were followed by calculations of gas-in-place and their uncertainties in the Pittsburgh seam caved zone and fractured zone of longwall mines in this mining district. Grid blanking was used to isolate the volume over the actual panels from the entire modeled district and to calculate gas amounts that were directly related to the emissions in longwall mines. Results indicated that gas-in-place in the Pittsburgh seam, in the caved zone and in the fractured zone, as well as displacements in major rock units, showed spatial correlations that could be modeled and estimated using geostatistical methods. This study showed that GIP volumes may
Karacan, C.O.; Olea, R.A.; Goodman, G.
2012-01-01
Determination of the size of the gas emission zone, the locations of gas sources within, and especially the amount of gas retained in those zones is one of the most important steps for designing a successful methane control strategy and an efficient ventilation system in longwall coal mining. The formation of the gas emission zone and the potential amount of gas-in-place (GIP) that might be available for migration into a mine are factors of local geology and rock properties that usually show spatial variability in continuity and may also show geometric anisotropy. Geostatistical methods are used here for modeling and prediction of gas amounts and for assessing their associated uncertainty in gas emission zones of longwall mines for methane control.This study used core data obtained from 276 vertical exploration boreholes drilled from the surface to the bottom of the Pittsburgh coal seam in a mining district in the Northern Appalachian basin. After identifying important coal and non-coal layers for the gas emission zone, univariate statistical and semivariogram analyses were conducted for data from different formations to define the distribution and continuity of various attributes. Sequential simulations performed stochastic assessment of these attributes, such as gas content, strata thickness, and strata displacement. These analyses were followed by calculations of gas-in-place and their uncertainties in the Pittsburgh seam caved zone and fractured zone of longwall mines in this mining district. Grid blanking was used to isolate the volume over the actual panels from the entire modeled district and to calculate gas amounts that were directly related to the emissions in longwall mines.Results indicated that gas-in-place in the Pittsburgh seam, in the caved zone and in the fractured zone, as well as displacements in major rock units, showed spatial correlations that could be modeled and estimated using geostatistical methods. This study showed that GIP volumes may
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
Fraczek, W; Bytnerowicz, A; Arbaugh, M J
2001-12-07
Models of O3 distribution in two mountain ranges, the Carpathians in Central Europe and the Sierra Nevada in California were constructed using ArcGIS Geostatistical Analyst extension (ESRI, Redlands, CA) using kriging and cokriging methods. The adequacy of the spatially interpolated ozone (O3) concentrations and sample size requirements for ozone passive samplers was also examined. In case of the Carpathian Mountains, only a general surface of O3 distribution could be obtained, partially due to a weak correlation between O3 concentration and elevation, and partially due to small numbers of unevenly distributed sample sites. In the Sierra Nevada Mountains, the O3 monitoring network was much denser and more evenly distributed, and additional climatologic information was available. As a result the estimated surfaces were more precise and reliable than those created for the Carpathians. The final maps of O3 concentrations for Sierra Nevada were derived from cokriging algorithm based on two secondary variables--elevation and maximum temperature as well as the determined geographic trend. Evenly distributed and sufficient numbers of sample points are a key factor for model accuracy and reliability.
Preliminary Cost Model for Space Telescopes
NASA Technical Reports Server (NTRS)
Stahl, H. Philip; Prince, F. Andrew; Smart, Christian; Stephens, Kyle; Henrichs, Todd
2009-01-01
Parametric cost models are routinely used to plan missions, compare concepts and justify technology investments. However, great care is required. Some space telescope cost models, such as those based only on mass, lack sufficient detail to support such analysis and may lead to inaccurate conclusions. Similarly, using ground based telescope models which include the dome cost will also lead to inaccurate conclusions. This paper reviews current and historical models. Then, based on data from 22 different NASA space telescopes, this paper tests those models and presents preliminary analysis of single and multi-variable space telescope cost models.
Zhong, Buqing; Liang, Tao; Wang, Lingqing; Li, Kexin
2014-08-15
An extensive soil survey was conducted to study pollution sources and delineate contamination of heavy metals in one of the metalliferous industrial bases, in the karst areas of southwest China. A total of 597 topsoil samples were collected and the concentrations of five heavy metals, namely Cd, As (metalloid), Pb, Hg and Cr were analyzed. Stochastic models including a conditional inference tree (CIT) and a finite mixture distribution model (FMDM) were applied to identify the sources and partition the contribution from natural and anthropogenic sources for heavy metal in topsoils of the study area. Regression trees for Cd, As, Pb and Hg were proved to depend mostly on indicators of anthropogenic activities such as industrial type and distance from urban area, while the regression tree for Cr was found to be mainly influenced by the geogenic characteristics. The FMDM analysis showed that the geometric means of modeled background values for Cd, As, Pb, Hg and Cr were close to their background values previously reported in the study area, while the contamination of Cd and Hg were widespread in the study area, imposing potentially detrimental effects on organisms through the food chain. Finally, the probabilities of single and multiple heavy metals exceeding the threshold values derived from the FMDM were estimated using indicator kriging (IK) and multivariate indicator kriging (MVIK). The high probabilities exceeding the thresholds of heavy metals were associated with metalliferous production and atmospheric deposition of heavy metals transported from the urban and industrial areas. Geostatistics coupled with stochastic models provide an effective way to delineate multiple heavy metal pollution to facilitate improved environmental management. Copyright © 2014 Elsevier B.V. All rights reserved.
Imprecise (fuzzy) information in geostatistics
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.
Hydrogeologic unit flow characterization using transition probability geostatistics.
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.
Preliminary Model of Porphyry Copper Deposits
Berger, Byron R.; Ayuso, Robert A.; Wynn, Jeffrey C.; Seal, Robert R., II
2008-01-01
The U.S. Geological Survey (USGS) Mineral Resources Program develops mineral-deposit models for application in USGS mineral-resource assessments and other mineral resource-related activities within the USGS as well as for nongovernmental applications. Periodic updates of models are published in order to incorporate new concepts and findings on the occurrence, nature, and origin of specific mineral deposit types. This update is a preliminary model of porphyry copper deposits that begins an update process of porphyry copper models published in USGS Bulletin 1693 in 1986. This update includes a greater variety of deposit attributes than were included in the 1986 model as well as more information about each attribute. It also includes an expanded discussion of geophysical and remote sensing attributes and tools useful in resource evaluations, a summary of current theoretical concepts of porphyry copper deposit genesis, and a summary of the environmental attributes of unmined and mined deposits.
Akita, Yasuyuki; Baldasano, Jose M; Beelen, Rob; Cirach, Marta; de Hoogh, Kees; Hoek, Gerard; Nieuwenhuijsen, Mark; Serre, Marc L; de Nazelle, Audrey
2014-04-15
In recognition that intraurban exposure gradients may be as large as between-city variations, recent air pollution epidemiologic studies have become increasingly interested in capturing within-city exposure gradients. In addition, because of the rapidly accumulating health data, recent studies also need to handle large study populations distributed over large geographic domains. Even though several modeling approaches have been introduced, a consistent modeling framework capturing within-city exposure variability and applicable to large geographic domains is still missing. To address these needs, we proposed a modeling framework based on the Bayesian Maximum Entropy method that integrates monitoring data and outputs from existing air quality models based on Land Use Regression (LUR) and Chemical Transport Models (CTM). The framework was applied to estimate the yearly average NO2 concentrations over the region of Catalunya in Spain. By jointly accounting for the global scale variability in the concentration from the output of CTM and the intraurban scale variability through LUR model output, the proposed framework outperformed more conventional approaches.
Reducing uncertainty in geostatistical description with well testing pressure data
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.
Geostatistics applied to gas reservoirs
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.
NASA Astrophysics Data System (ADS)
Moral, Francisco J.; Álvarez, Pedro; Canito, José L.
Researchers or decision-makers frequently need information about atmospheric pollution patterns in urbanized areas. The preparation of this type of information is a complex task, due to the influence of several individual pollutants, with different units, on the global air pollution (e.g. nitrogen dioxide concentrations, ppm, and noise, dB). In this work, a new methodology based on the formulation of the Rasch model is proposed to obtain a measure of the atmospheric pollution. Two main results were obtained after applying this method: (1) A classification of all locations according to the pollution level, which was the value of the Rasch measure; (2) The influence on the environmental deterioration of each individual pollutant (particularly, in this work, NO 2, NO, CO 2, CO and noise). Finally, pollution at locations where no measurements were available was estimated with the optimum interpolation technique, kriging. Kriged estimates were subsequently used to map atmospheric pollution. To illustrate the application of this two-step method (Rasch model plus interpolation), which is useful to generate hazard assessment maps based on the spatial distribution of atmospheric pollution, an example is shown.
Using geostatistics to evaluate cleanup goals
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.
Satellite servicing mission preliminary cost estimation model
NASA Technical Reports Server (NTRS)
1987-01-01
The cost model presented is a preliminary methodology for determining a rough order-of-magnitude cost for implementing a satellite servicing mission. Mission implementation, in this context, encompassess all activities associated with mission design and planning, including both flight and ground crew training and systems integration (payload processing) of servicing hardward with the Shuttle. A basic assumption made in developing this cost model is that a generic set of servicing hardware was developed and flight tested, is inventoried, and is maintained by NASA. This implies that all hardware physical and functional interfaces are well known and therefore recurring CITE testing is not required. The development of the cost model algorithms and examples of their use are discussed.
NASA Astrophysics Data System (ADS)
Galeana-Pizaña, J. Mauricio; López-Caloca, Alejandra; López-Quiroz, Penélope; Silván-Cárdenas, José Luis; Couturier, Stéphane
2014-08-01
Forest conservation is considered an option for mitigating the effect of greenhouse gases on global climate, hence monitoring forest carbon pools at global and local levels is important. The present study explores the capability of remote-sensing variables (vegetation indices and textures derived from SPOT-5; backscattering coefficient and interferometric coherence of ALOS PALSAR images) for modeling the spatial distribution of above-ground biomass in the Environmental Conservation Zone of Mexico City. Correlation and spatial autocorrelation coefficients were used to select significant explanatory variables in fir and pine forests. The correlation for interferometric coherence in HV polarization was negative, with correlations coefficients r = -0.83 for the fir and r = -0.75 for the pine forests. Regression-kriging showed the least root mean square error among the spatial interpolation methods used, with 37.75 tC/ha for fir forests and 29.15 tC/ha for pine forests. The results showed that a hybrid geospatial method, based on interferometric coherence data and a regression-kriging interpolator, has good potential for estimating above-ground biomass carbon.
Breast carcinoma, intratumour heterogeneity and histological grading, using geostatistics.
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.
Using geostatistics to estimate coal reserves
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.)
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
second phase, we develop a module, to be added to the flow-duration curve prediction framework, capable of enhancing E-HYPE-based predictions of FDCs by modelling the residuals obtained from the first phase. Among all possible methods, we apply geostatistical modelling of residuals and, alternatively, regional regression, so that residuals between empirical and E-HYPE-base predicted FDCs are described in terms of geomorphological and climatic catchment descriptors.
Reservoir property grids improve with geostatistics
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.
Peter R. Robichaud
1997-01-01
Geostatistics provides a method to describe the spatial continuity of many natural phenomena. Spatial models are based upon the concept of scaling, kriging and conditional simulation. These techniques were used to describe the spatially-varied surface conditions on timber harvest and burned hillslopes. Geostatistical techniques provided estimates of the ground cover (...
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
Xiao, Yong; Gu, Xiaomin; Yin, Shiyang; Shao, Jingli; Cui, Yali; Zhang, Qiulan; Niu, Yong
2016-01-01
Based on the geo-statistical theory and ArcGIS geo-statistical module, datas of 30 groundwater level observation wells were used to estimate the decline of groundwater level in Beijing piedmont. Seven different interpolation methods (inverse distance weighted interpolation, global polynomial interpolation, local polynomial interpolation, tension spline interpolation, ordinary Kriging interpolation, simple Kriging interpolation and universal Kriging interpolation) were used for interpolating groundwater level between 2001 and 2013. Cross-validation, absolute error and coefficient of determination (R(2)) was applied to evaluate the accuracy of different methods. The result shows that simple Kriging method gave the best fit. The analysis of spatial and temporal variability suggest that the nugget effects from 2001 to 2013 were increasing, which means the spatial correlation weakened gradually under the influence of human activities. The spatial variability in the middle areas of the alluvial-proluvial fan is relatively higher than area in top and bottom. Since the changes of the land use, groundwater level also has a temporal variation, the average decline rate of groundwater level between 2007 and 2013 increases compared with 2001-2006. Urban development and population growth cause over-exploitation of residential and industrial areas. The decline rate of the groundwater level in residential, industrial and river areas is relatively high, while the decreasing of farmland area and development of water-saving irrigation reduce the quantity of water using by agriculture and decline rate of groundwater level in agricultural area is not significant.
Preliminary Proactive Sample Size Determination for Confirmatory Factor Analysis Models
ERIC Educational Resources Information Center
Koran, Jennifer
2016-01-01
Proactive preliminary minimum sample size determination can be useful for the early planning stages of a latent variable modeling study to set a realistic scope, long before the model and population are finalized. This study examined existing methods and proposed a new method for proactive preliminary minimum sample size determination.
Addressing uncertainty in rock properties through geostatistical simulation
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.
Assessing the resolution-dependent utility of tomograms for geostatistics
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.
Combining geostatistics with Moran's I analysis for mapping soil heavy metals in Beijing, China.
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.
Preliminary Phase Field Computational Model Development
Li, Yulan; Hu, Shenyang Y.; Xu, Ke; Suter, Jonathan D.; McCloy, John S.; Johnson, Bradley R.; Ramuhalli, Pradeep
2014-12-15
experiments, special experimental methods were devised to create similar boundary conditions in the iron films. Preliminary MFM studies conducted on single and polycrystalline iron films with small sub-areas created with focused ion beam have correlated quite well qualitatively with phase-field simulations. However, phase-field model dimensions are still small relative to experiments thus far. We are in the process of increasing the size of the models and decreasing specimen size so both have identical dimensions. Ongoing research is focused on validation of the phase-field model. Validation is being accomplished through comparison with experimentally obtained MFM images (in progress), and planned measurements of major hysteresis loops and first order reversal curves. Extrapolation of simulation sizes to represent a more stochastic bulk-like system will require sampling of various simulations (i.e., with single non-magnetic defect, single magnetic defect, single grain boundary, single dislocation, etc.) with distributions of input parameters. These outputs can then be compared to laboratory magnetic measurements and ultimately to simulate magnetic Barkhausen noise signals.
Importance of stationarity for geostatistical assessment of environmental contamination
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.
A preliminary model of wheelchair service delivery.
Eggers, Sara L; Myaskovsky, Larissa; Burkitt, Kelly H; Tolerico, Michelle; Switzer, Galen E; Fine, Michael J; Boninger, Michael L
2009-06-01
To integrate and expand on previously published models of wheelchair service delivery, and provide a preliminary framework for developing more comprehensive, descriptive models of wheelchair service delivery for adults with spinal cord injury within the U.S. health care system. Literature review and a qualitative analysis of in-depth interviews. Not applicable. Ten academic, clinical, regulatory, and industry experts (Department of Veterans Affairs [VA] and non-VA) in wheelchair service delivery. Not applicable. Interviewees were asked to discuss the full range of variables and stakeholders involved in wheelchair service delivery, and to limit their scope to the provision of primary subsequent or replacement chairs (not backup chairs) to adults within the United States. Most experts we interviewed stressed that clients who require a wheelchair play a central role in the wheelchair service delivery process. Providers (including clinicians, rehabilitation engineers, and rehabilitation counselors) are also critical stakeholders. More so than in other health care settings, suppliers play an integral role in the provision of wheelchairs to clients and may significantly influence the appropriateness of the wheelchair provided. Suppliers often have a direct role in wheelchair service delivery through their interactions with the clinician and/or client. This model also identified a number of system-level factors (including facility administration and standards, policies, and regulations) that influence wheelchair service delivery and ultimately the appropriateness of the wheelchair provided. We developed a detailed, descriptive model of wheelchair service delivery that integrates the delivery process and device outcomes, and includes the patient-level, provider-level, and system-level factors that may directly influence those processes and outcomes. We believe that this detailed model can help clinicians and researchers describe and consider the complexities of wheelchair
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
Understanding and predicting the spatiotemporal patterns of precipitation in the Mediterranean islands is an important topic of research, which is emphasized by alarming long-term predictions for increased drought conditions [4]. The analysis of records from drought-prone areas around the world has demonstrated that precipitation data are non-Gaussian. Typically, such data are fitted to the gamma distribution function and then transformed into a normalized index, the so-called Standardized Precipitation Index (SPI) [5]. The SPI can be defined for different time scales and has been applied to data from various regions [2]. Precipitation maps can be constructed using the stochastic method of Ordinary Kriging [1]. Such mathematical tools help to better understand the space-time variability and to plan water resources management. We present preliminary results of an ongoing investigation of the space-time precipitation distribution on the island of Crete (Greece). The study spans the time period from 1948 to 2012 and extends over an area of 8 336 km2. The data comprise monthly precipitation measured at 56 stations. Analysis of the data showed that the most severe drought occurred in 1950 followed by 1989, whereas the wettest year was 2002 followed by 1977. A spatial trend was observed with the spatially averaged annual precipitation in the West measured at about 450mm higher than in the East. Analysis of the data also revealed strong correlations between the precipitation in the western and eastern parts of the island. In addition to longitude, elevation (masl) was determined to be an important factor that exhibits strong linear correlation with precipitation. The precipitation data exhibit wet and dry periods with strong variability even during the wet period. Thus, fitting the data to specific probability distribution models has proved challenging. Different time scales, e.g. monthly, biannual, and annual have been investigated. Herein we focus on annual
GEOSTATISTICAL SAMPLING DESIGNS FOR HAZARDOUS WASTE SITES
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...
GEOSTATISTICAL SAMPLING DESIGNS FOR HAZARDOUS WASTE SITES
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...
Hydrogeologic Unit Flow Characterization Using Transition Probability Geostatistics
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.
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
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
[Tuscan Chronic Care Model: a preliminary analysis].
Barbato, Angelo; Meggiolaro, Angela; Rossi, Luigi; Fioravanti, C; Palermita, F; La Torre, Giuseppe
2015-01-01
the aim of this study is to present a preliminary analysis of efficacy and effectiveness of a model of chronically ill care (Chronic Care Model, CCM). the analysis took into account 106 territorial modules, 1016 General Practitioners and 1,228,595 patients. The diagnostic and therapeutic pathways activated (PDTA), involved four chronic conditions, selected according to the prevalence and incidence, in Tuscany Region: Diabetes Mellitus (DM), Heart Failure (SC), Chronic Obstructive Pulmonary Disease (COPD) and stroke. Six epidemiological indicators of process and output were selected, in order to measure the model of care performed, before and after its application: adherence to specific follow-up for each pathology (use of clinical and laboratory indicators), annual average of expenditure per/capita/euro for diagnostic tests, in laboratory and instrumental, average expenditure per/capita/year for specialist visits; hospitalization rate for diseases related to the main pathology, hospitalization rate for long-term complications and rate of access to the emergency department (ED). Data were collected through the database; the differences before and after the intervention and between exposed and unexposed, were analyzed by method "Before-After (Controlled and Uncontrolled) Studies". The impact of the intervention was calculated as DD (difference of the differences). DM management showed an increased adhesion to follow-up (DD: +8.1%), and the use of laboratory diagnostics (DD: +4,9 €/year/pc), less hospitalization for long-term complications and for endocrine related diseases (DD respectively: 5.8/1000 and DD: +1.2/1000), finally a smaller increase of access to PS (DD: -1.6/1000), despite a slight increase of specialistic visits (DD: +0,38 €/year/pc). The management of SC initially showed a rising adherence to follow-up (DD: +2.3%), a decrease of specialist visits (DD:E 1.03 €/year/pc), hospitalization and access to PS for exacerbations (DD: -4.4/1000 and DD: -6
Preliminary Multi-Variable Parametric Cost Model for Space Telescopes
NASA Technical Reports Server (NTRS)
Stahl, H. Philip; Hendrichs, Todd
2010-01-01
This slide presentation reviews creating a preliminary multi-variable cost model for the contract costs of making a space telescope. There is discussion of the methodology for collecting the data, definition of the statistical analysis methodology, single variable model results, testing of historical models and an introduction of the multi variable models.
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.
Preliminary results of steel containment vessel model test
Luk, V.K.; Hessheimer, M.F.; Matsumoto, T.; Komine, K.; Arai, S.; Costello, J.F.
1998-04-01
A high pressure test of a mixed-scaled model (1:10 in geometry and 1:4 in shell thickness) of a steel containment vessel (SCV), representing an improved boiling water reactor (BWR) Mark II containment, was conducted on December 11--12, 1996 at Sandia National Laboratories. This paper describes the preliminary results of the high pressure test. In addition, the preliminary post-test measurement data and the preliminary comparison of test data with pretest analysis predictions are also presented.
Geostatistics and spatial analysis in biological anthropology.
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. (c) 2008 Wiley-Liss, Inc.
Book Review Geostatistical Analysis of Compositional Data
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.
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.
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...
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...
Preliminary Evaluation of the Full-Purpose Partnership Schoolwide Model
ERIC Educational Resources Information Center
Smith, Joshua S.; Anderson, Jeffrey A.; Abell, Amy K.
2008-01-01
The full-purpose partnership (FPP) schoolwide model primarily focuses on prevention and early intervention. This model brings the tenets of service coordination directly into the school and focuses efforts to involve families from a strengths perspective before behavioral issues become significant. A preliminary evaluation has shown that this…
Outline and Preliminary Evaluation of the Classical Digital Library Model.
ERIC Educational Resources Information Center
MacCall, Steven L.; Cleveland, Ana D.; Gibson, Ian E.
1999-01-01
Outlines the classical digital library model, which is derived from traditional practices of library and information science professionals, as an alternative to the database retrieval model. Reports preliminary results from an evaluation study of library and information professionals and endusers involved with primary care medicine. (AEF)
A preliminary model of the coma of 2060 Chiron
NASA Technical Reports Server (NTRS)
Boice, Daniel C.; Konno, I.; Stern, S. Alan; Huebner, Walter F.
1992-01-01
We have included gravity in our fluid dynamic model with chemical kinetics of dusty comet comae and applied it with two dust sizes to 2060 Chiron. A progress report on the model and preliminary results concerning gas/dust dynamics and chemistry is given.
Improved Reconstruction of Two-Dimensional Resistivity Field Data Using Geostatistics
NASA Astrophysics Data System (ADS)
Bagtzoglou, A.; Lane, J.; Cornacchiulo, D.; Ergun, K.
2003-04-01
The need to reduce the effects of various type of noise observed in geophysical field data motivated this study to determine if geostatistical methods (in this case krigging) could be used to restore noisy field data eliminated from source files prior to inversion. We used resistivity forward and inversion modeling software for a simple fault earth model to produce and invert synthetic datasets that were manipulated using a geostatistical program developed for this study. The effect of a range of random noise and data density deletion were examined to study the influence these factors have on the inversion of resistivity data and the subsequent interpretability of the geologic structure.
Preliminary design specifications of a calcium model
NASA Technical Reports Server (NTRS)
1978-01-01
A list of objectives, requirements, and guidelines are given for a calcium model. Existing models are reviewed and evaluated in relation to the stated objectives and requirements. The reviewed models were either too abstract or apparently invalidated. A technical approach to the design of a desirable model is identified.
Geostatistics for environmental and geotechnical applications
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.
Geostatistical enhancement of macro-scale runoff simulations
NASA Astrophysics Data System (ADS)
Pugliese, Alessio; Persiano, Simone; Castellarin, Attilio; Parajka, Juraj; Arheimer, Berit; Capell, René; Bagli, Stefano; Mazzoli, Paolo; Montanari, Alberto; Blöschl, Günter
2017-04-01
This study presents the results of the research experiment Geostatistical Enhancement of European Hydrological Prediction (GEEHP). GEEHP is developed within the EU funded SWITCH-ON project, which proposes to conduct collaborative experiments in a virtual laboratory in order to share water-related information and to tackle changes in the hydrosphere for operational needs (http://www.water-switch-on.eu). The main objective of GEEHP is to develop a simple and easy-to-apply technique for locally enhancing the performance of macro-scale rainfall-runoff models on the basis of observed streamflow data available at nearby streamgauges, without re-running computationally intensive rainfall-runoff simulations. The experiment relies upon the prediction of regional period-of-record flow-duration curves (FDCs) by means of a geostatistical procedure based on Top-kriging, which has been recently shown to be particularly reliable for the regionalization of FDCs. The procedure developed employs two different types of daily streamflow data collected in a limited portion of territories centred in Tyrol (Austria and Italy): large-scale rainfall-runoff model simulation series (EHYPE, http://hypeweb.smhi.se/europehype) and observed series from 46 gauged catchments. The first phase of the experiment required the implementation and cross-validation of the geostatistically-based regional model over the study area, capable of predicting FDCs in ungauged sites. Cross-validation results showed good overall performances of the regional model, with an average Nash-Sutcliffe efficiency on log-flows (LNSE) equal to 0.898 over the entire river network in Tyrol. In a second phase, we selected 11 target catchments within the study area, for which both EHYPE simulations and observed data were available over the period 1980-2010. Then, we computed residuals between Top-kriged FDCs and FDCs constructed from simulated streamflow series, and, finally, we used these residuals for enhancing simulated time
Using geostatistics to predict the characteristics of washed coal
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.
Preliminary Multivariable Cost Model for Space Telescopes
NASA Technical Reports Server (NTRS)
Stahl, H. Philip
2010-01-01
Parametric cost models are routinely used to plan missions, compare concepts and justify technology investments. Previously, the authors published two single variable cost models based on 19 flight missions. The current paper presents the development of a multi-variable space telescopes cost model. The validity of previously published models are tested. Cost estimating relationships which are and are not significant cost drivers are identified. And, interrelationships between variables are explored
An Inverse Model for TETRAD: Preliminary Results
Shook, George Michael; Renner, Joel Lawrence
2002-09-01
A model-independent parameter estimation model known as PEST has been linked to the reservoir simulator TETRAD. The method of inverse modeling is briefly reviewed, and the link between PEST and TETRAD is discussed. A single example is presented that illustrates the power of parameter estimation from well observations.
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.
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.
Use of geostatistics for remediation planning to transcend urban political boundaries.
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.
V and V Efforts of Auroral Precipitation Models: Preliminary Results
NASA Technical Reports Server (NTRS)
Zheng, Yihua; Kuznetsova, Masha; Rastaetter, Lutz; Hesse, Michael
2011-01-01
Auroral precipitation models have been valuable both in terms of space weather applications and space science research. Yet very limited testing has been performed regarding model performance. A variety of auroral models are available, including empirical models that are parameterized by geomagnetic indices or upstream solar wind conditions, now casting models that are based on satellite observations, or those derived from physics-based, coupled global models. In this presentation, we will show our preliminary results regarding V&V efforts of some of the models.
Intellectual Competence and Academic Performance: Preliminary Validation of a Model
ERIC Educational Resources Information Center
Chamorro-Premuzic, Tomas; Arteche, Adriane
2008-01-01
The present study provides a preliminary empirical test of [Chamorro-Premuzic, T., & Furnham, A. (2004). A possible model to understand the personality-intelligence interface. "British Journal of Psychology," 95, 249-264], [Chamorro-Premuzic, T., & Furnham, A. (2006a). Intellectual competence and the intelligent personality: A…
Intellectual Competence and Academic Performance: Preliminary Validation of a Model
ERIC Educational Resources Information Center
Chamorro-Premuzic, Tomas; Arteche, Adriane
2008-01-01
The present study provides a preliminary empirical test of [Chamorro-Premuzic, T., & Furnham, A. (2004). A possible model to understand the personality-intelligence interface. "British Journal of Psychology," 95, 249-264], [Chamorro-Premuzic, T., & Furnham, A. (2006a). Intellectual competence and the intelligent personality: A…
Modeling method and preliminary model of Asteroid Toutatis from Chang'E-2 optical images
NASA Astrophysics Data System (ADS)
Li, Xiang-Yu; Qiao, Dong
2014-06-01
Shape modeling is fundamental to the analysis of dynamic environment and motion around asteroid. Chang'E-2 successfully made a flyby of Asteroid 4179 Toutatis and obtained plenty of high-resolution images during the mission. In this paper, the modeling method and preliminary model of Asteroid Toutatis are discussed. First, the optical images obtained by Chang'E-2 are analyzed. Terrain and silhouette features in images are described. Then, the modeling method based on previous radar model and preliminary information from optical images is proposed. A preliminary polyhedron model of Asteroid Toutatis is established. Finally, the spherical harmonic coefficients of Asteroid Toutatis based on the polyhedron model are obtained. Some parameters of model are analyzed and compared. Although the model proposed in this paper is only a preliminary model, this work offers a valuable reference for future high-resolution models.
Geospatial Interpolation and Mapping of Tropospheric Ozone Pollution Using Geostatistics
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
Assessment of spatial distribution of fallout radionuclides through geostatistics concept.
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.
Geospatial interpolation and mapping of tropospheric ozone pollution using geostatistics.
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.
Preliminary Saturated-Zone Flow Model
1997-06-10
This milestone consists of an updated fully 3D model of ground-water flow within the saturated zone at Yucca Mountain, Nevada. All electronic files pertaining to this deliverable have been transferred via ftp transmission to Steve Bodnar (M and O) and the technical data base. The model was developed using a flow and transport simulator, FEHMN, developed at Los Alamos National Laboratory, and represents a collaborative effort between staff from the US Geological Survey and Los Alamos National Laboratory. The model contained in this deliverable is minimally calibrated and represents work in progress. The flow model developed for this milestone is designed to feed subsequent transport modeling studies at Los Alamos which also use the FEHMN software. In addition, a general-application parameter estimation routine, PEST, was used in conjunction with FEHMN to reduce the difference between observed and simulated values of hydraulic head through the adjustment of model variables. This deliverable in large part consists of the electronic files for Yucca Mountain Site saturated-zone flow model as it existed as of 6/6/97, including the executable version of FEHMN (accession no. MOL.19970610.0204) used to run the code on a Sun Ultrasparc I workstation. It is expected that users of the contents of this deliverable be knowledgeable about the oration of FEHMN.
Stochastic modeling of a lava-flow aquifer system
Cronkite-Ratcliff, Collin; Phelps, Geoffrey A.
2014-01-01
This report describes preliminary three-dimensional geostatistical modeling of a lava-flow aquifer system using a multiple-point geostatistical model. The purpose of this study is to provide a proof-of-concept for this modeling approach. An example of the method is demonstrated using a subset of borehole geologic data and aquifer test data from a portion of the Calico Hills Formation, a lava-flow aquifer system that partially underlies Pahute Mesa, Nevada. Groundwater movement in this aquifer system is assumed to be controlled by the spatial distribution of two geologic units—rhyolite lava flows and zeolitized tuffs. The configuration of subsurface lava flows and tuffs is largely unknown because of limited data. The spatial configuration of the lava flows and tuffs is modeled by using a multiple-point geostatistical simulation algorithm that generates a large number of alternative realizations, each honoring the available geologic data and drawn from a geologic conceptual model of the lava-flow aquifer system as represented by a training image. In order to demonstrate how results from the geostatistical model could be analyzed in terms of available hydrologic data, a numerical simulation of part of an aquifer test was applied to the realizations of the geostatistical model.
Preliminary shuttle structural dynamics modeling design study
NASA Technical Reports Server (NTRS)
1972-01-01
The design and development of a structural dynamics model of the space shuttle are discussed. The model provides for early study of structural dynamics problems, permits evaluation of the accuracy of the structural and hydroelastic analysis methods used on test vehicles, and provides for efficiently evaluating potential cost savings in structural dynamic testing techniques. The discussion is developed around the modes in which major input forces and responses occur and the significant structural details in these modes.
Utility of Social Modeling for Proliferation Assessment - Preliminary Assessment
Coles, Garill A.; Gastelum, Zoe N.; Brothers, Alan J.; Thompson, Sandra E.
2009-06-01
This Preliminary Assessment draft report will present the results of a literature search and preliminary assessment of the body of research, analysis methods, models and data deemed to be relevant to the Utility of Social Modeling for Proliferation Assessment research. This report will provide: 1) a description of the problem space and the kinds of information pertinent to the problem space, 2) a discussion of key relevant or representative literature, 3) a discussion of models and modeling approaches judged to be potentially useful to the research, and 4) the next steps of this research that will be pursued based on this preliminary assessment. This draft report represents a technical deliverable for the NA-22 Simulations, Algorithms, and Modeling (SAM) program. Specifically this draft report is the Task 1 deliverable for project PL09-UtilSocial-PD06, Utility of Social Modeling for Proliferation Assessment. This project investigates non-traditional use of social and cultural information to improve nuclear proliferation assessment, including nonproliferation assessment, proliferation resistance assessments, safeguards assessments and other related studies. These assessments often use and create technical information about the State’s posture towards proliferation, the vulnerability of a nuclear energy system to an undesired event, and the effectiveness of safeguards. This project will find and fuse social and technical information by explicitly considering the role of cultural, social and behavioral factors relevant to proliferation. The aim of this research is to describe and demonstrate if and how social science modeling has utility in proliferation assessment.
A preliminary weather model for optical communications through the atmosphere
NASA Technical Reports Server (NTRS)
Shaik, K. S.
1988-01-01
A preliminary weather model is presented for optical propagation through the atmosphere. It can be used to compute the attenuation loss due to the atmosphere for desired link availability statistics. The quantitative results that can be obtained from this model provide good estimates for the atmospheric link budget necessary for the design of an optical communication system. The result is extended to provide for the computation of joint attenuation probability for n sites with uncorrelated weather patterns.
Preliminary report on electromagnetic model studies
Frischknecht, F.C.; Mangan, G.B.
1960-01-01
More than 70 resopnse curves for various models have been obtained using the slingram and turam electromagnetic methods. Results show that for the slingram method, horizontal co-planar coils are usually more sensitive than vertical, co-axial or vertical, co-planar coils. The shape of the anomaly usually is simpler for the vertical coils.
A preliminary deposit model for lithium brines
Bradley, Dwight; Munk, LeeAnn; Jochens, Hillary; Hynek, Scott; Labay, Keith A.
2013-01-01
This report is part of an effort by the U.S. Geological Survey to update existing mineral deposit models and to develop new ones. The global transition away from hydrocarbons toward energy alternatives increases demand for many scarce metals. Among these is lithium, a key component of lithium-ion batteries for electric and hybrid vehicles. Lithium brine deposits account for about three-fourths of the world’s lithium production. Updating an earlier deposit model, we emphasize geologic information that might directly or indirectly help in exploration for lithium brine deposits, or for assessing regions for mineral resource potential. Special attention is given to the best-known deposit in the world—Clayton Valley, Nevada, and to the giant Salar de Atacama, Chile.
Preliminary Model of Acute Mountain Sickness Severity
2010-10-01
variance, the Akaike information criterion (AIC) and Bayesian information criterion (BIC) were utilized in selecting the final model using the... information and completed an Environmental Symptoms Questionnaire (ESQ). The ESQ assessed AMS severity using the validated AMS-Cerebral (AMS-C) factor...reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching
NASA Astrophysics Data System (ADS)
Shreve, C. M.; Okin, G. S.; Bowles, J.; Gardner, J.
2007-12-01
Political tensions, rough terrain, and remoteness have lead to a gap in the ecological understanding cold, mountainous deserts of Asia. Remote sensing is a time- and cost-efficient way to understand the spatial distribution and temporal dynamics of plant and snow cover in these regions. Here, a unique high-resolution hyperspectral dataset from Afghanistan is employed to classify ground cover at high resolution. The hyperspectral data was taken using a CASI-1500 Visible Near InfraRed (VNIR) spectrometer. The instrument was run in a mode with 1518 crosstrack pixels and 72 spectral bands between 380 and 1050 nm. The GSD was controlled by the altitude above ground level and aircraft speed, which varied resulting in GSD between 4 and 6 meters. Geolocation was provided by a CMIGITS II and the resulting accuracy will be better than 40 m. Atmospheric conditions were challenging and proper atmospheric compensation of the data remains a challenge. A bottom- up geostatistical approach for quantifying the coverage of vegetation and snow will be applied to establish the practical limits of coarse resolution MODIS data for classifying vegetation and snow cover, a scale suitable for monitoring large regions and for modeling. A Multiple Endmember Linear Spectral Mixture Algorithm (MESMA) will be applied to classify land cover. Semivariograms at the multispectral (30 m) and coarse resolution scale (1 km) will be compared with simulated variograms using hysperspectral data. Patches of vegetation and snow cover used for spatial comparison will be identified in the image and characterized using object-oriented image analysis software. The relative amount of cover will be determined using block-kriging and compared between scenes with statistical tests. Insight gained from this analysis can be applied to improve existing data products and can be applied for carbon budget and climate change models.
Tidal Response of Preliminary Jupiter Model
NASA Astrophysics Data System (ADS)
Wahl, Sean M.; Hubbard, William B.; Militzer, Burkhard
2016-11-01
In anticipation of improved observational data for Jupiter’s gravitational field, from the Juno spacecraft, we predict the static tidal response for a variety of Jupiter interior models based on ab initio computer simulations of hydrogen-helium mixtures. We calculate hydrostatic-equilibrium gravity terms, using the non-perturbative concentric Maclaurin Spheroid method that eliminates lengthy expansions used in the theory of figures. Our method captures terms arising from the coupled tidal and rotational perturbations, which we find to be important for a rapidly rotating planet like Jupiter. Our predicted static tidal Love number, {k}2=0.5900, is ˜10% larger than previous estimates. The value is, as expected, highly correlated with the zonal harmonic coefficient J 2, and is thus nearly constant when plausible changes are made to the interior structure while holding J 2 fixed at the observed value. We note that the predicted static k 2 might change, due to Jupiter’s dynamical response to the Galilean moons, and find reasons to argue that the change may be detectable—although we do not present here a theory of dynamical tides for highly oblate Jovian planets. An accurate model of Jupiter’s tidal response will be essential for interpreting Juno observations and identifying tidal signals from effects of other interior dynamics of Jupiter’s gravitational field.
Preliminary Multi-Variable Cost Model for Space Telescopes
NASA Technical Reports Server (NTRS)
Stahl, H. Philip; Hendrichs, Todd
2010-01-01
Parametric cost models are routinely used to plan missions, compare concepts and justify technology investments. This paper reviews the methodology used to develop space telescope cost models; summarizes recently published single variable models; and presents preliminary results for two and three variable cost models. Some of the findings are that increasing mass reduces cost; it costs less per square meter of collecting aperture to build a large telescope than a small telescope; and technology development as a function of time reduces cost at the rate of 50% per 17 years.
Modelling Extortion Racket Systems: Preliminary Results
NASA Astrophysics Data System (ADS)
Nardin, Luis G.; Andrighetto, Giulia; Székely, Áron; Conte, Rosaria
Mafias are highly powerful and deeply entrenched organised criminal groups that cause both economic and social damage. Overcoming, or at least limiting, their harmful effects is a societally beneficial objective, which renders its dynamics understanding an objective of both scientific and political interests. We propose an agent-based simulation model aimed at understanding how independent and combined effects of legal and social norm-based processes help to counter mafias. Our results show that legal processes are effective in directly countering mafias by reducing their activities and changing the behaviour of the rest of population, yet they are not able to change people's mind-set that renders the change fragile. When combined with social norm-based processes, however, people's mind-set shifts towards a culture of legality rendering the observed behaviour resilient to change.
NASA Astrophysics Data System (ADS)
Laine, Eevaliisa
2015-04-01
The Outokumpu mining district - a metallogenic province about 100 km long x 60 km wide - hosts a Palaeoproterozoic sulfide deposit characterized by an unusual lithological association. It is located in the North Karelia Schist Belt , which was thrust on the late Archaean gneissic-granitoid basement of the Karelian craton during the early stages of the Svecofennian Orogeny between 1.92 and 1.87 Ga (Koistinen 1981). Two major tectono-stratigraphic units can be distinguished, a lower, parautochthonous 'Lower Kaleva' unit and an upper, allochthonous 'upper Kaleva' unit or 'Outokumpu allochthon'. The latter consists of tightly-folded deep marine turbiditic mica schists and metagraywackes containing intercalations of black schist, and the Outo¬kumpu assemblage, which comprises ca. 1950 Ma old, serpentinized peridotites surrounded by carbonate-calc-silicate ('skarn')-quartz rocks. The ore body is enclosed in the Outokumpu assemblage, which is thought to be part of a disrupted and incomplete ophiolite complex (Vuollo & Piirainen 1989) that can be traced to the Kainuu schist belt further north where the well-preserved Jormua ophiolite is ex¬posed (Kontinen 1987, Peltonen & Kontinen 2004). Outokumpu can be divided into blocks divided by faults and shear zones (Saalmann and Laine, 2014). The aim of this study was to make a 3D lithological model of a small part of the Outokumpu association rocks in the Vuonos area honoring the 3D fault model built by Saalmann and Laine (2014). The Vuonos study area is also a part of the Outokumpu mining camp area (Aatos et al. 2013, 2014). Fault and shear structures was used in geostatistical gridding and simulation of the lithologies. Several possible realizations of the structural grids, conforming the main lithological trends were built. Accordingly, it was possible to build a 3D structural grid containing information of the distribution of the possible lithologies and an estimation the associated uncertainties. References: Aatos, S
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
Regional flow duration curves: Geostatistical techniques versus multivariate regression
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.
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.
Modeling the complete Otto cycle: Preliminary version. [computer programming
NASA Technical Reports Server (NTRS)
Zeleznik, F. J.; Mcbride, B. J.
1977-01-01
A description is given of the equations and the computer program being developed to model the complete Otto cycle. The program incorporates such important features as: (1) heat transfer, (2) finite combustion rates, (3) complete chemical kinetics in the burned gas, (4) exhaust gas recirculation, and (5) manifold vacuum or supercharging. Changes in thermodynamic, kinetic and transport data as well as model parameters can be made without reprogramming. Preliminary calculations indicate that: (1) chemistry and heat transfer significantly affect composition and performance, (2) there seems to be a strong interaction among model parameters, and (3) a number of cycles must be calculated in order to obtain steady-state conditions.
Modeling the connection between development and evolution: Preliminary report
Mjolsness, E.; Reinitz, J.; Garrett, C.D.; Sharp, D.H.
1993-07-29
In this paper we outline a model which incorporates development processes into an evolutionary frame work. The model consists of three sectors describing development, genetics, and the selective environment. The formulation of models governing each sector uses dynamical grammars to describe processes in which state variables evolve in a quantitative fashion, and the number and type of participating biological entities can change. This program has previously been elaborated for development. Its extension to the other sectors of the model is discussed here and forms the basis for further approximations. A specific implementation of these ideas is described for an idealized model of the evolution of a multicellular organism. While this model doe not describe an actual biological system, it illustrates the interplay of development and evolution. Preliminary results of numerical simulations of this idealized model are presented.
Reducing spatial uncertainty in climatic maps through geostatistical analysis
NASA Astrophysics Data System (ADS)
Pesquer, Lluís; Ninyerola, Miquel; Pons, Xavier
2014-05-01
Climatic maps from meteorological stations and geographical co-variables can be obtained through correlative models (Ninyerola et al., 2000)*. Nevertheless, the spatial uncertainty of the resulting maps could be reduced. The present work is a new stage over those approaches aiming to study how to obtain better results while characterizing spatial uncertainty. The study area is Catalonia (32000 km2), a region with highly variable relief (0 to 3143 m). We have used 217 stations (321 to 1244 mm) to model the annual precipitation in two steps: 1/ multiple regression using geographical variables (elevation, distance to the coast, latitude, etc) and 2/ refinement of the results by adding the spatial interpolation of the regression residuals with inverse distance weighting (IDW), regularized splines with tension (SPT) or ordinary kriging (OK). Spatial uncertainty analysis is based on an independent subsample (test set), randomly selected in previous works. The main contribution of this work is the analysis of this test set as well as the search for an optimal process of division (split) of the stations in two sets, one used to perform the multiple regression and residuals interpolation (fit set), and another used to compute the quality (test set); optimal division should reduce spatial uncertainty and improve the overall quality. Two methods have been evaluated against classical methods: (random selection RS and leave-one-out cross-validation LOOCV): selection by Euclidian 2D-distance, and selection by anisotropic 2D-distance combined with a 3D-contribution (suitable weighted) from the most representative independent variable. Both methods define a minimum threshold distance, obtained by variogram analysis, between samples. Main preliminary results for LOOCV, RS (average from 10 executions), Euclidian criterion (EU), and for anisotropic criterion (with 1.1 value, UTMY coordinate has a bit more weight than UTMX) combined with 3D criteria (A3D) (1000 factor for elevation
Performance prediction using geostatistics and window reservoir simulation
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.
The use of geostatistics in the study of floral phenology of Vulpia geniculata (L.) link.
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.
The Use of Geostatistics in the Study of Floral Phenology of Vulpia geniculata (L.) Link
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
NASA Astrophysics Data System (ADS)
Greenberg, J. A.
2013-12-01
As geospatial analyses progress in tandem with increasing availability of large complex geographic data sets and high performance computing (HPC), there is an increasing gap in the ability of end-user tools to take advantage of these advances. Specifically, the practical implementation of complex statistical models on large gridded geographic datasets (e.g. remote sensing analysis, species distribution mapping, topographic transformations, and local neighborhood analyses) currently requires a significant knowledge base. A user must be proficient in the chosen model as well as the nuances of scientific programming, raster data models, memory management, parallel computing, and system design. This is further complicated by the fact that many of the cutting-edge analytical tools were developed for non-geospatial datasets and are not part of standard GIS packages, but are available in scientific computing languages such as R and MATLAB. We present a computing function 'rasterEngine' written in the R scientific computing language and part of the CRAN package 'spatial.tools' with these challenges in mind. The goal of rasterEngine is to allow a user to quickly develop and apply analytical models within the R computing environment to arbitrarily large gridded datasets, taking advantage of available parallel computing resources, and without requiring a deep understanding of HPC and raster data models. We provide several examples of rasterEngine being used to solve common grid based analyses, including remote sensing image analyses, topographic transformations, and species distribution modeling. With each example, the parallel processing performance results are presented.
Characterizing discourse deficits following penetrating head injury: a preliminary model.
Coelho, Carl; Lê, Karen; Mozeiko, Jennifer; Hamilton, Mark; Tyler, Elizabeth; Krueger, Frank; Grafman, Jordan
2013-05-01
Discourse analyses have demonstrated utility for delineating subtle communication deficits following closed head injuries (CHIs). The present investigation examined the discourse performance of a large group of individuals with penetrating head injury (PHI). Performance was also compared across 6 subgroups of PHI based on lesion locale. A preliminary model of discourse production following PHI was proposed and tested. Story narratives were elicited from 2 groups of participants, 167 with PHI and 46 non brain-injured (NBI). Micro- and macrostructural components of each story were analyzed. Measures of memory, executive functions, and intelligence were also administered. All measures were compared across groups and PHI subgroups. The proposed model of discourse production was tested with a structural equation modeling procedure. No differences for the discourse measures were noted across the six PHI subgroups. Three measures distinguished the PHI and NBI groups: narrative length, story grammar, and completeness. The proposed model of discourse production had an adequate-to-good fit with the cognitive and discourse data. In spite of differing mechanisms of injury, the PHI group's discourse performance was consistent with what has been reported for individuals with CHI. The model tested represents a preliminary step toward understanding discourse production following traumatic brain injury.
NASA Astrophysics Data System (ADS)
Li, Wang; Niu, Zheng; Gao, Shuai; Wang, Cheng
2014-11-01
Light Detection and Ranging (LiDAR) and Synthetic Aperture Radar (SAR) are two competitive active remote sensing techniques in forest above ground biomass estimation, which is important for forest management and global climate change study. This study aims to further explore their capabilities in temperate forest above ground biomass (AGB) estimation by emphasizing the spatial auto-correlation of variables obtained from these two remote sensing tools, which is a usually overlooked aspect in remote sensing applications to vegetation studies. Remote sensing variables including airborne LiDAR metrics, backscattering coefficient for different SAR polarizations and their ratio variables for Radarsat-2 imagery were calculated. First, simple linear regression models (SLR) was established between the field-estimated above ground biomass and the remote sensing variables. Pearson's correlation coefficient (R2) was used to find which LiDAR metric showed the most significant correlation with the regression residuals and could be selected as co-variable in regression co-kriging (RCoKrig). Second, regression co-kriging was conducted by choosing the regression residuals as dependent variable and the LiDAR metric (Hmean) with highest R2 as co-variable. Third, above ground biomass over the study area was estimated using SLR model and RCoKrig model, respectively. The results for these two models were validated using the same ground points. Results showed that both of these two methods achieved satisfactory prediction accuracy, while regression co-kriging showed the lower estimation error. It is proved that regression co-kriging model is feasible and effective in mapping the spatial pattern of AGB in the temperate forest using Radarsat-2 data calibrated by airborne LiDAR metrics.
Geostatistical analysis as applied to two environmental radiometric time series.
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.
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
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
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 ...
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 ...
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
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.
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
Grisotto, Laura; Consonni, Dario; Cecconi, Lorenzo; Catelan, Dolores; Lagazio, Corrado; Bertazzi, Pier Alberto; Baccini, Michela; Biggeri, Annibale
2016-04-18
In this paper the focus is on environmental statistics, with the aim of estimating the concentration surface and related uncertainty of an air pollutant. We used air quality data recorded by a network of monitoring stations within a Bayesian framework to overcome difficulties in accounting for prediction uncertainty and to integrate information provided by deterministic models based on emissions meteorology and chemico-physical characteristics of the atmosphere. Several authors have proposed such integration, but all the proposed approaches rely on representativeness and completeness of existing air pollution monitoring networks. We considered the situation in which the spatial process of interest and the sampling locations are not independent. This is known in the literature as the preferential sampling problem, which if ignored in the analysis, can bias geostatistical inferences. We developed a Bayesian geostatistical model to account for preferential sampling with the main interest in statistical integration and uncertainty. We used PM10 data arising from the air quality network of the Environmental Protection Agency of Lombardy Region (Italy) and numerical outputs from the deterministic model. We specified an inhomogeneous Poisson process for the sampling locations intensities and a shared spatial random component model for the dependence between the spatial location of monitors and the pollution surface. We found greater predicted standard deviation differences in areas not properly covered by the air quality network. In conclusion, in this context inferences on prediction uncertainty may be misleading when geostatistical modelling does not take into account preferential sampling.
Mine planning and emission control strategies using geostatistics
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.
Geostatistics, remote sensing and precision farming.
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.
Preliminary evaluation of a lake whitefish (Coregonus clupeaformis) bioenergetics model
Madenjian, Charles P.; Pothoven, Steven A.; Schneeberger, Philip J.; O'Connor, Daniel V.; Brandt, Stephen B.
2005-01-01
We conducted a preliminary evaluation of a lake whitefish (Coregonus clupeaformis) bioenergetics model by applying the model to size-at-age data for lake whitefish from northern Lake Michigan. We then compared estimates of gross growth efficiency (GGE) from our bioenergetis model with previously published estimates of GGE for bloater (C. hoyi) in Lake Michigan and for lake whitefish in Quebec. According to our model, the GGE of Lake Michigan lake whitefish decreased from 0.075 to 0.02 as age increased from 2 to 5 years. In contrast, the GGE of lake whitefish in Quebec inland waters decreased from 0.12 to 0.05 for the same ages. When our swimming-speed submodel was replaced with a submodel that had been used for lake trout (Salvelinus namaycush) in Lake Michigan and an observed predator energy density for Lake Michigan lake whitefish was employed, our model predicted that the GGE of Lake Michigan lake whitefish decreased from 0.12 to 0.04 as age increased from 2 to 5 years.
Preliminary deformation model for National Seismic Hazard map of Indonesia
Meilano, Irwan; Gunawan, Endra; Sarsito, Dina; Prijatna, Kosasih; Abidin, Hasanuddin Z.; Susilo,; Efendi, Joni
2015-04-24
Preliminary deformation model for the Indonesia’s National Seismic Hazard (NSH) map is constructed as the block rotation and strain accumulation function at the elastic half-space. Deformation due to rigid body motion is estimated by rotating six tectonic blocks in Indonesia. The interseismic deformation due to subduction is estimated by assuming coupling on subduction interface while deformation at active fault is calculated by assuming each of the fault‘s segment slips beneath a locking depth or in combination with creeping in a shallower part. This research shows that rigid body motion dominates the deformation pattern with magnitude more than 15 mm/year, except in the narrow area near subduction zones and active faults where significant deformation reach to 25 mm/year.
Preliminary prediction model for the ROTI index at high latitude
NASA Astrophysics Data System (ADS)
Rochel Grimald, Sandrine; Boscher, Daniel; Fabbro, Vincent; Rougerie, Sébastien
2017-04-01
The variation of electron density can be described by the ROTI index (i.e. the Rate of change of Total electron content Index). This index is indicative of the electron density gradients which can be responsible of loss of satellite communications or loss of lock of GNSS system.. At high latitude, the ionosphere is connected to the magnetosphere through the magnetic field lines. When the magnetic activity increases, particles from the magnetosphere are injected in the ionosphere along the magnetic field lines. They disturb the ionospheric layer and are responsible of changes in the ROTI index. In this paper, we will use the NOAA POES satellites data to study the link between the ROTI index value and the particles flux in the inner magnetosphere. Then we will use the results to developp a preliminary ROTI model.
Utility of Social Modeling for Proliferation Assessment - Preliminary Findings
Coles, Garill A.; Gastelum, Zoe N.; Brothers, Alan J.; Thompson, Sandra E.
2009-07-16
Often the methodologies for assessing proliferation risk are focused around the inherent vulnerability of nuclear energy systems and associated safeguards. For example an accepted approach involves ways to measure the intrinsic and extrinsic barriers to potential proliferation. This paper describes preliminary investigation into non-traditional use of social and cultural information to improve proliferation assessment and advance the approach to assessing nuclear material diversion. Proliferation resistance assessment, safeguard assessments and related studies typically create technical information about the vulnerability of a nuclear energy system to diversion of nuclear material. The purpose of this research project is to find ways to integrate social information with technical information by explicitly considering the role of culture, groups and/or individuals to factors that impact the possibility of proliferation. When final, this work is expected to describe and demonstrate the utility of social science modeling in proliferation and proliferation risk assessments.
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
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
Geostatistics for spatial genetic structures: study of wild populations of perennial ryegrass.
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.
Statistical and Geostatistical analysis of interannual rainfall data in the island of Crete
NASA Astrophysics Data System (ADS)
Kotsopoulou, Anastasia; Varouchakis, Emmanouil
2017-04-01
Hydrological modeling requires spatially distributed precipitation data of high accuracy. However, precipitation is usually measured at a limited number of locations. In particular, in areas of complex terrain, where the topography plays a key role in the precipitation process, rainfall stations are usually sparse. Spatial interpolation techniques can be applied both to interpolate rainfall data and to combine them with secondary information that may improve the results. Regression Kriging (RK) is an interpolation methodology that combines a regression approach with a geostatistical approach. RK along with Ordinary Kriging (OK) are applied to represent the rainfall spatial distribution on the island of Crete, Greece. A period of 30 years is examined, and the statistical analysis of rainfall data is performed to identify key hydrological years and to analyze the interannual behavior of the rainfall. Then, geostatistical analysis is conducted to present the average spatial distribution of rainfall on the island of Crete.
Incorporating reservoir heterogeneity with geostatistics to investigate waterflood recoveries
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.
Susitna Hydroelectric Project: terrestrial environmental workshop and preliminary simulation model
Everitt, Robert R.; Sonntag, Nicholas C.; Auble, Gregory T.; Roelle, James E.; Gazey, William
1982-01-01
The technical feasibility, economic viability, and environmental impacts of a hydroelectric development project in the Susitna River Basin are being studied by Acres American, Inc. on behalf of the Alaska Power Authority. As part of these studies, Acres American recently contracted LGL Alaska Research Associates, Inc. to coordinate the terrestrial environmental studies being performed by the Alaska Department of Fish and Game and, as subcontractors to LGL, several University of Alaska research groups. LGL is responsible for further quantifying the potential impacts of the project on terrestrial wildlife and vegetation, and for developing a plan to mitigate adverse impacts on the terrestrial environment. The impact assessment and mitigation plan will be included as part of a license application to the Federal Energy Regulatory Commission (FERC) scheduled for the first quarter of 1983. The quantification of impacts, mitigation planning, and design of future research is being organized using a computer simulation modelling approach. Through a series of workshops attended by researchers, resource managers, and policy-makers, a computer model is being developed and refined for use in the quantification of impacts on terrestrial wildlife and vegetation, and for evaluating different mitigation measures such as habitat enhancement and the designation of replacement lands to be managed by wildlife habitat. This report describes the preliminary model developed at the first workshop held August 23 -27, 1982 in Anchorage.
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.
Martínez-Murillo, J F; Hueso-González, P; Ruiz-Sinoga, J D
2017-10-01
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. Copyright © 2017 Elsevier B.V. All rights reserved.
Preliminary 2D numerical modeling of common granular problems
NASA Astrophysics Data System (ADS)
Wyser, Emmanuel; Jaboyedoff, Michel
2017-04-01
Granular studies received an increasing interest during the last decade. Many scientific investigations were successfully addressed to acknowledge the ubiquitous behavior of granular matter. We investigate liquid impacts onto granular beds, i.e. the influence of the packing and compaction-dilation transition. However, a physically-based model is still lacking to address complex microscopic features of granular bed response during liquid impacts such as compaction-dilation transition or granular bed uplifts (Wyser et al. in review). We present our preliminary 2D numerical modeling based on the Discrete Element Method (DEM) using nonlinear contact force law (the Hertz-Mindlin model) for disk shape particles. The algorithm is written in C programming language. Our 2D model provides an analytical tool to address granular problems such as i) granular collapses and ii) static granular assembliy problems. This provides a validation framework of our numerical approach by comparing our numerical results with previous laboratory experiments or numerical works. Inspired by the work of Warnett et al. (2014) and Staron & Hinch (2005), we studied i) the axisymetric collapse of granular columns. We addressed the scaling between the initial aspect ratio and the final runout distance. Our numerical results are in good aggreement with the previous studies of Warnett et al. (2014) and Staron & Hinch (2005). ii) Reproducing static problems for regular and randomly stacked particles provides a valid comparison to results of Egholm (2007). Vertical and horizontal stresses within the assembly are quite identical to stresses obtained by Egholm (2007), thus demonstating the consistency of our 2D numerical model. Our 2D numerical model is able to reproduce common granular case studies such as granular collapses or static problems. However, a sufficient small timestep should be used to ensure a good numerical consistency, resulting in higher computational time. The latter becomes critical
Sand transport on Mars: Preliminary results from models
NASA Technical Reports Server (NTRS)
Greeley, R.; Anderson, F. S.; Blumberg, D.; Lo, E.; Xu, P.; Pollack, J.
1993-01-01
Most studies of active aeolian processes on Mars have focused on dust, i.e., particles approximately 1 micron in diameter that are transported in suspension by wind. The presence of sand dunes on Mars indicates that larger grains (approximately greater than 60 microns, transported primarily in saltation) are also present. Although indirect evidence suggests that some dunes may be active, definitive evidence is lacking. Nonetheless, numerous studies demonstrate that sand is substantially easier to transport by wind than dust, and it is reasonable to infer that sand transportation in saltation occurs under present Martian conditions. In order to assess potential source regions, transportation pathways, and sites of deposition for sand on Mars, an iterative sand transport algorithm was developed that is based on the Mars General Circulation Model of Pollack et al. The results of the dust transport model are then compared with observed surface features, such as dune field locations observed on images, and surficial deposits as inferred from Viking IRTM observations. Preliminary results suggest that the north polar dune fields in the vicinity of 270 degrees W, 70 degrees N originated from weathered polar layered plains centered at 280 degrees W, 85 degrees N, and that Thaumasia Fossae, southern Hellas Planitia, and the area west of Hellespontus Montes are sand depositional sites. Examples of transportation 'corridors' include a westward pathway in the latitudinal band 35 degrees N to 45 degrees N, and a pathway southward from Solis Planum to Thaumasia Fossae, among others.
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.
Geostatistical mapping of effluent-affected sediment distribution on the Palos Verdes Shelf
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.
Geostatistical mapping of effluent-affected sediment distribution on the Palos Verdes shelf
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.
A geostatistical approach to predicting sulfur content in the Pittsburgh coal bed
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.
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
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.
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.
NASA Astrophysics Data System (ADS)
Razack, Moumtaz; Lasm, Théophile
2006-06-01
This work is aimed at estimating the transmissivity of highly fractured hard rock aquifers using a geostatistical approach. The studied aquifer is formed by the crystalline and metamorphic rocks of the Western Ivory Coast (West Africa), in the Man Danané area. The study area covers 7290 km 2 (90 km×81 km). The fracturing network is dense and well connected, without a marked fracture direction. A data base comprising 118 transmissivity ( T) values and 154 specific capacity ( Q/ s) values was compiled. A significant empirical relationship between T and Q/ s was found, which enabled the transmissivity data to be supplemented. The variographic analysis of the two variables showed that the variograms of T and Q/ s (which are lognormal variables) are much more structured than those of log T and log Q/ s (which are normal variables). This result is contrary to what was previously published and raises the question whether normality is necessary in geostatistical analysis. Several input and geostatistical estimations of the transmissivity were tested using the cross validation procedure: measured transmissivity data; supplemented transmissivity data; kriging; cokriging. The cross validation results showed that the best estimation is provided using the kriging procedure, the transmissivity field represented by the whole data sample (measured+estimated using specific capacity) and the structural model evaluated solely on the measured transmissivity. The geostatistical approach provided in fine a reliable estimation of the transmissivity of the Man Danané aquifer, which will be used as an input in forthcoming modelling.
Preliminary Study of a Model Rotor in Descent
NASA Technical Reports Server (NTRS)
McAlister, K. W.; Tung, C.; Sharpe, D. L.; Huang, S.; Hendley, E. M.
2000-01-01
Within a program designed to develop experimental techniques for measuring the trajectory and structure of vortices trailing from the tips of rotor blades, the present preliminary study focuses on a method for quantifying the trajectory of the trailing vortex during descent flight conditions. This study also presents rotor loads and blade surface pressures for a range of tip-path plane angles and Mach numbers. Blade pressures near the leading edge and along the outer radius are compared with data obtained on the same model rotor, but in open jet facilities. A triangulation procedure based on two directable laser-light sheets, each containing an embedded reference, proved effective in defining the spatial coordinates of the trailing vortex. When interrogating a cross section of the flow that contains several trailing vortices, the greatest clarity was found to result when the flow is uniformly seeded. Surface pressure responses during blade-vortex interactions appeared equally sensitive near the leading edge and along the outer portion of the blade, but diminished rapidly as the distance along the blade chord increased. The pressure response was virtually independent of whether the tip-path plane angle was obtained through shaft tilt or cyclic pitch. Although the shape and frequency of the pressure perturbations on the advancing blade during blade-vortex interaction are similar to those obtained in open-jet facilities, the angle of the tip-path plane may need to be lower than the range covered in this study.
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.
An interactive Bayesian geostatistical inverse protocol for hydraulic tomography
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.
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.
NASA Astrophysics Data System (ADS)
Niazi, A.; Bentley, L. R.; Hayashi, M.
2016-12-01
Geostatistical simulations are used to construct heterogeneous aquifer models. Optimally, such simulations should be conditioned with both lithologic and hydraulic data. We introduce an approach to condition lithologic geostatistical simulations of a paleo-fluvial bedrock aquifer consisting of relatively high permeable sandstone channels embedded in relatively low permeable mudstone using hydraulic data. The hydraulic data consist of two-hour single well pumping tests extracted from the public water well database for a 250-km2 watershed in Alberta, Canada. First, lithologic models of the entire watershed are simulated and conditioned with hard lithological data using transition probability - Markov chain geostatistics (TPROGS). Then, a segment of the simulation around a pumping well is 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 are then adjusted to minimize the difference between simulated and actual pumping test data using the parameter estimation program PEST. If the simulated pumping test data do not adequately match the measured data, the lithologic model is updated by locally deforming the lithology distribution using the probability perturbation method 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 well that has pumping test data. The method creates a local groundwater model that honors both the lithologic model and pumping test data and provides estimates of hydraulic conductivity and specific storage. Eventually, the simulations will be integrated into a watershed-scale groundwater model.
Use of geostatistics in planning optimum drilling program
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.
Spatiotemporal analysis of olive flowering using geostatistical techniques.
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.
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
Geostatistics and GIS: tools for characterizing environmental contamination.
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.
Validation of NDVI/LAI Empirical Model to Force a Pasture Growth Model: Preliminary Results
NASA Astrophysics Data System (ADS)
Alexandre, C.; Lajoie, G.; Tillard, E.; Salgado, P.
2016-08-01
Estimating forage biomass is nowadays essential for a good farm management. To answer to this problem we can use mathematic models that use meteorological, soil, grass data. However, in specifics territories as Reunion Island, they are not efficient. That is why we want to force this kind of model to improve the biomass prediction. The Leaf Area Index (LAI) is one parameter used in growth models. Estimate this parameter from satellite images could a solution to predict better the forage biomass. Preliminary results show a strong relation between LAI and NDVI (Normalized Vegetation Index).
Hydrogen-burn survival: preliminary thermal model and test results
McCulloch, W.H.; Ratzel, A.C.; Kempka, S.N.; Furgal, D.T.; Aragon, J.J.
1982-08-01
This report documents preliminary Hydrogen Burn Survival (HBS) Program experimental and analytical work conducted through February 1982. The effects of hydrogen deflagrations on safety-related equipment in nuclear power plant containment buildings are considered. Preliminary results from hydrogen deflagration experiments in the Sandia Variable Geometry Experimental System (VGES) are presented and analytical predictions for these tests are compared and discussed. Analytical estimates of component thermal responses to hydrogen deflagrations in the upper and lower compartments of an ice condenser, pressurized water reactor are also presented.
Preliminary Pole and Shape Models for Three Near-Earth Asteroids
NASA Astrophysics Data System (ADS)
Warner, Brian D.; Pravec, Petr; Kusnirak, Peter; Benishek, Vladimir, Ferrero, Andrea
2017-07-01
Observations of three near-Earth asteroids (NEAs) were made between 1993 and 2016. The resulting data were used to find preliminary pole and shape models for 1863 Antinous, (5836) 1993 MF, and (154244) 2002 KL6.
In this technical support document (TSD) EPA describes the air quality modeling performed to support the 2015 ozone National Ambient Air Quality Standards (NAAQS) preliminary interstate transport assessment Notice of Data Availability (NODA).
Geostatistical Solutions for Downscaling Remotely Sensed Land Surface Temperature
NASA Astrophysics Data System (ADS)
Wang, Q.; Rodriguez-Galiano, V.; Atkinson, P. M.
2017-09-01
Remotely sensed land surface temperature (LST) downscaling is an important issue in remote sensing. Geostatistical methods have shown their applicability in downscaling multi/hyperspectral images. In this paper, four geostatistical solutions, including regression kriging (RK), downscaling cokriging (DSCK), kriging with external drift (KED) and area-to-point regression kriging (ATPRK), are applied for downscaling remotely sensed LST. Their differences are analyzed theoretically and the performances are compared experimentally using a Landsat 7 ETM+ dataset. They are also compared to the classical TsHARP method.
Geostatistics for environmental and geotechnical applications: A technology transferred
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.
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.
Student Matriculation: A Proposal to Study a Preliminary Model.
ERIC Educational Resources Information Center
Farland, Ronnald W.; Berg, Ernest
Developed for the Board of Governors of the California Community Colleges (CCC) as part of a larger study of academic quality, this report presents a preliminary analysis of student matriculation, a guidance process which brings the student into an agreement with the college for the purpose of achieving the student's educational objectives through…
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
A Nonparametric Geostatistical Method For Estimating Species Importance
Andrew J. Lister; Rachel Riemann; Michael Hoppus
2001-01-01
Parametric statistical methods are not always appropriate for conducting spatial analyses of forest inventory data. Parametric geostatistical methods such as variography and kriging are essentially averaging procedures, and thus can be affected by extreme values. Furthermore, non normal distributions violate the assumptions of analyses in which test statistics are...
A geostatistical and sampling analysis of regraded spoil materials
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.
An application of geostatistics and fractal geometry for reservoir characterization
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.
A geostatistical approach to mapping site response spectral amplifications
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.
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.
Prediction of sedimentary facies of x-oilfield in northwest of China by geostatistical inversion
NASA Astrophysics Data System (ADS)
Lei, Zhao; Ling, Ke; Tingting, He
2017-03-01
In the early stage of oilfield development, there are only a few wells and well spacing can reach several kilometers. for the alluvial fans and other heterogeneous reservoirs, information from wells alone is not sufficient to derive detailed reservoir information. In this paper, the method of calculating sand thickness through geostatistics inversion is studied, and quantitative relationships between each sedimentary micro-facies are analyzed by combining with single well sedimentary facies. Further, the sedimentary facies plane distribution based on seismic inversion is obtained by combining with sedimentary model, providing the geological basis for the next exploration and deployment.
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
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
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.
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
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
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.
Analysis of Large Scale Spatial Variability of Soil Moisture Using a Geostatistical Method
Lakhankar, Tarendra; Jones, Andrew S.; Combs, Cynthia L.; Sengupta, Manajit; Vonder Haar, Thomas H.; Khanbilvardi, Reza
2010-01-01
Spatial and temporal soil moisture dynamics are critically needed to improve the parameterization for hydrological and meteorological modeling processes. This study evaluates the statistical spatial structure of large-scale observed and simulated estimates of soil moisture under pre- and post-precipitation event conditions. This large scale variability is a crucial in calibration and validation of large-scale satellite based data assimilation systems. Spatial analysis using geostatistical approaches was used to validate modeled soil moisture by the Agriculture Meteorological (AGRMET) model using in situ measurements of soil moisture from a state-wide environmental monitoring network (Oklahoma Mesonet). The results show that AGRMET data produces larger spatial decorrelation compared to in situ based soil moisture data. The precipitation storms drive the soil moisture spatial structures at large scale, found smaller decorrelation length after precipitation. This study also evaluates the geostatistical approach for mitigation for quality control issues within in situ soil moisture network to estimates at soil moisture at unsampled stations. PMID:22315576
Geostatistical borehole image-based mapping of karst-carbonate aquifer pores
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.
Geostatistical Borehole Image-Based Mapping of Karst-Carbonate Aquifer Pores.
Sukop, Michael C; Cunningham, Kevin J
2016-03-01
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. © 2015, National Ground Water Association.
Assessment of groundwater pollution in West Delhi, India using geostatistical approach.
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.
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.
NASA Astrophysics Data System (ADS)
Chiessi, Vittorio; D'Orefice, Maurizio; Scarascia Mugnozza, Gabriele; Vitale, Valerio; Cannese, Christian
2010-07-01
This paper describes the results of a rockfall hazard assessment for the village of San Quirico (Abruzzo region, Italy) based on an engineering-geological model. After the collection of geological, geomechanical, and geomorphological data, the rockfall hazard assessment was performed based on two separate approaches: i) simulation of detachment of rock blocks and their downhill movement using a GIS; and ii) application of geostatistical techniques to the analysis of georeferenced observations of previously fallen blocks, in order to assess the probability of arrival of blocks due to potential future collapses. The results show that the trajectographic analysis is significantly influenced by the input parameters, with particular reference to the coefficients of restitution values. In order to solve this problem, the model was calibrated based on repeated field observations. The geostatistical approach is useful because it gives the best estimation of point-source phenomena such as rockfalls; however, the sensitivity of results to basic assumptions, e.g. assessment of variograms and choice of a threshold value, may be problematic. Consequently, interpolations derived from different variograms have been used and compared among them; hence, those showing the lowest errors were adopted. The data sets which were statistically analysed are relevant to both kinetic energy and surveyed rock blocks in the accumulation area. The obtained maps highlight areas susceptible to rock block arrivals, and show that the area accommodating the new settlement of S. Quirico Village has the highest level of hazard according to both probabilistic and deterministic methods.
The Regional Particulate Matter Model. 1. Model description and preliminary results
NASA Astrophysics Data System (ADS)
Binkowski, Francis S.; Shankar, Uma
1995-12-01
The Regional Acid Deposition Model has been modified to create the Regional Particulate Model, a three-dimensional Eulerian model that simulates the chemistry, transport, and dynamics of sulfuric acid aerosol resulting from primary emissions and the gas phase oxidation of sulfur dioxide. The new model uses a bimodal lognormal distribution to represent particles in the submicrometer size range. In addition to including the horizontal and vertical advection and vertical diffusion of the aerosol number concentration and sulfate mass concentration fields, the model now explicitly treats the response of the distribution parameters to particle coagulation within and between the modes, condensation of sulfate vapor onto existing particles, formation of new particles, evaporation and condensation of ambient water vapor in the presence of ammonia, and particle-size-dependent dry deposition. The model has been used to study how the degree of sulfuric acid neutralization by ambient ammonia affects the total aerosol concentrations and particle size distributions over eastern North America. Preliminary results for three representative locations, rural, near-source, and nominal downwind of source, show that the effect is greatest for the rural and smallest for the near-source regions, which corresponds with the largest and smallest values, respectively, of ammonium-to-sulfate molar ratios. The results indicate that the model could provide a tool for investigating the effects of various pollution control strategies, as well as new or alternative formulations of important aerosol processes.
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.
A Preliminary Model of Insider Theft of Intellectual Property
2011-01-01
predispositions and organizational and individual stressors as antecedents of a range of malicious activity. Our past work has involved modeling insider fraud...The personal, situational, and behavioral antecedents identified in the CWB literature are also sup- ported in many models of computer-related malicious... Model – which extends the Entitled Independent Model - has more potential indicators for early warning. Together these two models present the big
Geostatistics for natural resources characterization. Part 1 and part 2
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.
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.
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.
A Preliminary Investigation of a Hyperbolic Model of Attitude Change.
ERIC Educational Resources Information Center
Crano, William D.; Cooper, Ralph E.
A model of attitudes change having the mathematical characteristics of a hyperbolic function, with major parameters determined by source credibility and discrepancy, is proposed and tested against leading established models of attitude change. The proposed hyperbolic model proved to be a more accurate predictive device than either of the two…
NASA Astrophysics Data System (ADS)
Mayer, J. M.; Stead, D.
2017-04-01
With the increased drive towards deeper and more complex mine designs, geotechnical engineers are often forced to reconsider traditional deterministic design techniques in favour of probabilistic methods. These alternative techniques allow for the direct quantification of uncertainties within a risk and/or decision analysis framework. However, conventional probabilistic practices typically discretize geological materials into discrete, homogeneous domains, with attributes defined by spatially constant random variables, despite the fact that geological media display inherent heterogeneous spatial characteristics. This research directly simulates this phenomenon using a geostatistical approach, known as sequential Gaussian simulation. The method utilizes the variogram which imposes a degree of controlled spatial heterogeneity on the system. Simulations are constrained using data from the Ok Tedi mine site in Papua New Guinea and designed to randomly vary the geological strength index and uniaxial compressive strength using Monte Carlo techniques. Results suggest that conventional probabilistic techniques have a fundamental limitation compared to geostatistical approaches, as they fail to account for the spatial dependencies inherent to geotechnical datasets. This can result in erroneous model predictions, which are overly conservative when compared to the geostatistical results.
Preliminary Modeling and Simulation Study on Olfactory Cell Sensation
Zhou Jun; Chen Peihua; Liu Qingjun; Wang Ping; Yang Wei
2009-05-23
This paper introduced olfactory sensory neuron's whole-cell model with a concrete voltage-gated ionic channels and simulation. Though there are many models in olfactory sensory neuron and olfactory bulb, it remains uncertain how they express the logic of olfactory information processing. In this article, the olfactory neural network model is also introduced. This model specifies the connections among neural ensembles of the olfactory system. The simulation results of the neural network model are consistent with the observed olfactory biological characteristics such as 1/f-type power spectrum and oscillations.
Application of geostatistics to coal-resource characterization and mine planning. Final report
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.
Preliminary Dynamic Modeling of the Hanford Waste Treatment Plant Melter Offgas
Smith, F.G. III
2001-09-21
This report documents preliminary versions of the models that include the components of the offgas systems from the melters through the exhaust stacks and the vessel ventilation systems. The models consider only the two major chemical species in the offgas stream: air and steam or water vapor. Model mass and energy balance calculations are designed to show the dynamic behavior of gas pressure and flow throughout the offgas systems in response to transient driving forces.
A preliminary compressible second-order closure model for high speed flows
NASA Technical Reports Server (NTRS)
Speziale, Charles G.; Sarkar, Sutanu
1989-01-01
A preliminary version of a compressible second-order closure model that was developed in connection with the National Aero-Space Plane Project is presented. The model requires the solution of transport equations for the Favre-averaged Reynolds stress tensor and dissipation rate. Gradient transport hypotheses are used for the Reynolds heat flux, mass flux, and turbulent diffusion terms. Some brief remarks are made about the direction of future research to generalize the model.
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
Environment modelling in near Earth space: Preliminary LDEF results
NASA Technical Reports Server (NTRS)
Coombs, C. R.; Atkinson, D. R.; Wagner, J. D.; Crowell, L. B.; Allbrooks, M.; Watts, A. J.
1992-01-01
Hypervelocity impacts by space debris cause not only local cratering or penetrations, but also cause large areas of damage in coated, painted or laminated surfaces. Features examined in these analyses display interesting morphological characteristics, commonly exhibiting a concentric ringed appearance. Virtually all features greater than 0.2 mm in diameter possess a spall zone in which all of the paint was removed from the aluminum surface. These spall zones vary in size from approximately 2 - 5 crater diameters. The actual craters in the aluminum substrate vary from central pits without raised rims, to morphologies more typical of craters formed in aluminum under hypervelocity laboratory conditions for the larger features. Most features also possess what is referred to as a 'shock zone' as well. These zones vary in size from approximately 1 - 20 crater diameters. In most cases, only the outer-most layer of paint was affected by this impact related phenomenon. Several impacts possess ridge-like structures encircling the area in which this outer-most paint layer was removed. In many ways, such features resemble the lunar impact basins, but on an extremely reduced scale. Overall, there were no noticeable penetrations, bulges or spallation features on the backside of the tray. On Row 12, approximately 85 degrees from the leading edge (RAM direction), there was approximately one impact per 15 cm(exp 2). On the trailing edge, there was approximately one impact per 72 cm(exp 2). Currently, craters on four aluminum experiment trays from Bay E09, directly on the leading edge are being measured and analyzed. Preliminary results have produced more than 2200 craters on approximately 1500 cm(exp 2) - or approximately 1 impact per 0.7 cm(exp 2).
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
A Preliminary Field Test of an Employee Work Passion Model
ERIC Educational Resources Information Center
Zigarmi, Drea; Nimon, Kim; Houson, Dobie; Witt, David; Diehl, Jim
2011-01-01
Four dimensions of a process model for the formulation of employee work passion, derived from Zigarmi, Nimon, Houson, Witt, and Diehl (2009), were tested in a field setting. A total of 447 employees completed questionnaires that assessed the internal elements of the model in a corporate work environment. Data from the measurements of work affect,…
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
A preliminary numerical model of the Geminid meteoroid stream
NASA Astrophysics Data System (ADS)
Ryabova, G. O.
2016-02-01
A pilot numerical model of the Geminid meteoroid stream is presented. This model implies cometary origin of the stream. Ejection of relatively small amount of particles (90 000 test meteoroids with masses 0.02, 0.003 and 0.0003 g) from the asteroid (3200) Phaethon (the parent body) was simulated, and their evolution was followed till the present time. The particles close to the Earth orbit were considered as the `shower'. It was found that the width of the model shower is at least twice less comparatively the real shower. The maximum activity of the model shower is dislocated and occurs about one day late. The most probable reason for both discrepancies is the drastic transformation of the parent body orbit during rapid release of the volatiles in the process of the stream initial formation. The dispersion of the model stream was evaluated in terms of the Southworth-Hawkins D-criterion.
A geostatistical extreme-value framework for fast simulation of natural hazard events.
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.
Wave-current interactions: model development and preliminary results
NASA Astrophysics Data System (ADS)
Mayet, Clement; Lyard, Florent; Ardhuin, Fabrice
2013-04-01
The coastal area concentrates many uses that require integrated management based on diagnostic and predictive tools to understand and anticipate the future of pollution from land or sea, and learn more about natural hazards at sea or activity on the coast. The realistic modelling of coastal hydrodynamics needs to take into account various processes which interact, including tides, surges, and sea state (Wolf [2008]). These processes act at different spatial scales. Unstructured-grid models have shown the ability to satisfy these needs, given that a good mesh resolution criterion is used. We worked on adding a sea state forcing in a hydrodynamic circulation model. The sea state model is the unstructured version of WAVEWATCH III c (Tolman [2008]) (which version is developed at IFREMER, Brest (Ardhuin et al. [2010]) ), and the hydrodynamic model is the 2D barotropic module of the unstructured-grid finite element model T-UGOm (Le Bars et al. [2010]). We chose to use the radiation stress approach (Longuet-Higgins and Stewart [1964]) to represent the effect of surface waves (wind waves and swell) in the barotropic model, as previously done by Mastenbroek et al. [1993]and others. We present here some validation of the model against academic cases : a 2D plane beach (Haas and Warner [2009]) and a simple bathymetric step with analytic solution for waves (Ardhuin et al. [2008]). In a second part we present realistic application in the Ushant Sea during extreme event. References Ardhuin, F., N. Rascle, and K. Belibassakis, Explicit wave-averaged primitive equations using a generalized Lagrangian mean, Ocean Modelling, 20 (1), 35-60, doi:10.1016/j.ocemod.2007.07.001, 2008. Ardhuin, F., et al., Semiempirical Dissipation Source Functions for Ocean Waves. Part I: Definition, Calibration, and Validation, J. Phys. Oceanogr., 40 (9), 1917-1941, doi:10.1175/2010JPO4324.1, 2010. Haas, K. A., and J. C. Warner, Comparing a quasi-3D to a full 3D nearshore circulation model: SHORECIRC and
Preliminary numerical analysis of improved gas chromatograph model
NASA Technical Reports Server (NTRS)
Woodrow, P. T.
1973-01-01
A mathematical model for the gas chromatograph was developed which incorporates the heretofore neglected transport mechanisms of intraparticle diffusion and rates of adsorption. Because a closed-form analytical solution to the model does not appear realizable, techniques for the numerical solution of the model equations are being investigated. Criteria were developed for using a finite terminal boundary condition in place of an infinite boundary condition used in analytical solution techniques. The class of weighted residual methods known as orthogonal collocation is presently being investigated and appears promising.
NASA Astrophysics Data System (ADS)
Zovi, Francesco; Camporese, Matteo; Hendricks Franssen, Harrie-Jan; Huisman, Johan Alexander; Salandin, Paolo
2017-05-01
Alluvial aquifers are often characterized by the presence of braided high-permeable paleo-riverbeds, which constitute an interconnected preferential flow network whose localization is of fundamental importance to predict flow and transport dynamics. Classic geostatistical approaches based on two-point correlation (i.e., the variogram) cannot describe such particular shapes. In contrast, multiple point geostatistics can describe almost any kind of shape using the empirical probability distribution derived from a training image. However, even with a correct training image the exact positions of the channels are uncertain. State information like groundwater levels can constrain the channel positions using inverse modeling or data assimilation, but the method should be able to handle non-Gaussianity of the parameter distribution. Here the normal score ensemble Kalman filter (NS-EnKF) was chosen as the inverse conditioning algorithm to tackle this issue. Multiple point geostatistics and NS-EnKF have already been tested in synthetic examples, but in this study they are used for the first time in a real-world case study. The test site is an alluvial unconfined aquifer in northeastern Italy with an extension of approximately 3 km2. A satellite training image showing the braid shapes of the nearby river and electrical resistivity tomography (ERT) images were used as conditioning data to provide information on channel shape, size, and position. Measured groundwater levels were assimilated with the NS-EnKF to update the spatially distributed groundwater parameters (hydraulic conductivity and storage coefficients). Results from the study show that the inversion based on multiple point geostatistics does not outperform the one with a multiGaussian model and that the information from the ERT images did not improve site characterization. These results were further evaluated with a synthetic study that mimics the experimental site. The synthetic results showed that only for a much
A Preliminary Ship Design Model for Cargo Throughput Optimization
2014-06-01
aspect of this collection of information, including suggestions for reducing this burden, to Washington headquarters Services , Directorate for...payload, and range that would give the optimal rate of cargo delivery , or throughput, in a given scenario. A physics based mathematical model is...give the optimal rate of cargo delivery , or throughput, in a given scenario. A physics based mathematical model is developed to display the inter
RHF RELAP5 model and preliminary loss-of-offsite-power simulation results for LEU conversion
Licht, J. R.; Bergeron, A.; Dionne, B.; Thomas, F.
2014-08-01
The purpose of this document is to describe the current state of the RELAP5 model for the Institut Laue-Langevin High Flux Reactor (RHF) located in Grenoble, France, and provide an update to the key information required to complete, for example, simulations for a loss of offsite power (LOOP) accident. A previous status report identified a list of 22 items to be resolved in order to complete the RELAP5 model. Most of these items have been resolved by ANL and the RHF team. Enough information was available to perform preliminary safety analyses and define the key items that are still required. Section 2 of this document describes the RELAP5 model of RHF. The final part of this section briefly summarizes previous model issues and resolutions. Section 3 of this document describes preliminary LOOP simulations for both HEU and LEU fuel at beginning of cycle conditions.
A preliminary geodetic data model for geographic information systems
NASA Astrophysics Data System (ADS)
Kelly, K. M.
2009-12-01
Our ability to gather and assimilate integrated data collections from multiple disciplines is important for earth system studies. Moreover, geosciences data collection has increased dramatically, with pervasive networks of observational stations on the ground, in the oceans, in the atmosphere and in space. Contemporary geodetic observations from several space and terrestrial technologies contribute to our knowledge of earth system processes and thus are a valuable source of high accuracy information for many global change studies. Assimilation of these geodetic observations and numerical models into models of weather, climate, oceans, hydrology, ice, and solid Earth processes is an important contribution geodesists can make to the earth science community. Clearly, the geodetic observations and models are fundamental to these contributions. ESRI wishes to provide leadership in the geodetic community to collaboratively build an open, freely available content specification that can be used by anyone to structure and manage geodetic data. This Geodetic Data Model will provide important context for all geographic information. The production of a task-specific geodetic data model involves several steps. The goal of the data model is to provide useful data structures and best practices for each step, making it easier for geodesists to organize their data and metadata in a way that will be useful in their data analyses and to their customers. Built on concepts from the successful Arc Marine data model, we introduce common geodetic data types and summarize the main thematic layers of the Geodetic Data Model. These provide a general framework for envisioning the core feature classes required to represent geodetic data in a geographic information system. Like Arc Marine, the framework is generic to allow users to build workflow or product specific geodetic data models tailored to the specific task(s) at hand. This approach allows integration of the data with other existing
The NASA/GISS Mars general circulation model: Preliminary experiments
NASA Technical Reports Server (NTRS)
Allison, Michael; Chandler, M. A.; Delgenio, A. D.; Lacis, A.; Rind, D.; Rossow, W. B.; Travis, L. D.; Zhou, W.
1993-01-01
The NASA/GISS Mars General Circulation Model (GCM) is an adapted version of the GISS Global Climate/Middle Atmosphere Model, specifically developed for the diagnostic validation and objective analysis of measured atmospheric temperatures from the Mars Observer Pressure Modulator Infrared Radiometer (PMIRR) experiment. The GISS Mars GCM has 23 vertical layers extending from the surface to approximately 80 km altitude, representing a vertical resolution of about 0.3 scale heights. The primitive (vertically hydrostatic) equations are solved in finite difference form on the Krakawa B grid, with a horizontal resolution of 8 deg x 10 deg (latitude-longitude). The model includes a diurnal solar cycle, heat transport within a two-layer ground, and a high-order 'slopes-scheme' for the advection of heat in the upper atmosphere. The radiative transfer scheme is based on the correlated k distribution method for the treatment of nongray gaseous absorption thermal emission, and multiple scattering, including options for suspended dust. A special feature of the model of particular importance for Mars is a parameterization of gravity-wave-induced drag incorporating orographic forcing, wind shear, convection, and radiative damping. The implementation of the GISS Mars model includes global maps of topography, roughness, and albedo.
Preliminary results of a three-dimensional radiative transfer model
O`Hirok, W.
1995-09-01
Clouds act as the primary modulator of the Earth`s radiation at the top of the atmosphere, within the atmospheric column, and at the Earth`s surface. They interact with both shortwave and longwave radiation, but it is primarily in the case of shortwave where most of the uncertainty lies because of the difficulties in treating scattered solar radiation. To understand cloud-radiative interactions, radiative transfer models portray clouds as plane-parallel homogeneous entities to ease the computational physics. Unfortunately, clouds are far from being homogeneous, and large differences between measurement and theory point to a stronger need to understand and model cloud macrophysical properties. In an attempt to better comprehend the role of cloud morphology on the 3-dimensional radiation field, a Monte Carlo model has been developed. This model can simulate broadband shortwave radiation fluxes while incorporating all of the major atmospheric constituents. The model is used to investigate the cloud absorption anomaly where cloud absorption measurements exceed theoretical estimates and to examine the efficacy of ERBE measurements and cloud field experiments. 3 figs.
Preliminary Landing Tests of a 1/6-Scale Dynamic Model of a Lunar Excursion Vehicle
NASA Technical Reports Server (NTRS)
1962-01-01
Preliminary Landing Tests of a 1/6-Scale Dynamic Model of a Lunar Excursion Vehicle. The film shows 21 trials made on 8 days of the scale Model 413 lunar landing vehicle. Attitudes tested were a pitch of 0, -15, or 15 degrees and yaw of 0 or 45 degrees. Velocities were vertical 10 and horizontal 10, though two trials were simple vertical drops. [Entire movie available on DVD from CASI as Doc ID 20070030974. Contact help@sti.nasa.gov
Future mission studies: Preliminary comparisons of solar flux models
NASA Technical Reports Server (NTRS)
Ashrafi, S.
1991-01-01
The results of comparisons of the solar flux models are presented. (The wavelength lambda = 10.7 cm radio flux is the best indicator of the strength of the ionizing radiations such as solar ultraviolet and x-ray emissions that directly affect the atmospheric density thereby changing the orbit lifetime of satellites. Thus, accurate forecasting of solar flux F sub 10.7 is crucial for orbit determination of spacecrafts.) The measured solar flux recorded by National Oceanic and Atmospheric Administration (NOAA) is compared against the forecasts made by Schatten, MSFC, and NOAA itself. The possibility of a combined linear, unbiased minimum-variance estimation that properly combines all three models into one that minimizes the variance is also discussed. All the physics inherent in each model are combined. This is considered to be the dead-end statistical approach to solar flux forecasting before any nonlinear chaotic approach.
Preliminary Efforts to Couple TETRAD with Geophysics Models
Shook, G.M.; Renner, J.L.
2002-02-19
The Geothermal Program at the Idaho National Engineering and Environmental Laboratory is enhancing our reservoir simulation capabilities by writing new subroutines with TETRAD that write necessary files for use with SAIC's geophysics models, including DC Resistivity, SP, and microgravity. This is part of long-term efforts to develop reservoir models that take advantage of various observations that are - or can be - made on both existing fields or during exploration efforts. These new routines will be made available to the TETRAD user community in 2002 through the next release of TETRAD 2002.
Preliminary Efforts to Couple TETRAD with Geophysics Models
Shook, George Michael; Renner, Joel Lawrence; Bloomfield, Kevin Kit
2002-01-01
The Geothermal Program at the Idaho National Engineering and Environmental Laboratory is enhancing our reservoir simulation capabilities by writing new subroutines with TETRAD that write necessary files for use with SAIC's geophysics models, including DC Resistivity, SP, and microgravity. This is part of long-term efforts to develop reservoir models that take advantage of various observations that are - or can be - made on both existing fields or during exploration efforts. These new routines will be made available to the TETRAD user community in 2002 through the next release of TETRAD 2002.
MODEL SELECTION FOR GEOSTATISTICAL MODELS. (R829095C001)
The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...
Preliminary Modulus and Breakage Calculations on Cellulose Models
USDA-ARS?s Scientific Manuscript database
The Young’s modulus of polymers can be calculated by stretching molecular models with the computer. The molecule is stretched and the derivative of the changes in stored potential energy for several displacements, divided by the molecular cross-section area, is the stress. The modulus is the slope o...
A preliminary study to Assess Model Uncertainties in Fluid Flows
Marc Oliver Delchini; Jean C. Ragusa
2009-09-01
The goal of this study is to assess the impact of various flow models for a simplified primary coolant loop of a light water nuclear reactor. The various fluid flow models are based on the Euler equations with an additional friction term, gravity term, momentum source, and energy source. The geometric model is purposefully chosen simple and consists of a one-dimensional (1D) loop system in order to focus the study on the validity of various fluid flow approximations. The 1D loop system is represented by a rectangle; the fluid is heated up along one of the vertical legs and cooled down along the opposite leg. A pressurizer and a pump are included in the horizontal legs. The amount of energy transferred and removed from the system is equal in absolute value along the two vertical legs. The various fluid flow approximations are compressible vs. incompressible, and complete momentum equation vs. Darcy’s approximation. The ultimate goal is to compute the fluid flow models’ uncertainties and, if possible, to generate validity ranges for these models when applied to reactor analysis. We also limit this study to single phase flows with low-Mach numbers. As a result, sound waves carry a very small amount of energy in this particular case. A standard finite volume method is used for the spatial discretization of the system.
Preliminary Shuttle Space Suit Shielding Model. Chapter 9
NASA Technical Reports Server (NTRS)
Anderson, Brooke M.; Nealy, J. E.; Qualls, G. D.; Staritz, P. J.; Wilson, J. W.; Kim, M.-H. Y.; Cucinotta, F. A.; Atwell, W.; DeAngelis, G.; Ware, J.; Persans, A. E.
2003-01-01
There are two space suits in current usage within the space program: EMU [2] and Orlan-M Space Suit . The Shuttle space suit components are discussed elsewhere [2,5,6] and serve as a guide to development of the current model. The present model is somewhat simplified in details which are considered to be second order in their effects on exposures. A more systematic approach is ongoing on a part-by-part basis with the most important ones in terms of exposure contributions being addressed first with detailed studies of the relatively thin space suit fabric as the first example . Additional studies to validate the model of the head coverings (bubble, helmet, visors.. .) will be undertaken in the near future. The purpose of this paper is to present the details of the model as it is now and to examine its impact on estimates of astronaut health risks. In this respect, the nonuniform distribution of mass of the space suit provides increased shielding in some directions and some organs. These effects can be most important in terms of health risks and especially critical to evaluation of potential early radiation effects .
Preliminary Exploration of Adaptive State Predictor Based Human Operator Modeling
NASA Technical Reports Server (NTRS)
Trujillo, Anna C.; Gregory, Irene M.
2012-01-01
Control-theoretic modeling of the human operator dynamic behavior in manual control tasks has a long and rich history. In the last two decades, there has been a renewed interest in modeling the human operator. There has also been significant work on techniques used to identify the pilot model of a given structure. The purpose of this research is to attempt to go beyond pilot identification based on collected experimental data and to develop a predictor of pilot behavior. An experiment was conducted to quantify the effects of changing aircraft dynamics on an operator s ability to track a signal in order to eventually model a pilot adapting to changing aircraft dynamics. A gradient descent estimator and a least squares estimator with exponential forgetting used these data to predict pilot stick input. The results indicate that individual pilot characteristics and vehicle dynamics did not affect the accuracy of either estimator method to estimate pilot stick input. These methods also were able to predict pilot stick input during changing aircraft dynamics and they may have the capability to detect a change in a subject due to workload, engagement, etc., or the effects of changes in vehicle dynamics on the pilot.
Measuring Experiential Avoidance: A Preliminary Test of a Working Model
ERIC Educational Resources Information Center
Hayes, Steven C.; Strosahl, Kirk; Wilson, Kelly G.; Bissett, Richard T.; Pistorello, Jacqueline; Toarmino, Dosheen; Polusny, Melissa A.; Dykstra, Thane A.; Batten, Sonja V.; Bergan, John; Stewart, Sherry H.; Zvolensky, Michael J.; Eifert, Georg H.; Bond, Frank W.; Forsyth, John P.; Karekla, Maria; Mccurry, Susan M.
2004-01-01
The present study describes the development of a short, general measure of experiential avoidance, based on a specific theoretical approach to this process. A theoretically driven iterative exploratory analysis using structural equation modeling on data from a clinical sample yielded a single factor comprising 9 items. A fully confirmatory factor…
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
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.
An assessment of gas emanation hazard using a geographic information system and geostatistics.
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.
Hansen, K.M.
1992-10-01
Sequential indicator simulation (SIS) is a geostatistical technique designed to aid in the characterization of uncertainty about the structure or behavior of natural systems. This report discusses a simulation experiment designed to study the quality of uncertainty bounds generated using SIS. The results indicate that, while SIS may produce reasonable uncertainty bounds in many situations, factors like the number and location of available sample data, the quality of variogram models produced by the user, and the characteristics of the geologic region to be modeled, can all have substantial effects on the accuracy and precision of estimated confidence limits. It is recommended that users of SIS conduct validation studies for the technique on their particular regions of interest before accepting the output uncertainty bounds.
Modeling and simulation for space medicine operations: preliminary requirements considered.
Dawson, D L; Billica, R D; McDonald, P V
2001-01-01
The NASA Space Medicine program is now developing plans for more extensive use of high-fidelity medical simulation systems. The use of simulation is seen as means to more effectively use the limited time available for astronaut medical training. Training systems should be adaptable for use in a variety of training environments, including classrooms or laboratories, space vehicle mockups, analog environments, and in microgravity. Modeling and simulation can also provide the space medicine development program a mechanism for evaluation of other medical technologies under operationally realistic conditions. Systems and procedures need preflight verification with ground-based testing. Traditionally, component testing has been accomplished, but practical means for "human in the loop" verification of patient care systems have been lacking. Medical modeling and simulation technology offer potential means to accomplish such validation work. Initial considerations in the development of functional requirements and design standards for simulation systems for space medicine are discussed.
Thermal buoyancy on Venus: Preliminary results of finite element modeling
NASA Technical Reports Server (NTRS)
Burt, J. D.; Head, James W., III
1992-01-01
Enhanced surface temperatures and a thinner lithosphere on Venus relative to Earth have been cited as leading to increased lithospheric buoyancy. This would limit or prevent subduction on Venus and favor the construction of thickened crust through underthrusting. In order to evaluate the conditions distinguishing between underthrusting and subduction, we have modeled the thermal and buoyancy consequences of the subduction end member. This study considers the fate of a slab from the time it starts to subduct, but bypasses the question of subduction initiation. Thermal changes in slabs subducting into a mantle having a range of initial geotherms are used to predict density changes and thus their overall buoyancy. Finite element modeling is then applied in a first approximation of the assessment of the relative rates of subduction as compared to the buoyant rise of the slab through a viscous mantle.
Modeling and simulation for space medicine operations: preliminary requirements considered
NASA Technical Reports Server (NTRS)
Dawson, D. L.; Billica, R. D.; McDonald, P. V.
2001-01-01
The NASA Space Medicine program is now developing plans for more extensive use of high-fidelity medical simulation systems. The use of simulation is seen as means to more effectively use the limited time available for astronaut medical training. Training systems should be adaptable for use in a variety of training environments, including classrooms or laboratories, space vehicle mockups, analog environments, and in microgravity. Modeling and simulation can also provide the space medicine development program a mechanism for evaluation of other medical technologies under operationally realistic conditions. Systems and procedures need preflight verification with ground-based testing. Traditionally, component testing has been accomplished, but practical means for "human in the loop" verification of patient care systems have been lacking. Medical modeling and simulation technology offer potential means to accomplish such validation work. Initial considerations in the development of functional requirements and design standards for simulation systems for space medicine are discussed.
A preliminary model of ion beam neutralization. [in thruster plasmas
NASA Technical Reports Server (NTRS)
Parks, D. E.; Katz, I.
1979-01-01
A theoretical model of neutralized thruster ion beam plasmas has been developed. The basic premise is that the beam forms an electrostatic trap for the neutralizing electrons. A Maxwellian spectrum of electron energies is maintained by collisions between trapped electrons and by collective randomization of velocities of electrons injected from the neutralizer into the surrounding plasma. The theory contains the observed barometric law relationship between electron density and electron temperatures and ion beam spreading in good agreement with measured results.
Preliminary development of the Active Colonoscopy Training Model
Choi, JungHun; Ravindra, Kale; Robert, Randolph; Drozek, David
2011-01-01
Formal colonoscopy training requires a significant amount of time and effort. In particular, it requires actual patients for a realistic learning experience. The quality of colonoscopy training varies, and includes didactic courses and procedures proctored by skilled surgeons. A colonoscopy training model is occasionally used as part of the training method, but the effects are minute due to both the simple and tedious training procedures. To enhance the educational effect of the colonoscopy training model, the Active Colonoscopy Training Model (ACTM) has been developed. ACTM is an interactive colonoscopy training device which can create the environment of a real colonoscopy procedure as closely as possible. It comprises a configurable rubber colon, a human torso, sensors, a display, and the control part. The ACTM provides audio and visual interaction to the trainee by monitoring important factors, such as forces caused by the distal tip and the shaft of the colonoscope and the pressure to open up the lumen and the localization of the distal tip. On the computer screen, the trainee can easily monitor the status of the colonoscopy, which includes the localization of the distal tip, maximum forces, pressure inside the colon, and surgery time. The forces between the rubber colon and the constraints inside the ACTM are measured and the real time display shows the results to the trainee. The pressure sensors will check the pressure at different parts of the colon. The real-time localized distal tip gives the colonoscopy trainee easier and more confident operation without introducing an additional device in the colonoscope. With the current need for colonoscopists and physicians, the ACTM can play an essential role resolving the problems of the current colonoscopy training model, and significantly improve the training quality of the colonoscopy. PMID:22915931
NASA Astrophysics Data System (ADS)
Jamaludin, Amril Hadri; Karim, Nurulzatushima Abdul; Noor, Raja Nor Husna Raja Mohd; Othman, Nurulhidayah; Malik, Sulaiman Abdul
2017-08-01
Construction waste management (CWM) is the practice of minimizing and diverting construction waste, demolition debris, and land-clearing debris from disposal and redirecting recyclable resources back into the construction process. Best practice model means best choice from the collection of other practices that was built for purpose of construction waste management. The practice model can help the contractors in minimizing waste before the construction activities will be started. The importance of minimizing wastage will have direct impact on time, cost and quality of a construction project. This paper is focusing on the preliminary study to determine the factors of waste generation in the construction sites and identify the effectiveness of existing construction waste management practice conducted in Malaysia. The paper will also include the preliminary works of planned research location, data collection method, and analysis to be done by using the Analytical Hierarchy Process (AHP) to help in developing suitable waste management best practice model that can be used in the country.
ON THE GEOSTATISTICAL APPROACH TO THE INVERSE PROBLEM. (R825689C037)
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...
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
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.
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
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.
Spatial continuity measures for probabilistic and deterministic geostatistics
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.
Preliminary Modeling of Accident Tolerant Fuel Concepts under Accident Conditions
Gamble, Kyle A.; Hales, Jason D.
2016-12-01
The catastrophic events that occurred at the Fukushima-Daiichi nuclear power plant in 2011 have led to widespread interest in research of alternative fuels and claddings that are proposed to be accident tolerant. Thus, the United States Department of Energy through its NEAMS (Nuclear Energy Advanced Modeling and Simulation) program has funded an Accident Tolerant Fuel (ATF) High Impact Problem (HIP). The ATF HIP is funded for a three-year period. The purpose of the HIP is to perform research into two potential accident tolerant concepts and provide an in-depth report to the Advanced Fuels Campaign (AFC) describing the behavior of the concepts, both of which are being considered for inclusion in a lead test assembly scheduled for placement into a commercial reactor in 2022. The initial focus of the HIP is on uranium silicide fuel and iron-chromium-aluminum (FeCrAl) alloy cladding. Utilizing the expertise of three national laboratory participants (INL, LANL, and ANL) a comprehensive mulitscale approach to modeling is being used including atomistic modeling, molecular dynamics, rate theory, phase-field, and fuel performance simulations. In this paper, we present simulations of two proposed accident tolerant fuel systems: U3Si2 fuel with Zircaloy-4 cladding, and UO2 fuel with FeCrAl cladding. The simulations investigate the fuel performance response of the proposed ATF systems under Loss of Coolant and Station Blackout conditions using the BISON code. Sensitivity analyses are completed using Sandia National Laboratories’ DAKOTA software to determine which input parameters (e.g., fuel specific heat) have the greatest influence on the output metrics of interest (e.g., fuel centerline temperature). Early results indicate that each concept has significant advantages as well as areas of concern. Further work is required prior to formulating the proposition report for the Advanced Fuels Campaign.
Preliminary Environmental Flow and Transport Modeling at the INEEL
Magnuson, Swen O; Mccarthy, James Michael; Navratil, James Dale
1999-09-01
The Idaho National Engineering and Environmental Laboratory (INEEL) is located in southeastern Idaho in the USA. The primary mission since the laboratory was founded in 1949 has been nuclear reactor research. Fifty-two reactors have been built and operated on the INEEL. Other principal activities at the laboratory have been reprocessing of spent nuclear fuel. Low-level radioactive waste generated on site and mixed and transuranic waste from the Rocky Flats plutonium processing facility in Colorado has been disposed on the INEEL at the Radioactive Waste Management Complex (RWMC). Waste disposal at the RWMC began in 1952 with shallow land burial in pits and trenches. The INEEL was placed on the National Priorities List (NPL) in 1989. The resulting environmental assessments of the potential negative health impacts of disposed waste at the RWMC have required the use of predictive numerical simulations. A petroleum reservoir simulator called TETRAD was modified for use in simulating environmental flow and transport. Use of this code has allowed the complex subsurface stratigraphy to be simulated, including an extensive region of unsaturated fractured basalt. Dual continual simulation approaches have been used to assess combined aqueous- and gaseous-phase transport of volatile organic compounds as well as dissolved-phase transport of radionuclides. Calibration of the simulator to available monitoring data has increased the confidence in the simulator results to the point where the model sensitivities are being used to direct additional characterization efforts. Eventually, as the model calibration improves and confidence in the model predictions increases, the simulator will be used as a decision tool for selecting remedial alternatives for the wastes buried at the RWMC. An overview of the overall program including a summary of laboratory actinide migration studies will be presented.
Preliminary model for human lipoprotein metabolism in hyperlipoproteinemia.
Phair, R D; Hammond, M G; Bowden, J A; Fried, M; Fisher, W R; Berman, M
1975-12-01
A model is proposed for the metabolism of plasma lipoprotein apoproteins based on studies of a hyperlipoproteinemic subject who received 2.5 mCi[3H]leucine intravenously. Measurements included apoprotein specific activities (apo-B and apo-C) of very low density lipoprotein (VLDL) and of three low density lipoprotein (LDL) subspecies, Sf 17 LDL, Sf 10 LDL, and Sf 4 LDL. Activities of plasma albumin were also determined. The data were analyzed using a compartmental model and the SAAM computer program. A chain-like series of compartments were necessary to simulate plasma VLDL kinetics, suggesting a multistep delipidation process. The data are consistent with the notion that VLDL is the dominant LDL precursor. Two modes of conversion from VLDL to LDL are required. After partial delipidation some VLDL is converted to the Sf 17 LDL, while the remainder undergoes further delipidation before being converted to Sf 4 LDL, the major plasma LDL component. Some direct release of LDL into plasma had to be introduced to fit the data, about 24% of total LDL production. The three LDL subspecies follow a precursor-product relationship (Sf 17 leads to Sf 10 leads to Sf 4). The analysis also indicates that in using labeled leucine as a tracer, the slow exchange of leucine with the total body protein pool must be considered in trying to resolve the LDL subsystem and in the estimation of steady-state apoprotein levels. In view of the fact that the proposed model is based predominantly on the data from a single patient, no generalizations can be made about parameter values. The study is most valuable, however, in pointing out metabolic pathways not considered before and in calling attention to variables that must be considered in the design of experiments to study lipoprotein kinetics.
Preliminary Shape and Spin Axis Models for Two Asteroids
NASA Astrophysics Data System (ADS)
Stephens, Robert D.; Warner, Brian D.
2015-04-01
A combination of dense lightcurves obtained by the authors over several apparitions and sparse data was used to model shapes for two asteroids: the Mars-crosser (21028) 1989 TO and Hungaria member (32814) 1990 XZ. For 1989 TO, a reasonably reliable spin axis and period of (86°, 0°, 3.66527 h) was found, although one of (292°, -62°, 3.66527 h) cannot be formally excluded. The solution for 1990 XZ is ambiguous. While two solutions are presented, they are not considered very reliable determinations.
Field observations, preliminary model analysis, and aquifer thermal efficiency
Miller, R.T.; Delin, G.N.
1993-01-01
In the first model, the sensitivity analysis assumed 8 days of injection of 150°C water at 18.9 liters per second (L/s), 8 days of storage, and 8 days of withdrawal of hot water at 18.9 L/s. The analysis indicates that, for practical ranges of hydraulic and thermal properties, the ratio of horizontal to vertical hydraulic conductivity is the least important property and thermal dispersivity is the most important property used to compute temperature and aquifer thermal efficiency
A 3D Geostatistical Mapping Tool
Weiss, W. W.; Stevenson, Graig; Patel, Ketan; Wang, Jun
1999-02-09
This software provides accurate 3D reservoir modeling tools and high quality 3D graphics for PC platforms enabling engineers and geologists to better comprehend reservoirs and consequently improve their decisions. The mapping algorithms are fractals, kriging, sequential guassian simulation, and three nearest neighbor methods.
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
Preliminary Model Results of Beach Profile Dynamics with Stratigraphy
NASA Astrophysics Data System (ADS)
Reniers, A. J.; Koktas, M.; Gallagher, E. L.; Wadman, H. M.; Brodie, K. L.; Johnson, B. D.; McNinch, J.
2014-12-01
The presence of spatial variation in grain size within the surf and swash zone is often ignored in numerical modeling whereas Upon closer inspection, a broad range of grain sizes is visible on a beach. This could potentially lead to a significant mismatch between predictions and observations of profile evolution given the strong sensitivity of sediment transport formulae to the grain size. To explore this in more detail, numerical simulations with XBeach have been performed to simulate the observations of changes in beach profile and stratigraphy within the swash zone at Duck, NC, under a range of wave and tidal conditions (see presentations by Wadman et al., and Gallagher et al. for complementary information on the observations at this conference). The research focus is to establish the morphodynamic response to the sediment dynamics at short and longer time scales in the presence of stratigraphy. A better understanding of the mechanisms and subsequently improved modeling will provide more accurate predictions of the morphodynamic response of the beach during moderate and extreme conditions. It will also help in the interpretation of sediment layering of the beach to relate to past extreme storms on geological time scales.
Preliminary Fracture Model for The SE Geysers Geothermal Reservoir
NASA Astrophysics Data System (ADS)
Furrey, L.; Furrey, L.; Wagoner, J.; Elkibbi, M.; Hutchings, L. J.
2001-12-01
In this study we combine interpretation of steam entry points, seismicity, shear-wave splitting, geology, and rock physics to develop a fracture model for the Southeast Geysers reservoir in an attempt to improve understanding of the permeability and steam flow within the reservoir. The Geysers is a dry steam field located approximately 140 km NNW of San Francisco, in Sonoma and Lake Counties in northern California. We developed this model by utilizing three-dimensional coordinates of wellbores and observations of steam entries encountered during drilling in conjunction with the locations of microearthquakes, the orientations of fractures from shear-wave splitting, geologic interpretation, and the result of rock physics interpretations. We utilize earthVision5.1TM visualization software in analyzing this data. We are interested in analyzing the fault, fractures, or fracture sets that appear to have the major control over fluid flow at reservoir depths. Faults offsetting the reservoir graywacke and felsite are generally identified by indirect methods. Fault detection within the reservoir rocks is difficult because the geology is relatively homogeneous and lacks marker horizons. Most high-angle faults mapped at the surface are truncated above the reservoir by thrust faults, and do not project to zones of high permeability within the reservoir. Thus, we utilize steam entry points along with geological formation topography to assist in the identification of faults at depth.
Preliminary conceptual model for mineral evolution in Yucca Mountain
Duffy, C.J.
1993-12-01
A model is presented for mineral alteration in Yucca Mountain, Nevada, that suggests that the mineral transformations observed there are primarily controlled by the activity of aqueous silica. The rate of these reactions is related to the rate of evolution of the metastable silica polymorphs opal-CT and cristobalite assuming that a{sub SiO{sub 2(aq)}} is fixed at the equilibrium solubility of the most soluble silica polymorph present. The rate equations accurately predict the present depths of disappearance of opal-CT and cristobalite. The rate equations have also been used to predict the extent of future mineral alteration that may result from emplacement of a high-level nuclear waste repository in Yucca Mountain. Relatively small changes in mineralogy are predicted, but these predictions are based on the assumption that emplacement of a repository would not increase the pH of water in Yucca Mountain nor increase its carbonate content. Such changes may significantly increase mineral alteration. Some of the reactions currently occurring in Yucca Mountain consume H{sup +} and CO{sub 3}{sup 2{minus}}. Combining reaction rate models for these reactions with water chemistry data may make it possible to estimate water flux through the basal vitrophyre of the Topopah Spring Member and to help confirm the direction and rate of flow of groundwater in Yucca Mountain.
Preliminary time-phased TWRS process model results
Orme, R.M.
1995-03-24
This report documents the first phase of efforts to model the retrieval and processing of Hanford tank waste within the constraints of an assumed tank farm configuration. This time-phased approach simulates a first try at a retrieval sequence, the batching of waste through retrieval facilities, the batching of retrieved waste through enhanced sludge washing, the batching of liquids through pretreatment and low-level waste (LLW) vitrification, and the batching of pretreated solids through high-level waste (HLW) vitrification. The results reflect the outcome of an assumed retrieval sequence that has not been tailored with respect to accepted measures of performance. The batch data, composition variability, and final waste volume projects in this report should be regarded as tentative. Nevertheless, the results provide interesting insights into time-phased processing of the tank waste. Inspection of the composition variability, for example, suggests modifications to the retrieval sequence that will further improve the uniformity of feed to the vitrification facilities. This model will be a valuable tool for evaluating suggested retrieval sequences and establishing a time-phased processing baseline. An official recommendation on tank retrieval sequence will be made in September, 1995.
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
Preliminary insights into a model for mafic magma fragmentation
NASA Astrophysics Data System (ADS)
Edwards, Matt; Pioli, Laura; Andronico, Daniele; Cristaldi, Antonio; Scollo, Simona
2017-04-01
Fragmentation of mafic magmas remains a poorly understood process despite the common occurrence of low viscosity explosive eruptions. In fact, it has been commonly overlooked based on the assumption that low viscosity magmas have very limited explosivity and low potential to undergo brittle fragmentation. However, it is now known that highly explosive, ash forming eruptions can be relatively frequent at several mafic volcanoes. Three questions arise due to this - What is the specific fragmentation mechanism occuring in these eruptions? What are the primary factors controlling fragmentation efficiency? Can a link between eruption style and fragmentation efficiency be quantified? We addressed these questions by coupling theoretical observations and field analysis of the recent May 2016 eruption at Mount Etna volcano. Within this complex 10-day event three paroxysmal episodes of pulsating basaltic lava jets alternating with small lava flows were recorded from a vent within the Voragine crater. The associated plumes which were produced deposited tephra along narrow axes to the east and south east. Sampling was done on the deposits associated with the first two plumes and the third one. We briefly characterise the May 2016 eruption by assessing plume height, eruption phases, total erupted masses and fallout boundaries and comparing them to previous eruptions. We also analyse the total grainsize distribution (TGSD) of the scoria particles formed in the jets. Conventional methods for obtaining grainsize and total distributions of an eruption are based on mass and provide limited information on fragmentation though. For this reason, the TGSD was assessed by coupling particle analyser data and conventional sieving data to assess both particle size and number of particle distributions with better precision. This allowed for more accurate testing of several existing models describing the shape of the TGSD. Coupled further with observations on eruption dynamics and eruption
Preliminary characterization and modeling of SMA-based textile composites
NASA Astrophysics Data System (ADS)
Masuda, Arata; Ni, Qing-Qing; Sone, Akira; Zhang, Run-Xin; Yamamura, Takahiko
2004-07-01
In this paper, we conduct a feasibility study to investigate the future potential of textile composites with shape memory alloys. Two different types of SMA-based textile composites are presented. First, a composite plate with embedded woven SMA layer is fabricated, and the stiffness tuning capability is evaluated by impact vibration tests. The results are not favorable, but may be improved by increasing the volume fraction of SMA, and by controlling the prestrain more accurately during the lamination process. The modeling and analysis methodology for woven SMA-based composites are briefly discussed. Then, the possibility of textile composites with SMA stitching is discussed, that is expected to give the composites multi-functions such as tunable stiffness, shape control and sensing capability, selectively distributed on demand.
Modeling of enterprise information systems implementation: a preliminary investigation
NASA Astrophysics Data System (ADS)
Yusuf, Yahaya Y.; Abthorpe, M. S.; Gunasekaran, Angappa; Al-Dabass, D.; Onuh, Spencer
2001-10-01
The business enterprise has never been in greater need of Agility and the current trend will continue unabated well into the future. It is now recognized that information system is both the foundation and a necessary condition for increased responsiveness. A successful implementation of Enterprise Resource Planning (ERP) can help a company to move towards delivering on its competitive objectives as it enables suppliers to reach out to customers beyond the borders of traditional market defined by geography. The cost of implementation, even when it is successful, could be significant. Bearing in mind the potential strategic benefits, it is important that the implementation project is managed effectively. To this end a project cost model against which to benchmark ongoing project expenditure versus activities completed has been proposed in this paper.
Preliminary empirical model of inner boundary of ion plasma sheet
NASA Astrophysics Data System (ADS)
Cao, J. B.; Zhang, D.; Reme, H.; Dandouras, I.; Sauvaud, J. A.; Fu, H. S.; Wei, X. H.
2015-09-01
The penetration of the plasma sheet into the inner magnetosphere is important to both ring current formation and spacecraft charging at geosynchronous orbit. This paper, using hot ion data recorded by HIA of TC-1/DSP, establishes an empirical model of the inner boundary of ion plasma sheet (IBIPS) on the near equatorial plane. All IBIPS are located inside geocentric radial distance of 9 RE. We divided local times (LT) into eight local time bins and found that during quiet times (Kp ⩽ 2-), the IBIPS is closest to the Earth on the pre-midnight side (LT = 1930-2130) and farthest on the dawn side (LT = 0430-0730), which differs from previous spiral models. The geocentric radius of IBIPS in each local time bin can be described by a linear fitting function: Rps = A + Bkp · Kp. The changing rate Bkp of the radius of IBIPS relative to Kp index on the midnight side (LT = 2230-0130) and post-night side (LT = 0130-0430) are the two largest (0.66 and 0.67), indicating that the IBIPS on the night side (LT = 2230-0430) moves fastest when Kp changes. Since the IBIPSs in different local times bins have different changing rates, both the size and shape of IBIPS change when Kp varies. The correlation coefficients between the radius of IBIPS and the instantaneous Kp increase with the increase of ΔT (the time difference between IBIPS crossing time and preceding Kp interval), which suggests that with the increase of ΔT, the radius of IBIPS is more and more controlled by instantaneous Kp, and the influence of preceding Kp becomes weaker. The response time of IBIPS to Kp is between 80 and 95 min. When ΔT > 95 min, the correlation coefficient basically keeps unchanged and only has a weak increase, suggesting that the IBIPS is mainly determined by the convection electric field represented by instantaneous Kp.
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.
NASA Astrophysics Data System (ADS)
Korres, Wolfgang; Reichenau, Tim G.; Fiener, Peter; Koyama, Christian N.; Bogena, Heye R.; Cornelissen, Thomas; Baatz, Roland; Herbst, Michael; Diekkrüger, Bernd; Vereecken, Harry; Schneider, Karl
2016-04-01
Soil moisture and its spatio-temporal pattern is a key variable in hydrology, meteorology and agriculture. The aim of the current study is to analyze spatio-temporal soil moisture patterns of 9 datasets from the Rur catchment (Western Germany) with a total area of 2364 km², consisting of a low mountain range (forest and grassland) and a loess plain dominated by arable land. Data was acquired across a variety of land use types, on different spatial scales (plot to mesoscale catchment) and with different methods (field measurements, remote sensing, and modelling). All datasets were analyzed using the same methodology. In a geostatistical analysis sill and range of the theoretical variogram were inferred. Based on this analysis, three groups of datasets with similar characteristics in the autocorrelation structure were identified: (i) modelled and measured datasets from a forest sub-catchment (influenced by soil properties and topography), (ii) remotely sensed datasets from the cropped part of the total catchment (influenced by the land-use structure of the cropped area), and (iii) modelled datasets from the cropped part of the Rur catchment (influenced by large scale variability of soil properties). A fractal analysis revealed that soil moisture patterns of all datasets show a multi-fractal behavior (varying fractal dimensions, patterns are only self-similar over certain ranges of scales), with at least one scale break and generally high fractal dimensions (high spatial variability). Corresponding scale breaks were found in various datasets and the factors explaining these scale breaks are consistent with the findings of the geostatistical analysis. The joined analysis of the different datasets showed that small differences in soil moisture dynamics, especially at maximum porosity and wilting point in the soils, can have a large influence on the soil moisture patterns and their autocorrelation structure.
Magnetic resonance imaging in a spinal abscess model. Preliminary report.
Runge, V M; Williams, N M; Lee, C; Timoney, J F
1998-04-01
Magnetic resonance (MR) scan technique and lesion detectability were evaluated using a newly developed spinal abscess model in the New Zealand White rabbit. To create the lesion, an epidural needle was inserted under fluoroscopic guidance in the lumbar region and advanced to penetrate the ligamentum flavum. Next, polyethylene tubing was fed through the needle into the epidural space. A mixed suspension of Staphylococcus aureus (Cowan I) and blue polystyrene microspheres then was injected. Lesions were evaluated by MR imaging in four animals at multiple time points (3, 6, and 9 days). Imaging was performed at 1.5 tesla using a surface coil. Precontrast T2-and T1-weighted scans were first obtained. The T1-weighted scans were acquired both with and without fat saturation, and were repeated after intravenous contrast administration. The contrast agent used was gadoteridol (gadolinium HP-DO3A or ProHance) at a dose of 0.3 mmol/kg. On prospective film review, postcontrast scans proved superior for lesion detection. A spinal abscess could be identified postcontrast in all cases, irrespective of the use of fat saturation. The next best imaging technique for lesion detection was the T2-weighted scan, with 5 of 8 lesions noted thereon. Visualization of lesion margins proved to be a primary factor in prospective lesion identification. Region of interest image analysis demonstrated the postcontrast scans to be superior to all precontrast scan techniques for conspicuity of the interface between the abscess and the compressed spinal cord, with these results statistically significant. The lesions were characterized histologically by infiltrates of heterophils into the meninges and outer spinal cord with accompanying mild hemorrhage, fibrin exudation, and bacterial colonies. The lesions in three animals were confirmed to be in the epidural space, with the lesion in one animal in the subdural space. The current animal model was developed to study spine infection and, specifically
Preliminary model of an urban floodplain under changing land use
NASA Astrophysics Data System (ADS)
Smith, D. P.; Bedient, P. B.
1981-05-01
The hydrologic response of Brays Bayou, a rapidly developing watershed located in southwest Houston, was modeled using the U.S. Corps of Engineers HEC-1 flood hydrograph package. Six subwatersheds were delineated using existing drainage specifications and topographic maps, while aerial photography revealed the current land-use patterns. Unit responses of the individual subcatchments were generated as a function of various basin parameters and the extent of urbanization. Calibration was achieved using several historical storm events at four separate gauges within Brays watershed. The detention storage capacities in the less developed subwatersheds were simulated by placing a reservoir at the outlet of each subwatershed. The additional storage necessary to reduce the peak discharge downstream to a level below the capacity of the channel was considered to be detention storage. A variety of rainfall patterns with different durations and intensities were used to determine the design detention storage for each subwatershed. Land uses were simulated for present conditions and the predicted ultimate scenario.
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.
NASA Astrophysics Data System (ADS)
Appelhans, Tim; Mwangomo, Ephraim; Otte, Insa; Detsch, Florian; Nauss, Thomas; Hemp, Andreas; Ndyamkama, Jimmy
2015-04-01
This study introduces the set-up and characteristics of a meteorological station network on the southern slopes of Mt. Kilimanjaro, Tanzania. The set-up follows a hierarchical approach covering an elevational as well as a land-use disturbance gradient. The network consists of 52 basic stations measuring ambient air temperature and above ground air humidity and 11 precipitation measurement sites. We provide in depth descriptions of various machine learning and classical geo-statistical methods used to fill observation gaps and extend the spatial coverage of the network to a total of 60 research sites. Performance statistics for these methods indicate that the presented data sets provide reliable measurements of the meteorological reality at Mt. Kilimanjaro. These data provide an excellent basis for ecological studies and are also of great value for regional atmospheric numerical modelling studies for which such comprehensive in-situ validation observations are rare, especially in tropical regions of complex terrain.
Goovaerts, Pierre
2005-01-01
associated uncertainty, while being easier to implement than a full Bayesian model. The availability of a public-domain executable makes the geostatistical analysis of health data, and its comparison to traditional smoothers, more accessible to common users. In future papers this methodology will be generalized to the simulation of the spatial distribution of risk values and the propagation of the uncertainty attached to predicted risks in local cluster analysis. PMID:16354294
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
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
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
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
Geostatistics and cost-effective environmental remediation
Rautman, C.A.
1996-04-12
Numerous sites within the U.S. Department of Energy (DOE) complex have been contaminated with various radioactive and hazardous materials by defense-related activities during the post-World War II era. The perception is that characterization and remediation of these contaminated sites will be too costly using currently available technology. Consequently, the DOE Office of Technology Development has funded development of a number of alternative processes for characterizing and remediating these sites. The former Feed-Materials Processing Center near Fernald, Ohio (USA), was selected for demonstrating several innovative technologies. Contamination at the Fernald site consists principally of particulate uranium and derivative compounds in surficial soil. A field-characterization demonstration program was conducted during the summer of 1994 specifically to demonstrate the relative economic performance of seven proposed advanced-characterization tools for measuring uranium activity of in-situ soils. These innovative measurement technologies are principally radiation detectors of varied designs. Four industry-standard measurement technologies, including conventional, regulatory-agency-accepted soil sampling followed by laboratory geochemical analysis, were also demonstrated during the program for comparative purposes. A risk-based economic-decision model has been used to evaluate the performance of these alternative characterization tools. The decision model computes the dollar value of an objective function for each of the different characterization approaches. The methodology not only can assist site operators to choose among engineering alternatives for site characterization and/or remediation, but also can provide an objective and quantitative basis for decisions with respect to the completeness of site characterization.
Low Order Modeling Tools for Preliminary Pressure Gain Combustion Benefits Analyses
NASA Technical Reports Server (NTRS)
Paxson, Daniel E.
2012-01-01
Pressure gain combustion (PGC) offers the promise of higher thermodynamic cycle efficiency and greater specific power in propulsion and power systems. This presentation describes a model, developed under a cooperative agreement between NASA and AFRL, for preliminarily assessing the performance enhancement and preliminary size requirements of PGC components either as stand-alone thrust producers or coupled with surrounding turbomachinery. The model is implemented in the Numerical Propulsion Simulation System (NPSS) environment allowing various configurations to be examined at numerous operating points. The validated model is simple, yet physics-based. It executes quickly in NPSS, yet produces realistic results.
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.
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.
Spatial analysis of hazardous waste data using geostatistics
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.
Hierarchical Modeling and Robust Synthesis for the Preliminary Design of Large Scale Complex Systems
NASA Technical Reports Server (NTRS)
Koch, Patrick N.
1997-01-01
Large-scale complex systems are characterized by multiple interacting subsystems and the analysis of multiple disciplines. The design and development of such systems inevitably requires the resolution of multiple conflicting objectives. The size of complex systems, however, prohibits the development of comprehensive system models, and thus these systems must be partitioned into their constituent parts. Because simultaneous solution of individual subsystem models is often not manageable iteration is inevitable and often excessive. In this dissertation these issues are addressed through the development of a method for hierarchical robust preliminary design exploration to facilitate concurrent system and subsystem design exploration, for the concurrent generation of robust system and subsystem specifications for the preliminary design of multi-level, multi-objective, large-scale complex systems. This method is developed through the integration and expansion of current design techniques: Hierarchical partitioning and modeling techniques for partitioning large-scale complex systems into more tractable parts, and allowing integration of subproblems for system synthesis; Statistical experimentation and approximation techniques for increasing both the efficiency and the comprehensiveness of preliminary design exploration; and Noise modeling techniques for implementing robust preliminary design when approximate models are employed. Hierarchical partitioning and modeling techniques including intermediate responses, linking variables, and compatibility constraints are incorporated within a hierarchical compromise decision support problem formulation for synthesizing subproblem solutions for a partitioned system. Experimentation and approximation techniques are employed for concurrent investigations and modeling of partitioned subproblems. A modified composite experiment is introduced for fitting better predictive models across the ranges of the factors, and an approach for
Laganà, Pasqualina; Moscato, Umberto; Poscia, Andrea; La Milia, Daniele Ignazio; Boccia, Stefania; Avventuroso, Emanuela; Delia, Santi
2015-01-01
Legionnaires' disease is normally acquired by inhalation of legionellae from a contaminated environmental source. Water systems of large buildings, such as hospitals, are often contaminated with legionellae and therefore represent a potential risk for the hospital population. The aim of this study was to evaluate the potential contamination of Legionella pneumophila (LP) in a large hospital in Italy through georeferential statistical analysis to assess the possible sources of dispersion and, consequently, the risk of exposure for both health care staff and patients. LP serogroups 1 and 2-14 distribution was considered in the wards housed on two consecutive floors of the hospital building. On the basis of information provided by 53 bacteriological analysis, a 'random' grid of points was chosen and spatial geostatistics or FAIk Kriging was applied and compared with the results of classical statistical analysis. Over 50% of the examined samples were positive for Legionella pneumophila. LP 1 was isolated in 69% of samples from the ground floor and in 60% of sample from the first floor; LP 2-14 in 36% of sample from the ground floor and 24% from the first. The iso-estimation maps show clearly the most contaminated pipe and the difference in the diffusion of the different L. pneumophila serogroups. Experimental work has demonstrated that geostatistical methods applied to the microbiological analysis of water matrices allows a better modeling of the phenomenon under study, a greater potential for risk management and a greater choice of methods of prevention and environmental recovery to be put in place with respect to the classical statistical analysis.
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.
Environmental Barrier Coating (EBC) Durability Modeling; An Overview and Preliminary Analysis
NASA Technical Reports Server (NTRS)
Abdul-Aziz, A.; Bhatt, R. T.; Grady, J. E.; Zhu, D.
2012-01-01
A study outlining a fracture mechanics based model that is being developed to investigate crack growth and spallation of environmental barrier coating (EBC) under thermal cycling conditions is presented. A description of the current plan and a model to estimate thermal residual stresses in the coating and preliminary fracture mechanics concepts for studying crack growth in the coating are also discussed. A road map for modeling life and durability of the EBC and the results of FEA model(s) developed for predicting thermal residual stresses and the cracking behavior of the coating are generated and described. Further initial assessment and preliminary results showed that developing a comprehensive EBC life prediction model incorporating EBC cracking, degradation and spalling mechanism under stress and temperature gradients typically seen in turbine components is difficult. This is basically due to mismatch in thermal expansion difference between sub-layers of EBC as well as between EBC and substrate, diffusion of moisture and oxygen though the coating, and densification of the coating during operating conditions as well as due to foreign object damage, the EBC can also crack and spall from the substrate causing oxidation and recession and reducing the design life of the EBC coated substrate.
A preliminary model to avoid the overestimation of sample size in bioequivalence studies.
Ramírez, E; Abraira, V; Guerra, P; Borobia, A M; Duque, B; López, J L; Mosquera, B; Lubomirov, R; Carcas, A J; Frías, J
2013-02-01
Often the only available data in literature for sample size estimations in bioequivalence studies is intersubject variability, which tends to result in overestimation of sample size. In this paper, we proposed a preliminary model of intrasubject variability based on intersubject variability for Cmax and AUC data from randomized, crossovers, bioequivalence (BE) studies. From 93 Cmax and 121 AUC data from test-reference comparisons that fulfilled BE criteria, we calculated intersubject variability for the reference formulation and intrasubject variability from ANOVA. Lineal and exponential models (y=a(1-e-bx)) were fitted weighted by the inverse of the variance, to predict the intrasubject variability based on intersubject variability. To validate the model we calculated the coefficient of cross-validation of data from 30 new BE studies. The models fit very well (R2=0.997 and 0.990 for Cmax and AUC respectively) and the cross-validation correlation were 0.847 for Cmax and 0.572 for AUC. A preliminary model analyses allow us to estimate the intrasubject variability based on intersubject variability for sample size calculation purposes in BE studies. This approximation provides an opportunity for sample size reduction avoiding unnecessary exposure of healthy volunteers. Further modelling studies are desirable to confirm these results especially suggestions of the higher intersubject variability range.
Perry, K.E. Jr.; Buescher, B.J.; Anderson, D.; Epstein, J.S.
1995-09-01
An important aspect of determining the suitability of Yucca Mountain as a possible nuclear waste repository requires understanding the mechanical behavior of jointed rock-masses. To this end we have studied the frictional sliding between simulated rock joints in the laboratory using the technique of phase shifting moire interferometry. The models were made from stacks of Lexan plates and contained a central hole to induce slip between the plates when the models were loaded in compression. These preliminary results confirm the feasibility of the approach and show a clear evolution of slip as function of load.
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
NASA Technical Reports Server (NTRS)
Goldsmith, V.; Morris, W. D.; Byrne, R. J.; Whitlock, C. H.
1974-01-01
A computerized wave climate model is developed that applies linear wave theory and shelf depth information to predict wave behavior as they pass over the continental shelf as well as the resulting wave energy distributions along the coastline. Reviewed are also the geomorphology of the Mid-Atlantic Continental Shelf, wave computations resulting from 122 wave input conditions, and a preliminary analysis of these data.
NASA Astrophysics Data System (ADS)
Hemakumara, GPTS; Rainis, Ruslan
2015-02-01
Living in Low-lying areas is a challenging task, but due to the lack of suitable land at affordable prices, thousands of householders have been establishing their own houses on Low-lying areas. Manipulation and conversion of low lying areas have led to an increase in the frequency and severity of micro disasters because the cumulative effect of these settlements is very high. Therefore, it is needed to examine how individual households have been emerging in Low-lying areas. This process is primarily influenced and controlled by Socio-economic factors. In the field survey conducted for this study, 388 householders were interviewed face to face to obtain the primary data. Collected data were applied to the Multivariate binary logistic Model. The Dependent variable of the model was set as Stable Houses and Non-Stable Houses based on the weighted values that were obtained from the field observations. Independent variables of this study are nine key aspects of the socio-economic conditions in these areas. Units of analysis of the study were taken as individual housing plots in the study area. The particular combination of Socio-Economic factors that exerted influence on each housing plot was measured using predicted probability value of logistic model and linked it with GIS land plot's map. Accuracy of Final Model is 86.9 % and probability level of influencing factors given a clear idea about household distribution and status while providing guidance about how the planning authorities should monitor and manage low lying areas, taking into consideration the present housing condition of these areas.
A Preliminary and Simplified Closed Brayton Cycle Modeling for a Space Reactor Application
Guimaraes, Lamartine Nogueira Frutuoso; Camillo, Giannino Ponchio
2008-01-21
The Nuclear Energy Division (ENU) of the Institute for Advanced Studies (IEAv) has started a preliminary design study for a Closed Brayton Cycle Loop (CBCL) aimed at a space reactor application. The main objectives of the study are: 1) to establish a starting concept for the CBCL components specifications, and 2) to build a demonstrative simulator of CBCL. This preliminary design study is developing the CBCL around the NOELLE 60290 turbo machine. The actual nuclear reactor study is being conducted independently. Because of that, a conventional heat source is being used for the CBCL, in this preliminary design phase. This paper describes the steady state simulator of the CBCL operating with NOELLE 60290 turbo machine. In principle, several gases are being considered as working fluid, as for instance: air, helium, nitrogen, CO{sub 2} and gas mixtures such as helium and xenon. However, for this first application pure helium will be used as working fluid. Simplified models of heat and mass transfer were developed to simulate thermal components. Future efforts will focus on implementing a graphical interface to display the thermal process variables in steady state and to keep track of the modifications being implemented at the NOELLE 60290 turbo machine in order to build the CBCL.
[Spatial distribution of soil animals: a geostatistical approach].
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.
Bayesian geostatistics in health cartography: the perspective of malaria.
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.
Ice Surface Classification using Geo-Statistical Texture-Parameters
NASA Astrophysics Data System (ADS)
Wallin, B. F.; Herzfeld, U. C.
2009-12-01
The morphological features of ice surfaces contain valuable information on the state of morphogenetic, environmental, and dynamic processes that the ice has experienced. To effectively use this information, however, the scale and rapid evolution of earth's cryospheric systems necessitate intermediate processing and abstraction as vital tools. We demonstrate methods for automated detection of ice surface classes using robust geostatistical texture parameters and several clustering/classification techniques. By measuring texture parameters as aggregates of regional spatial point distributions, a great variety of surface types can be characterized and detected, at the expense of computational complexity. Unsupervised clustering algorithms such as OPTICS identify numerically distinct sets of values which then seed segmentation algorithms. The results of the methods applied to several data-sets are presented, including RADARSAT, ATM, and ICESAT observations over both land and sea-ice. The surface roughness characteristics are analyzed at the various scales covered by the data-sets.
Interactive Declustering of Spatial Environmental Data for Geostatistical Analyses
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.
Bayesian geostatistics in health cartography: the perspective of malaria
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
Medical Geography: a Promising Field of Application for Geostatistics.
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.
McPhee, Darcy K.; Chuchel, Bruce A.; Pellerin, Louise
2008-01-01
This report presents audiomagnetotelluric (AMT) data along fourteen profiles in Spring, Delamar, and Dry Lake Valleys, and the corresponding preliminary two-dimensional (2-D) inverse models. The AMT method is a valuable tool for estimating the electrical resistivity of the Earth over depth ranges from a few meters to less than one kilometer, and it is important for revealing subsurface structure and stratigraphy within the Basin and Range province of eastern Nevada, which can be used to define the geohydrologic framework of the region. We collected AMT data by using the Geometrics StrataGem EH4 system. Profiles were 0.7 - 3.2 km in length with station spacing of 50-400 m. Data were recorded in a coordinate system parallel to and perpendicular to the regional geologic-strike direction with Z positive down. We show AMT station locations, sounding curves of apparent resistivity, phase, and coherency, and 2-D models of subsurface resistivity along the profiles. The 2-D inverse models are computed from the transverse electric (TE), transverse magnetic (TM), and TE+TM mode data by using a conjugate gradient, finite-difference method. Preliminary interpretation of the 2-D models defines the structural framework of the basins and the resistivity contrasts between alluvial basin-fill, volcanic units, and carbonate basement rocks.
A Preliminary Data Model for Orbital Flight Dynamics in Shuttle Mission Control
NASA Technical Reports Server (NTRS)
ONeill, John; Shalin, Valerie L.
2000-01-01
The Orbital Flight Dynamics group in Shuttle Mission Control is investigating new user interfaces in a project called RIOTS [RIOTS 2000]. Traditionally, the individual functions of hardware and software guide the design of displays, which results in an aggregated, if not integrated interface. The human work system has then been designed and trained to navigate, operate and integrate the processors and displays. The aim of RIOTS is to reduce the cognitive demands of the flight controllers by redesigning the user interface to support the work of the flight controller. This document supports the RIOTS project by defining a preliminary data model for Orbital Flight Dynamics. Section 2 defines an information-centric perspective. An information-centric approach aims to reduce the cognitive workload of the flight controllers by reducing the need for manual integration of information across processors and displays. Section 3 describes the Orbital Flight Dynamics domain. Section 4 defines the preliminary data model for Orbital Flight Dynamics. Section 5 examines the implications of mapping the data model to Orbital Flight Dynamics current information systems. Two recurring patterns are identified in the Orbital Flight Dynamics work the iteration/rework cycle and the decision-making/information integration/mirroring role relationship. Section 6 identifies new requirements on Orbital Flight Dynamics work and makes recommendations based on changing the information environment, changing the implementation of the data model, and changing the two recurring patterns.
Preliminary assessment of the behavioral activation model in Japanese undergraduate students.
Takagaki, Koki; Okajima, Isa; Kunisato, Yoshihiko; Nakajima, Shun; Kanai, Yoshihiro; Ishikawa, Shin-Ichi; Sakano, Yuji
2013-02-01
Many studies have reported that behavioral activation is an effective intervention for depression. The behavioral activation model is based on several formulations. For example, depressive mood leads to avoidant behaviors, avoidance negatively affects social contacts, decreased socialization lessens opportunities for positive reinforcement, and a decrease in positive reinforcement results in more depressive mood. The purpose of this study was to examine relationships among avoidant behavior, social contact, frequency of positive reinforcement, and depressive mood by using structural equation modeling to assess support for aspects of this behavioral activation model. Participants were 630 Japanese undergraduate students and vocational school students. Results provided preliminary support for the model. Treating both avoidance and activating behavior might contribute to decreased impairment.
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
Preliminary structural sizing of a Mach 3.0 high-speed civil transport model
NASA Technical Reports Server (NTRS)
Blackburn, Charles L.
1992-01-01
An analysis has been performed pertaining to the structural resizing of a candidate Mach 3.0 High Speed Civil Transport (HSCT) conceptual design using a computer program called EZDESIT. EZDESIT is a computer program which integrates the PATRAN finite element modeling program to the COMET finite element analysis program for the purpose of calculating element sizes or cross sectional dimensions. The purpose of the present report is to document the procedure used in accomplishing the preliminary structural sizing and to present the corresponding results.
Formal Modeling and Analysis of a Preliminary Small Aircraft Transportation System (SATS)Concept
NASA Technical Reports Server (NTRS)
Carrreno, Victor A.; Gottliebsen, Hanne; Butler, Ricky; Kalvala, Sara
2004-01-01
New concepts for automating air traffic management functions at small non-towered airports raise serious safety issues associated with the software implementations and their underlying key algorithms. The criticality of such software systems necessitates that strong guarantees of the safety be developed for them. In this paper we present a formal method for modeling and verifying such systems using the PVS theorem proving system. The method is demonstrated on a preliminary concept of operation for the Small Aircraft Transportation System (SATS) project at NASA Langley.
A dispersion model approach to the preliminary design of adsorber beds for trace contaminants
NASA Technical Reports Server (NTRS)
Madey, R.; Czayka, M.; Forsythe, R.; Povlis, J.; Yin, K.
1976-01-01
It is shown that a dispersion model for the transport of a gas through a porous medium can be useful in the preliminary design of adsorber beds for the control of trace contaminants. The transmission function is considered, taking into account the transmission of 102-ppm acetaldehyde in helium flowing at various flow rates through an absorber bed. The experiments were conducted at a temperature of 25.0 C. Attention is given to a representation of the experimental breakthrough curve, the volume adsorption capacity, temperature studies, and correlations.
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.
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.
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.
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
Use of a Transition Probability/Markov Approach to Improve Geostatistical of Facies Architecture
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.
NASA Technical Reports Server (NTRS)
Parker, L. Neergaard; Zank, G. P.
2013-01-01
Successful forecasting of energetic particle events in space weather models require algorithms for correctly predicting the spectrum of ions accelerated from a background population of charged particles. We present preliminary results from a model that diffusively accelerates particles at multiple shocks. Our basic approach is related to box models in which a distribution of particles is diffusively accelerated inside the box while simultaneously experiencing decompression through adiabatic expansion and losses from the convection and diffusion of particles outside the box. We adiabatically decompress the accelerated particle distribution between each shock by either the method explored in Melrose and Pope (1993) and Pope and Melrose (1994) or by the approach set forth in Zank et al. (2000) where we solve the transport equation by a method analogous to operator splitting. The second method incorporates the additional loss terms of convection and diffusion and allows for the use of a variable time between shocks. We use a maximum injection energy (E(sub max)) appropriate for quasi-parallel and quasi-perpendicular shocks and provide a preliminary application of the diffusive acceleration of particles by multiple shocks with frequencies appropriate for solar maximum (i.e., a non-Markovian process).
NASA Technical Reports Server (NTRS)
Reaves, Mercedes C.; Belvin, W. Keith; Bailey, James P.
1992-01-01
Results of two different nonlinear finite element analyses and preliminary test results for the final design of the Controls-Structures Interaction Evolutionary Model are presented. Load-deflection data bases are generalized from analysis and testing of the 16-foot diameter, dish shaped reflector. Natural frequencies and mode shapes are obtained from vibrational analysis. Experimental and analytical results show similar trends; however, future test hardware modifications and finite element model refinement would be necessary to obtain better correlation. The two nonlinear analysis procedures are both adequate techniques for the analysis of prestressed structures with complex geometries.
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.
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.
NASA Astrophysics Data System (ADS)
Rosenzweig, C.
2011-12-01
The Agricultural Model Intercomparison and Improvement Project (AgMIP) is a distributed climate-scenario simulation exercise for historical model intercomparison and future climate change conditions with participation of multiple crop and agricultural trade modeling groups around the world. The goals of AgMIP are to improve substantially the characterization of risk of hunger and world food security due to climate change and to enhance adaptation capacity in both developing and developed countries. Recent progress and the current status of AgMIP will be presented, highlighting three areas of activity: preliminary results from crop pilot studies, outcomes from regional workshops, and emerging scientific challenges. AgMIP crop modeling efforts are being led by pilot studies, which have been established for wheat, maize, rice, and sugarcane. These crop-specific initiatives have proven instrumental in testing and contributing to AgMIP protocols, as well as creating preliminary results for aggregation and input to agricultural trade models. Regional workshops are being held to encourage collaborations and set research activities in motion for key agricultural areas. The first of these workshops was hosted by Embrapa and UNICAMP and held in Campinas, Brazil. Outcomes from this meeting have informed crop modeling research activities within South America, AgMIP protocols, and future regional workshops. Several scientific challenges have emerged and are currently being addressed by AgMIP researchers. Areas of particular interest include geospatial weather generation, ensemble methods for climate scenarios and crop models, spatial aggregation of field-scale yields to regional and global production, and characterization of future changes in climate variability.
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. PMID:26258132
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.
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
Preliminary analysis on hybrid Box-Jenkins - GARCH modeling in forecasting gold price
NASA Astrophysics Data System (ADS)
Yaziz, Siti Roslindar; Azizan, Noor Azlinna; Ahmad, Maizah Hura; Zakaria, Roslinazairimah; Agrawal, Manju; Boland, John
2015-02-01
Gold has been regarded as a valuable precious metal and the most popular commodity as a healthy return investment. Hence, the analysis and prediction of gold price become very significant to investors. This study is a preliminary analysis on gold price and its volatility that focuses on the performance of hybrid Box-Jenkins models together with GARCH in analyzing and forecasting gold price. The Box-Cox formula is used as the data transformation method due to its potential best practice in normalizing data, stabilizing variance and reduces heteroscedasticity using 41-year daily gold price data series starting 2nd January 1973. Our study indicates that the proposed hybrid model ARIMA-GARCH with t-innovation can be a new potential approach in forecasting gold price. This finding proves the strength of GARCH in handling volatility in the gold price as well as overcomes the non-linear limitation in the Box-Jenkins modeling.
Preece, R.J.; Putney, J.M.
1993-07-01
A preliminary assessment of Steam Generator (SG) modelling in the PWR thermal-hydraulic code RELAP5/MOD3 is presented. The study is based on calculations against a series of steady-state commissioning tests carried out on the Wolf Creek PWR over a range of load conditions. Data from the tests are used to assess the modelling of primary to secondary side heat transfer and, in particular, to examine the effect of reverting to the standard form of the Chen heat transfer correlation in place of the modified form applied in RELAP5/MOD2. Comparisons between the two versions of the code are also used to show how the new interphase drag model in RELAP5/MOD3 affects the calculation of SG liquid inventory and the void fraction profile in the riser.
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.
NASA Technical Reports Server (NTRS)
Parker, Linda Neergaard; Zank, Gary P.
2013-01-01
We present preliminary results from a model that diffusively accelerates particles at multiple shocks. Our basic approach is related to box models (Protheroe and Stanev, 1998; Moraal and Axford, 1983; Ball and Kirk, 1992; Drury et al., 1999) in which a distribution of particles is diffusively accelerated inside the box while simultaneously experiencing decompression through adiabatic expansion and losses from the convection and diffusion of particles outside the box (Melrose and Pope, 1993; Zank et al., 2000). We adiabatically decompress the accelerated particle distribution between each shock by either the method explored in Melrose and Pope (1993) and Pope and Melrose (1994) or by the approach set forth in Zank et al. (2000) where we solve the transport equation by a method analogous to operator splitting. The second method incorporates the additional loss terms of convection and diffusion and allows for the use of a variable time between shocks. We use a maximum injection energy (Emax) appropriate for quasi-parallel and quasi-perpendicular shocks (Zank et al., 2000, 2006; Dosch and Shalchi, 2010) and provide a preliminary application of the diffusive acceleration of particles by multiple shocks with frequencies appropriate for solar maximum (i.e., a non-Markovian process).
Geostatistical analysis of morphometric features in the selected parts of the Sudetes (SW Poland)
NASA Astrophysics Data System (ADS)
Pawlowski, Lukasz; Szymanowski, Mariusz; Migon, Piotr
2017-04-01
Recent years have brought rapid development of quantitative techniques that are successfully applied in geomorphology. They open up new interpretation possibilities, even in seemingly very well recognized areas. In particular, we are talking about the geomorphometric and geostatistical techniques whose integration in Geographic Information Systems allows to look at the spatial pattern of landforms and process signatures from a new perspective. The morphology of the Sudetes, as of other mountain ranges in central Europe, is the result of protracted interaction of tectonic and surface processes, passive geological factors such as lithology and structure, and passage of time. This raises the question whether, and to which extent, these different controls and signals have resulted in similarities or differences in the morphometric structure of different parts within the same mountain range. In this paper we assume that geomorphic signals of various origins are expressed by a set of primary and secondary topographic attributes, which can be further analyzed as regional variables and modelled using geostatistical methods. Special attention is paid to variogram modelling. This method allows the identification of the spatial structure of the morphometric characteristics, its spatial scale and direction reflected in quantitative parameters of variograms (model functions, range, sill, nugget, anisotropy). This parameters for various areas are compared to find (dis-)similarities between different parts of the Sudetes. Thus, the main goals of the paper are: 1. To evaluate the usefulness of topographic attributes' variogram modelling for quantification of the spatial morphometric structure of mountain areas, on the example of medium-altitude, non-glaciated mountain terrain. 2. To compare different parts of the Sudetes to find similarities and differences between them and to interpret the findings through the examination of geology and geomorphology of the region. The analysis
Periodicity in spatial data and geostatistical models: autocorrelation between patches
Volker C. Radeloff; Todd F. Miller; Hong S. He; David J. Mladenoff
2000-01-01
Several recent studies in landscape ecology have found periodicity in correlograms or semi-variograms calculated, for instance, from spatial data of soils, forests, or animal populations. Some of the studies interpreted this as an indication of regular or periodic landscape patterns. This interpretation is in disagreement with other studies that doubt whether such...
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.
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.
Preliminary design concepts for command and control modeling using Time Warp/hypercube
NASA Astrophysics Data System (ADS)
Laskowski, S. J.; Nugent, R. O.; Sokol, L. M.
1985-08-01
The objective of this task was to develop and evaluate preliminary design concepts for modeling command and control (C2) on the hypercube parallel processing computer architecture using the associated Time Warp operating system. MITRE performed this task in support of the Army Model Improvement Program (AMIP) Management Office (AMMO) systems research and planning efforts required as part of the development of a new family of Army models. Command and control can be thought of as large complex system of facilities, equipment, communications, procedures, and personnel through which command and control of forces and resources is exercised in performing the missions and functions assigned to them. Modeling C2 provides a means of analyzing the process and the effects of alternative doctrine, tactics, and C2 systems. The size and complexity of the command and control decision process make it difficult to model; simulation is one means of making the modeling problem tractable. The objective of this effort was to develop and evaluate design concepts for modeling command and control on the hypercube parallel processing computer architecture using the associated Time Warp operating system. In particular, the evaluation was to be responsive to two basic questions: Can Time Warp on a hypercub e architecture be used in conjunction with object-oriented techniques to significantly speed up the processing time associated with command and control modeling?
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
The assessment of hydraulic aquifer parameters is important for the evaluation of anthropogenic impacts on groundwater resources. The distribution of these parameters determines flow paths and solute travel times and is therefore critical for the successful design and deployment of remediation schemes at contaminated sites. 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 nonlinear least squares optimization problems, namely objective functions of the form L(Y ) = 1(Y ')TQ -Y1YY ' + 1[F(Y) - z]T Q-z1z [F(Y )- z], 2 2 where Y are the parameters, Y ' their deviations from the a priori estimate, QY Y their covariance matrix, z the measurements, Qzz their covariance matrix and F the forward model mapping parameters to observations. In theory, this objective function may be minimized using standard gradient-based techniques like Gauss-Newton. Due to the typically high number of parameters, however, this is not practical. Let nY be the number of parameters and nz the number of observations. Then QY Y and its inverse are both dense nY ×nY matrices, and the sensitivity matrix Hz := δz/δY is a nz ×nY matrix that has to be assembled using forward or adjoint model runs. Specialized schemes have been developed to reduce the dimensionality of the problem and avoid the high cost of handling products with QY Y -1. This enables efficient inversion in the case of a moderate number of observations as encountered in stationary inversion, where the cost of assembling Hz is in
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.
Guimarães, Ricardo J P S; Freitas, Corina C; Dutra, Luciano V; Felgueiras, Carlos A; Moura, Ana C M; Amaral, Ronaldo S; Drummond, Sandra C; Scholte, Ronaldo G C; Oliveira, Guilherme; Carvalho, Omar S
2009-03-01
Geostatistics is used in this work to make inferences about the presence of the species of Biomphalaria (B. glabrata, B. tenagophila and/or B. straminea), intermediate hosts of Schistosoma mansoni, at the São Francisco River Basin, in Minas Gerais, Brazil. One of these geostatistical procedures, known as indicator kriging, allows the classification of categorical data, in areas where the data are not available, using a punctual sample set. The result is a map of species and risk area definition. More than a single map of the categorical attribute, the procedure also permits the association of uncertainties of the stochastic model, which can be used to qualify the inferences. In order to validate the estimated data of the risk map, a fieldwork in five municipalities was carried out. The obtained results showed that indicator kriging is a rather robust tool since it presented a very good agreement with the field findings. The obtained risk map can be thought as an auxiliary tool to formulate proper public health strategies, and to guide other fieldwork, considering the places with higher occurrence probability of the most important snail species. Also, the risk map will enable better resource distribution and adequate policies for the mollusk control. This methodology will be applied to other river basins to generate a predictive map for Biomphalaria species distribution for the entire state of Minas Gerais.
Smith, F.
2012-08-06
At the request of Savannah River Remediation (SRR), SRNL has analyzed the expected performance obtained from using seven 32 million gallon Saltstone Disposal Units (SDUs) in the Z-Area Saltstone Disposal Facility (SDF) to store future saltstone grout. The analysis was based on preliminary SDU final design specifications. The analysis used PORFLOW modeling to calculate the release of 20 radionuclides from an SDU and transport of the radionuclides and daughters through the vadose zone. Results from this vadose zone analysis were combined with previously calculated releases from existing saltstone vaults and FDCs and a second PORFLOW model run to calculate aquifer transport to assessment points located along a boundary 100 m from the nearest edge of the SDF sources. Peak concentrations within 12 sectors spaced along the 100 m boundary were determined over a period of evaluation extending 20,000 years after SDF closure cap placement. These peak concentrations were provided to SRR to use as input for dose calculations.
Cognitive Work Analysis: Preliminary Data for a Model of Problem Solving Strategies
NASA Astrophysics Data System (ADS)
Rothmayer, Mark; Blue, Jennifer
2007-10-01
Investigations into problem solving strategies are part of the field of physics education research where investigators seek to improve physics instruction by conducting basic research of problem solving abilities among students, differences in knowledge representations between experts and novices, and how to transfer knowledge structures more effectively onto novices. We developed a new conceptual research tool in our laboratory, where we could potentially map the step by step flow of problem solving strategies among experts and novices. This model is derived from the theory of Cognitive Work Analysis, which is grounded in ecological psychology, and as far as we know it has never been applied to a knowledge domain like physics. We collected survey data from 140 undergraduates enrolled in an algebra based introductory physics course at Miami University as part of a larger study aimed to test the validity of the model. Preliminary data will be presented and discussed.
NASA Astrophysics Data System (ADS)
Cocciaro, B.; Faetti, S.; Fronzoni, L.
2017-08-01
As shown in the EPR paper (Einstein, Podolsky e Rosen, 1935), Quantum Mechanics is a non-local Theory. The Bell theorem and the successive experiments ruled out the possibility of explaining quantum correlations using only local hidden variables models. Some authors suggested that quantum correlations could be due to superluminal communications that propagate isotropically with velocity vt > c in a preferred reference frame. For finite values of vt and in some special cases, Quantum Mechanics and superluminal models lead to different predictions. So far, no deviations from the predictions of Quantum Mechanics have been detected and only lower bounds for the superluminal velocities vt have been established. Here we describe a new experiment that increases the maximum detectable superluminal velocities and we give some preliminary results.
Preliminary Results from the CCSM Carbon-Land Model Intercomparison Project (C- LAMP)
NASA Astrophysics Data System (ADS)
Hoffman, F. M.; Fung, I.; Randerson, J.; Thornton, P.; Stöckli, R.; Heinsch, F.; Running, S.; Hibbard, K.; John, J.; Covey, C.; Foley, J.; Post, W. M.; Hargrove, W. W.; Erickson, D. J.; Mahowald, N.
2006-12-01
The Biogeochemistry Working Group for the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM) has initiated an intercomparison of terrestrial biosphere models running within the CCSM framework. Called the CCSM Carbon-Land Model Intercomparison Project (C-LAMP), its purpose is to allow the U.S. scientific community to evaluate the performance of biogeochemical cycling models within CCSM and to identify the most important processes for inclusion in a biosphere model participating in simulations supporting the IPCC Fifth Assessment Report (AR5). Three terrestrial biogeochemistry modules coupled to CCSM---CLM3-CASA', CLM3-CN, and LSX-IBIS---will be evaluated following a set of carefully crafted experiments that build upon the C4MIP Phase 1 protocol. In Experiment 1, the models will be forced with an improved NCAR/NCAR reanalysis data set, while in Experiment 2, the models will be coupled to the Community Atmosphere Model Version 3 (CAM3) with carbon, water, and energy exchanges over the 20th century. In order to quickly verify and validate the performance of these biogeochemistry models against high quality observations, a set of offline runs for Fluxnet tower sites have been performed using observed meteorology. Certain biogeochemical, hydrological, physiological, and radiation fields have been saved hourly for intercomparison across models and with high frequency tower measurements. An analysis of the offline flux tower runs will be presented along with preliminary results from the global experiments run within the CCSM framework. Model results will be made available by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) via the Earth System Grid (ESG), and this presentation will include an invitation for community participation in the analysis and evaluation of the model results. C-LAMP is a subproject of the Computational Climate Science End Station headed by Dr. Warren Washington, using computing resources at the
A preliminary numerical model on the incipient motion conditions of flooded vehicles
NASA Astrophysics Data System (ADS)
Arrighi, Chiara; Castelli, Fabio; Oumeraci, Hocine
2014-05-01
The constant increase of the population living in cities and the concerns related to the climate change make flood risk in urban areas a crucial issue in terms of potential damages, casualties and injuries to the people. Vehicles are generally recognized as one of the most aggravating factors in case of floodings in the urban environment. On one hand they interact with the flow modifying locally flood parameters or causing obstructions, on the other hand they are indirectly responsible of many fatalities. According to many studies, most of deaths for drowning occurs in the cars and this drives the need to better understand the conditions in which the vehicles become unstable and consequently dangerous for people and infrastructures. In the view of better assessing the vulnerability during a flood, a deepening in the knowledge of this phenomenon is required. The approach proposed consist of a 3D numerical model able to clarify the occurrence of these conditions and the interaction between flood and vehicles. The model relies on the experimental data provided in literature. In this preliminary study the traditional approach typically used for the incipient motion threshold conditions of river sediments is adapted for the specific geometry of a vehicle. The dimensional analysis of the existing experimental data, is the preliminary step in order to design numerical simulations. The proposed conceptual model is the basis for the numerical analysis aiming at the generalization of empirical results. The dimensionless graph coming from the experiments shows a significant difference between vehicles with a density lower (empty car) than water and larger (filled by floodwater) than water. These behaviors represent both realistic conditions during a sudden flood. The 3D numerical simulation clarifies flow behavior around the obstacle and allows some hints on new approaches for flood modeling in the urban environment.
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.
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.
Lee, Seung-Jae; Serre, Marc L; van Donkelaar, Aaron; Martin, Randall V; Burnett, Richard T; Jerrett, Michael
2012-12-01
A better understanding of the adverse health effects of chronic exposure to fine particulate matter (PM2.5) requires accurate estimates of PM2.5 variation at fine spatial scales. Remote sensing has emerged as an important means of estimating PM2.5 exposures, but relatively few studies have compared remote-sensing estimates to those derived from monitor-based data. We evaluated and compared the predictive capabilities of remote sensing and geostatistical interpolation. We developed a space-time geostatistical kriging model to predict PM2.5 over the continental United States and compared resulting predictions to estimates derived from satellite retrievals. The kriging estimate was more accurate for locations that were about 100 km from a monitoring station, whereas the remote sensing estimate was more accurate for locations that were > 100 km from a monitoring station. Based on this finding, we developed a hybrid map that combines the kriging and satellite-based PM2.5 estimates. We found that for most of the populated areas of the continental United States, geostatistical interpolation produced more accurate estimates than remote sensing. The differences between the estimates resulting from the two methods, however, were relatively small. In areas with extensive monitoring networks, the interpolation may provide more accurate estimates, but in the many areas of the world without such monitoring, remote sensing can provide useful exposure estimates that perform nearly as well.
ERIC Educational Resources Information Center
Gann, Candace J.; Kunnavatana, S. Shanun
2016-01-01
This preliminary study investigated the use of the Function-Based Intervention Decision Model (Decision Model; Umbreit, Ferro, Liaupsin, & Lane, 2007) to improve teacher treatment integrity for a function-based classroom management plan. The participants were a special education teacher and three elementary-age students receiving special…
ERIC Educational Resources Information Center
Gann, Candace J.; Kunnavatana, S. Shanun
2016-01-01
This preliminary study investigated the use of the Function-Based Intervention Decision Model (Decision Model; Umbreit, Ferro, Liaupsin, & Lane, 2007) to improve teacher treatment integrity for a function-based classroom management plan. The participants were a special education teacher and three elementary-age students receiving special…
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.
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.
Gething, Peter W; Noor, Abdisalan M; Gikandi, Priscilla W; Ogara, Esther A. A; Hay, Simon I; Nixon, Mark S; Snow, Robert W; Atkinson, Peter M
2006-01-01
Background Reliable and timely information on disease-specific treatment burdens within a health system is critical for the planning and monitoring of service provision. Health management information systems (HMIS) exist to address this need at national scales across Africa but are failing to deliver adequate data because of widespread underreporting by health facilities. Faced with this inadequacy, vital public health decisions often rely on crudely adjusted regional and national estimates of treatment burdens. Methods and Findings This study has taken the example of presumed malaria in outpatients within the largely incomplete Kenyan HMIS database and has defined a geostatistical modelling framework that can predict values for all data that are missing through space and time. The resulting complete set can then be used to define treatment burdens for presumed malaria at any level of spatial and temporal aggregation. Validation of the model has shown that these burdens are quantified to an acceptable level of accuracy at the district, provincial, and national scale. Conclusions The modelling framework presented here provides, to our knowledge for the first time, reliable information from imperfect HMIS data to support evidence-based decision-making at national and sub-national levels. PMID:16719557
A geostatistical approach to data harmonization - Application to radioactivity exposure data
NASA Astrophysics Data System (ADS)
Baume, O.; Skøien, J. O.; Heuvelink, G. B. M.; Pebesma, E. J.; Melles, S. J.
2011-06-01
Environmental issues such as air, groundwater pollution and climate change are frequently studied at spatial scales that cross boundaries between political and administrative regions. It is common for different administrations to employ different data collection methods. If these differences are not taken into account in spatial interpolation procedures then biases may appear and cause unrealistic results. The resulting maps may show misleading patterns and lead to wrong interpretations. Also, errors will propagate when these maps are used as input to environmental process models. In this paper we present and apply a geostatistical model that generalizes the universal kriging model such that it can handle heterogeneous data sources. The associated best linear unbiased estimation and prediction (BLUE and BLUP) equations are presented and it is shown that these lead to harmonized maps from which estimated biases are removed. The methodology is illustrated with an example of country bias removal in a radioactivity exposure assessment for four European countries. The application also addresses multicollinearity problems in data harmonization, which arise when both artificial bias factors and natural drifts are present and cannot easily be distinguished. Solutions for handling multicollinearity are suggested and directions for further investigations proposed.
Physical frailty, disability, and dynamics in health perceptions: a preliminary mediation model
Mulasso, Anna; Roppolo, Mattia; Rabaglietti, Emanuela
2016-01-01
Purpose Frailty is a condition characterized by loss of functional reserve and altered homeostatic capacity. The aging process is related with complex indicators of physiological state. This study aims, with a preliminary mediation model, to reveal the possible role of mediator of health perceptions variability in the relationship between frailty and disability. Patients and methods A longitudinal study (100 days) was performed. Data from 92 institutionalized older adults were used in the analysis. Frailty was assessed in baseline using the Italian version of the Survey of Health, Ageing and Retirement in Europe – Frailty Instrument; health perceptions were assessed on a daily basis by three visual analog scale questions; and disability was measured in baseline and post-test using the Katz Activities of Daily Living questionnaire. The product-of-coefficient mediation approach was used to test direct and indirect effects of frailty. Results Results showed that daily variability of health perceptions plays the role of mediator between frailty and disability. In all the steps, statistically significant results were found. Conclusion This preliminary result may indicate that physical frailty increases the variability in health perceptions contributing to disability. PMID:27042027
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
NASA Astrophysics Data System (ADS)
Lynch, B.; Yanites, B.; Shen, H.; Poulsen, C. J.
2016-12-01
The tectonic history and the climate driven erosional processes acting in a region are the primary controls on the evolution of a landscape. Quantifying these controls is essential to our understanding of uplift and erosion histories in mountain ranges. Our understanding of how landscapes respond to tectonic forcings is generally well constrained but the influence of climate on landscape evolution remains unclear. This uncertainty is especially apparent when comparing field experiments; some studies have demonstrated a positive feedback between climate and erosion, while others have not. To complement the field experiments and gain important quantitative insight into what climatic properties influence landscape evolution, we implement a numerical modeling approach. We investigate climate-landscape interactions by coupling a high-resolution climate model, Weather Research and Forecasting Model (WRF), and a landscape evolution model, Landlab. The Andes act as the climatic setting for this study, due to the variation in climate along the length of the orogen, and serve as a natural laboratory to test controls on erosion. Discharge is quantified across the landscape with the WRF Hydro model. Discharge and topography are passed between the models, allowing for a feedback relationship to form between topography and precipitation. We will present our preliminary model runs that result from an asynchronous model coupling approach. These results will allow us to run further experiments to test feedbacks between topography and climate by monitoring topographic metrics and erosion histories. This work provides a necessary next step in landscape evolution modeling by using an actively evolving climate to model real precipitation dynamics. This next step allows for modeling more accurate representations of precipitation through the development of an orogen. This will result in an improved understanding of the co-evolution of climate and topography in these settings.
Not Available
1992-12-29
This volume documents model parameters chosen as of July 1992 that were used by the Performance Assessment Department of Sandia National Laboratories in its 1992 preliminary performance assessment of the Waste Isolation Pilot Plant (WIPP). Ranges and distributions for about 300 modeling parameters in the current secondary data base are presented in tables for the geologic and engineered barriers, global materials (e.g., fluid properties), and agents that act upon the WIPP disposal system such as climate variability and human-intrusion boreholes. The 49 parameters sampled in the 1992 Preliminary Performance Assessment are given special emphasis with tables and graphics that provide insight and sources of data for each parameter.
NASA Astrophysics Data System (ADS)
Naliboff, J. B.; Billen, M. I.
2010-12-01
A characteristic feature of global subduction zones is normal faulting in the outer rise region, which reflects flexure of the downgoing plate in response to the slab pull force. Variations in the patterns of outer rise normal faulting between different subduction zones likely reflects both the magnitude of flexural induced topography and the strength of the downgoing plate. In particular, the rheology of the uppermost oceanic lithosphere is likely to strongly control the faulting patterns, which have been well documented recently in both the Middle and South American trenches. These recent observations of outer rise faulting provide a unique opportunity to test different rheological models of the oceanic lithosphere using geodynamic numerical experiments. Here, we develop a new approach for modeling deformation in the outer rise and trench regions of downgoing slabs, and discuss preliminary 2-D numerical models examining the relationship between faulting patterns and the rheology of the oceanic lithosphere. To model viscous and brittle deformation within the oceanic lithosphere we use the CIG (Computational Infrastructure for Geodynamics) finite element code Gale, which is designed to solve long-term tectonic problems. In order to resolve deformation features on geologically realistic scales (< 1 km), we model only the portion of the subduction system seaward of the trench. Horizontal and vertical stress boundary conditions on the side walls drive subduction and reflect, respectively, the ridge-push and slab-pull plate-driving forces. The initial viscosity structure of the oceanic lithosphere and underlying asthenosphere follow a composite viscosity law that takes into account both Newtonian and non-Newtonian deformation. The viscosity structure is consequently governed primarily by the strain rate and thermal structure, which follows a half-space cooling model. Modification of the viscosity structure and development of discrete shear zones occurs during yielding
Using geostatistical methods to estimate snow water equivalence distribution in a mountain watershed
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.
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.
NASA Astrophysics Data System (ADS)
Kolyaie, S.; Yaghooti, M.; Majidi, G.
2011-12-01
This paper is a part of an ongoing research to examine the capability of geostatistical analysis for mobile networks coverage prediction, simulation and tuning. Mobile network coverage predictions are used to find network coverage gaps and areas with poor serviceability. They are essential data for engineering and management in order to make better decision regarding rollout, planning and optimisation of mobile networks.The objective of this research is to evaluate different interpolation techniques in coverage prediction. In method presented here, raw data collected from drive testing a sample of roads in study area is analysed and various continuous surfaces are created using different interpolation methods. Two general interpolation methods are used in this paper with different variables; first, Inverse Distance Weighting (IDW) with various powers and number of neighbours and second, ordinary kriging with Gaussian, spherical, circular and exponential semivariogram models with different number of neighbours. For the result comparison, we have used check points coming from the same drive test data. Prediction values for check points are extracted from each surface and the differences with actual value are computed. The output of this research helps finding an optimised and accurate model for coverage prediction.
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.
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.
Preliminary design, analysis, and costing of a dynamic scale model of the NASA space station
NASA Technical Reports Server (NTRS)
Gronet, M. J.; Pinson, E. D.; Voqui, H. L.; Crawley, E. F.; Everman, M. R.
1987-01-01
The difficulty of testing the next generation of large flexible space structures on the ground places an emphasis on other means for validating predicted on-orbit dynamic behavior. Scale model technology represents one way of verifying analytical predictions with ground test data. This study investigates the preliminary design, scaling and cost trades for a Space Station dynamic scale model. The scaling of nonlinear joint behavior is studied from theoretical and practical points of view. Suspension system interaction trades are conducted for the ISS Dual Keel Configuration and Build-Up Stages suspended in the proposed NASA/LaRC Large Spacecraft Laboratory. Key issues addressed are scaling laws, replication vs. simulation of components, manufacturing, suspension interactions, joint behavior, damping, articulation capability, and cost. These issues are the subject of parametric trades versus the scale model factor. The results of these detailed analyses are used to recommend scale factors for four different scale model options, each with varying degrees of replication. Potential problems in constructing and testing the scale model are identified, and recommendations for further study are outlined.
NASA Astrophysics Data System (ADS)
Fahrul Hassan, Mohd; Rahman, M. R. A.; Arifin, A. M. T.; Ismail, A. E.; Rasidi Ibrahim, M.; Zulafif Rahim, M.; Fauzi Ahmad, Md
2017-08-01
Product manufactured with short life cycle had only one major issue, it can lead to increasing volume of waste. Day by day, this untreated waste had consumed many landfill spaces, waiting for any possible alternatives. Lack of product recovery knowledge and recyclability features imprinted into product design are one of the main reason behind all this. Sustainable awareness aspect should not just be implied into people’s mind, but also onto product design. This paper presents a preliminary study on Kano model method in the conceptual design activities to improve product lifecycle. Kano model is a survey-type method, used to analyze and distinguished product qualities or features, also how the customers may have perceived them. Three important attributes of Kano model are performance, attractive and must-be. The proposed approach enables better understanding of customer requirements while providing a way for Kano model to be integrated into engineering design to improve product’s end-of-life. Further works will be continued to provide a better lifecycle option (increase percentage of reuse, remanufacture or recycle, whereby decrease percentage of waste) of a product using Kano model approach.
Preliminary subsonic aerodynamic model for simulation studies of the HL-20 lifting body
NASA Technical Reports Server (NTRS)
Jackson, E. Bruce; Cruz, Christopher I.
1992-01-01
A nonlinear, six-degree-of-freedom aerodynamic model for an early version of the HL-20 lifting body is described and compared with wind tunnel data upon which it is based. Polynomial functions describing most of the aerodynamic parameters are given and tables of these functions are presented. Techniques used to arrive at these functions are described. Basic aerodynamic coefficients were modeled as functions of angles of attack and sideslip. Vehicle lateral symmetry was assumed. Compressibility (Mach) effects were ignored. Control-surface effectiveness was assumed to vary linearly with angle of deflection and was assumed to be invariant with the angle of sideslip. Dynamic derivatives were obtained from predictive aerodynamic codes. Landing-gear and ground effects were scaled from Space Shuttle data. The model described is provided to support pilot-in-the-loop simulation studies of the HL-20. By providing the data in tabular format, the model is suitable for the data interpolation architecture of many existing engineering simulation facilities. Because of the preliminary nature of the data, however, this model is not recommended for study of the absolute performance of the HL-20.
Kudo, Takashi; Kushikata, Tetsuya; Kudo, Mihoko; Kudo, Tsuyoshi; Hirota, Kazuyoshi
2010-09-06
Neuropathic pain models are classified as central and peripheral pain models. Although various peripheral neuropathic pain models are established, central pain models are based only on spinal cord injury. DSP-4 is a competitive inhibitor of norepinephrine uptake that selectively degenerates the locus coeruleus (LC)-noradrenergic neurons projection to the cerebral cortex and hippocampus. In the present study, we have teste