Rautman, C.A.
1995-09-01
Two-dimensional, heterogeneous, spatially correlated models of thermal conductivity and bulk density have been created for a representative, east-west cross section of Yucca Mountain, Nevada, using geostatistical simulation. The thermal conductivity models are derived from spatially correlated, surrogate material-property models of porosity, through a multiple linear-regression equation, which expresses thermal conductivity as a function of porosity and initial temperature and saturation. Bulk-density values were obtained through a similar, linear-regression relationship with porosity. The use of a surrogate-property allows the use of spatially much-more-abundant porosity measurements to condition the simulations. Modeling was conducted in stratigraphic coordinates to represent original depositional continuity of material properties and the completed models were transformed to real-world coordinates to capture present-day tectonic tilting and faulting of the material-property units. Spatial correlation lengths required for geostatistical modeling were assumed, but are based on the results of previous transect-sampling and geostatistical-modeling work.
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.
NASA Astrophysics Data System (ADS)
Nowak, W.; de Barros, F. P. J.; Rubin, Y.
2010-03-01
Geostatistical optimal design optimizes subsurface exploration for maximum information toward task-specific prediction goals. Until recently, most geostatistical design studies have assumed that the geostatistical description (i.e., the mean, trends, covariance models and their parameters) is given a priori. This contradicts, as emphasized by Rubin and Dagan (1987a), the fact that only few or even no data at all offer support for such assumptions prior to the bulk of exploration effort. We believe that geostatistical design should (1) avoid unjustified a priori assumptions on the geostatistical description, (2) instead 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 follows these guidelines by considering uncertain covariance model parameters. We transfer this concept from kriging-like applications to geostatistical inverse problems. We also deem it inappropriate to consider parametric uncertainty only within a single covariance model. The Matérn family of covariance functions has an additional shape parameter. 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. This is appealing since it generalizes Bayesian model averaging from a finite number to an infinite number of models. We illustrate how our approach fulfills the above four guidelines in a series of synthetic test cases. The underlying scenarios are to minimize the prediction variance of (1) contaminant concentration or (2) arrival time at an ecologically sensitive location by optimal placement of hydraulic head and log conductivity measurements. Results highlight how both the impact of geostatistical model uncertainty and the sampling network design vary according to the
Factor-based Geostatistics for Groundwater Modeling
NASA Astrophysics Data System (ADS)
Savelyeva, E.; Pavlova, M.
2012-04-01
Analysis of groundwater levels is an important stage preceding modeling the filtration and migration processes in the hydro-geological environment. The boundary conditions are due to a pressure field, which strongly depends on groundwater levels, their spatial and temporal variability. Hydro-physical measurements are usually performed at a set of unhomogeneously spatially distributed wells according to some temporal scheme. Thus, it is an irregular spatio-temporal data set with a whole luggage of problems concerning organization of a spatio-temporal metrics system. These problems also affect modeling of a spatio-temporal correlation structure. There are different ways how to overcome these problems and obtain a reasonable model of spatio-temporal correlation structures. But still all these approaches are limited in future forecasting features. This work proposes an alternative approach - a factor-based space-time geostatistics. This method opens a set of possibilities concerning future modeling: possibility to use additional information to present different future scenario, characterization of uncertainty, probabilistic description of critical events. The basic idea is to replace a system of spatially correlated wells by a set of independent factors compressing data with a possibility of back transformation at the prescribed level of accuracy. Factors can be obtained by principle component analysis, independent sources and artificial neural network with a "bottle-neck". The selection of a method depends on the features of initial data and the process under study. All factors are time series nevertheless how they were obtained. A set of factors contains the main features of the groundwater level patterns. Groundwater levels modeling and forecasting is performed through modeling of these time series. This work considers three different stochastic approaches for modeling and forecasting of time series with hydrological origins: stochastic process with a deterministic
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.
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.
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. PMID:27566774
Distribution of Modelling Spatial Processes Using Geostatistical Analysis
NASA Astrophysics Data System (ADS)
Grynyshyna-Poliuga, Oksana; Stanislawska, Iwona; Swiatek, Anna
The Geostatistical Analyst uses sample points taken at different locations in a landscape and creates (interpolates) a continuous surface. The Geostatistical Analyst provides two groups of interpolation techniques: deterministic and geostatistical. All methods rely on the similarity of nearby sample points to create the surface. Deterministic techniques use mathematical functions for interpolation. Geostatistics relies on both statistical and mathematical methods, which can be used to create surfaces and assess the uncertainty of the predictions. The first step in geostatistical analysis is variography: computing and modelling a semivariogram. A semivariogram is one of the significant functions to indicate spatial correlation in observations measured at sample locations. It is commonly represented as a graph that shows the variance in measure with distance between all pairs of sampled locations. Such a graph is helpful to build a mathematical model that describes the variability of the measure with location. Modeling of relationship among sample locations to indicate the variability of the measure with distance of separation is called semivariogram modelling. It is applied to applications involving estimating the value of a measure at a new location. Our work presents the analysis of the data following the steps as given below: identification of data set periods, constructing and modelling the empirical semivariogram for single location and using the Kriging mapping function as modelling of TEC maps in mid-latitude during disturbed and quiet days. Based on the semivariogram, weights for the kriging interpolation are estimated. Additional observations do, in general, not provide relevant extra information to the interpolation, because the spatial correlation is well described with the semivariogram. Keywords: Semivariogram, Kriging, modelling, Geostatistics, TEC
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.
High resolution sequence stratigraphic concepts applied to geostatistical modeling
Desaubliaux, G.; De Lestang, A.P.; Eschard, R.
1995-08-01
Lithofacies simulations using a high resolution 3D grid allow to enhance the geometries of internal heterogeneities of reservoirs. In this study the series simulated were the Ness formation, part of the Brent reservoir in the Dunbar field located in the Viking graben of the North Sea. Simulations results have been used to build the reservoir layering supporting the 3D grid used for reservoir engineering, and also used as a frame to study the effects of secondary diagenetic processes on petrophysical properties. The method used is based on a geostatistical study and integrates the following data: a geological model using sequence stratigraphic concepts to define lithofacies sequences and associated bounding surfaces, well data (cores and logs) used as database for geostatistical analysis and simulations, seismic data: a 3D seismic survey has been used to define the internal surfaces bounding the units, outcrop data: The Mesa Verde formation (Colorado, USA) has been used as an outcrop analog to calibrate geostatistical parameters for the simulations (horizontal range of the variograms). This study illustrates the capacity to use high resolution sequence stratigraphic concepts to improve the simulations of reservoirs when the lack of subsurface information reduce the accuracy of geostatistical analysis.
The extension of geostatistical spatial analysis model and its application to datum land appraisal
NASA Astrophysics Data System (ADS)
Fu, Feihong; Li, Xuefei; Zou, Rong
2007-06-01
Geostatistical method can reflect quantitatively variable spatial distribution characteristic, and through produces many different theoretical models to reflect quantitatively the uncertain attribute because of lacking material. But geostatistics is taken a new discipline, it also exists the probability of extension. The extension of ordinary geostatistics includes mainly three aspects: the treatment of outliers in geostatistical spatial data, fitting the variogram and selecting Kriging estimate neighborhood. And it introduces the basic mentality of applying geostatistical space analytical model to appraise datum land price base on analyzing the feasibility.
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.
Constraining geostatistical models with hydrological data to improve prediction realism
NASA Astrophysics Data System (ADS)
Demyanov, V.; Rojas, T.; Christie, M.; Arnold, D.
2012-04-01
Geostatistical models reproduce spatial correlation based on the available on site data and more general concepts about the modelled patters, e.g. training images. One of the problem of modelling natural systems with geostatistics is in maintaining realism spatial features and so they agree with the physical processes in nature. Tuning the model parameters to the data may lead to geostatistical realisations with unrealistic spatial patterns, which would still honour the data. Such model would result in poor predictions, even though although fit the available data well. Conditioning the model to a wider range of relevant data provide a remedy that avoid producing unrealistic features in spatial models. For instance, there are vast amounts of information about the geometries of river channels that can be used in describing fluvial environment. Relations between the geometrical channel characteristics (width, depth, wave length, amplitude, etc.) are complex and non-parametric and are exhibit a great deal of uncertainty, which is important to propagate rigorously into the predictive model. These relations can be described within a Bayesian approach as multi-dimensional prior probability distributions. We propose a way to constrain multi-point statistics models with intelligent priors obtained from analysing a vast collection of contemporary river patterns based on previously published works. We applied machine learning techniques, namely neural networks and support vector machines, to extract multivariate non-parametric relations between geometrical characteristics of fluvial channels from the available data. An example demonstrates how ensuring geological realism helps to deliver more reliable prediction of a subsurface oil reservoir in a fluvial depositional environment.
Examples of improved reservoir modeling through geostatistical data integration
Bashore, W.M.; Araktingi, U.G.
1994-12-31
Results from four case studies are presented to demonstrate improvements in reservoir modeling and subsequent flow predictions through various uses of geostatistical integration methods. Specifically, these cases highlight improvements gained from (1) better understanding of reservoir geometries through 3D visualization, (2) forward modeling to assess the value of new data prior to acquisition and integration, (3) assessment of reduced uncertainty in porosity prediction through integration of seismic acoustic impedance, and (4) integration of crosswell tomographic and reflection data. The intent of each of these examples is to quantify the add-value of geological and geophysical data integration in engineering terms such as fluid-flow results and reservoir property predictions.
Geostatistical modelling of household malaria in Malawi
NASA Astrophysics Data System (ADS)
Chirombo, J.; Lowe, R.; Kazembe, L.
2012-04-01
Malaria is one of the most important diseases in the world today, common in tropical and subtropical areas with sub-Saharan Africa being the region most burdened, including Malawi. This region has the right combination of biotic and abiotic components, including socioeconomic, climatic and environmental factors that sustain transmission of the disease. Differences in these conditions across the country consequently lead to spatial variation in risk of the disease. Analysis of nationwide survey data that takes into account this spatial variation is crucial in a resource constrained country like Malawi for targeted allocation of scare resources in the fight against malaria. Previous efforts to map malaria risk in Malawi have been based on limited data collected from small surveys. The Malaria Indicator Survey conducted in 2010 is the most comprehensive malaria survey carried out in Malawi and provides point referenced data for the study. The data has been shown to be spatially correlated. We use Bayesian logistic regression models with spatial correlation to model the relationship between malaria presence in children and covariates such as socioeconomic status of households and meteorological conditions. This spatial model is then used to assess how malaria varies spatially and a malaria risk map for Malawi is produced. By taking intervention measures into account, the developed model is used to assess whether they have an effect on the spatial distribution of the disease and Bayesian kriging is used to predict areas where malaria risk is more likely to increase. It is hoped that this study can help reveal areas that require more attention from the authorities in the continuing fight against malaria, particularly in children under the age of five.
Modeling fine-scale soil surface structure using geostatistics
NASA Astrophysics Data System (ADS)
Croft, H.; Anderson, K.; Brazier, R. E.; Kuhn, N. J.
2013-04-01
There is widespread recognition that spatially distributed information on soil surface roughness (SSR) is required for hydrological and geomorphological applications. Such information is necessary to describe variability in soil structure, which is highly heterogeneous in time and space, to parameterize hydrology and erosion models and to understand the temporal evolution of the soil surface in response to rainfall. This paper demonstrates how results from semivariogram analysis can quantify key elements of SSR for such applications. Three soil types (silt, silt loam, and silty clay) were used to show how different types of structural variance in SSR evolve during simulated rainfall events. All three soil types were progressively degraded using artificial rainfall to produce a series of roughness states. A calibrated laser profiling instrument was used to measure SSR over a 10 cm × 10 cm spatial extent, at a 2 mm resolution. These data were geostatistically analyzed in the context of aggregate breakdown and soil crusting. The results show that such processes are represented by a quantifiable decrease in sill variance, from 7.81 (control) to 0.94 (after 60 min of rainfall). Soil surface features such as soil cracks, tillage lines and erosional areas were quantified by local maxima in semivariance at a given length scale. This research demonstrates that semivariogram analysis can retrieve spatiotemporal variations in soil surface condition; in order to provide information on hydrological pathways. Consequently, geostatistically derived SSR shows strong potential for inclusion as spatial information in hydrology and erosion models to represent complex surface processes at different soil structural scales.
NASA Astrophysics Data System (ADS)
Illman, Walter A.; Berg, Steven J.; Zhao, Zhanfeng
2015-05-01
The robust performance of hydraulic tomography (HT) based on geostatistics has been demonstrated through numerous synthetic, laboratory, and field studies. While geostatistical inverse methods offer many advantages, one key disadvantage is its highly parameterized nature, which renders it computationally intensive for large-scale problems. Another issue is that geostatistics-based HT may produce overly smooth images of subsurface heterogeneity when there are few monitoring interval data. Therefore, some may question the utility of the geostatistical inversion approach in certain situations and seek alternative approaches. To investigate these issues, we simultaneously calibrated different groundwater models with varying subsurface conceptualizations and parameter resolutions using a laboratory sandbox aquifer. The compared models included: (1) isotropic and anisotropic effective parameter models; (2) a heterogeneous model that faithfully represents the geological features; and (3) a heterogeneous model based on geostatistical inverse modeling. The performance of these models was assessed by quantitatively examining the results from model calibration and validation. Calibration data consisted of steady state drawdown data from eight pumping tests and validation data consisted of data from 16 separate pumping tests not used in the calibration effort. Results revealed that the geostatistical inversion approach performed the best among the approaches compared, although the geological model that faithfully represented stratigraphy came a close second. In addition, when the number of pumping tests available for inverse modeling was small, the geological modeling approach yielded more robust validation results. This suggests that better knowledge of stratigraphy obtained via geophysics or other means may contribute to improved results for HT.
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
Geostatistical Inverse Modeling for Natural Attenuation of Hydrocarbons in Groundwater
NASA Astrophysics Data System (ADS)
Hosseini, A. H.; Deutsch, C. V.; Mendoza, C. A.; Biggar, K. W.
2008-12-01
Parameter uncertainty for natural attenuation has been previously studied in the context of characterizing the uncertainty in the field measured biodegradation rate constant. Natural attenuation response variables (e.g. solute concentrations) should be stated in terms of a number of model parameters in such a way that (1) the most important mechanisms contributing to natural attenuation of petroleum hydrocarbons are simulated, (2) the independent variables (model parameters) and their uncertainty can be estimated using the available observations and prior information and (3) the model is not over-parameterized. Extensive sensitivity analyses show that the source term, aquifer heterogeneity and biodegradation rate of contaminants are the most important factors affecting the fate of dissolved petroleum hydrocarbon (PHC) contaminants in groundwater. A geostatistical inverse modeling approach is developed to quantify uncertainty in source geometry, source dissolution rate, aquifer heterogeneity and biodegradation rate constant. Multiple joint realizations of source geometry and aquifer transmissivity are constructed by distance function (DF) algorithm and sequential self calibration (SSC) approach. A gradient-based optimization approach is then adapted to condition the joint realizations to a number of observed concentrations recorded over a specific monitoring period. The conditioned joint realizations are then ranked based on their goodness of fit and used in the subsequent prediction of uncertainty in the response variables such as downstream concentrations, plume length and contaminant mass loaded into the aquifer. The inverse modeling approach and its associated calculation of sensitivity coefficients show that an extended monitoring period is significantly important in well-posedness of the problem; and an uncertainty in occurrence of the spill can have a minor impact on the modeling results as long as the observation data are collected while the contaminant
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.
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.
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)
martin, manuel; Lacarce, Eva; Meersmans, Jeroen; Orton, Thomas; Saby, Nicolas; Paroissien, Jean-Baptiste; Jolivet, Claudy; Boulonne, Line; Arrouays, Dominique
2013-04-01
Soil organic carbon (SOC) plays a major role in the global carbon budget. It can act as a source or a sink of atmospheric carbon, thereby possibly influencing the course of climate change. Improving the tools that model the spatial distributions of SOC stocks at national scales is a priority, both for monitoring changes in SOC and as an input for global carbon cycles studies. In this paper, first, we considered several increasingly complex boosted regression trees (BRT), a convenient and efficient multiple regression model from the statistical learning field. Further, we considered and a robust geostatistical approach coupled to the BRT models. Testing the different approaches was performed on the dataset from the French Soil Monitoring Network, with a consistent cross-validation procedure. We showed that the BRT models, given its ease of use and its predictive performance, could be preferred to geostatistical models for SOC mapping at the national scale, and if possible be joined with geostatistical models. This conclusion is valid provided that care is exercised in model fitting and validating, that the dataset does not allow for modeling local spatial autocorrelations, as it is the case for many national systematic sampling schemes, and when good quality data about SOC drivers included in the models is available.
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.
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)
Hodgetts, David; Burnham, Brian; Head, William; Jonathan, Atunima; Rarity, Franklin; Seers, Thomas; Spence, Guy
2013-04-01
In the hydrocarbon industry stochastic approaches are the main method by which reservoirs are modelled. These stochastic modelling approaches require geostatistical information on the geometry and distribution of the geological elements of the reservoir. As the reservoir itself cannot be viewed directly (only indirectly via seismic and/or well log data) this leads to a great deal of uncertainty in the geostatistics used, therefore outcrop analogues are characterised to help obtain the geostatistical information required to model the reservoir. Lidar derived Digital Outcrop Model's (DOM's) provide the ability to collect large quantities of statistical information on the geological architecture of the outcrop, far more than is possible by field work alone as the DOM allows accurate measurements to be made in normally inaccessible parts of the exposure. This increases the size of the measured statistical dataset, which in turn results in an increase in statistical significance. There are, however, many problems and biases in the data which cannot be overcome by sample size alone. These biases, for example, may relate to the orientation, size and quality of exposure, as well as the resolution of the DOM itself. Stochastic modelling used in the hydrocarbon industry fall mainly into 4 generic approaches: 1) Object Modelling where the geology is defined by a set of simplistic shapes (such as channels), where parameters such as width, height and orientation, among others, can be defined. 2) Sequential Indicator Simulations where geological shapes are less well defined and the size and distribution are defined using variograms. 3) Multipoint statistics where training images are used to define shapes and relationships between geological elements and 4) Discrete Fracture Networks for fractures reservoirs where information on fracture size and distribution are required. Examples of using DOM's to assist with each of these modelling approaches are presented, highlighting the
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
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.
Boden, Sven; Rogiers, Bart; Jacques, Diederik
2013-09-01
Decommissioning of nuclear building structures usually leads to large amounts of low level radioactive waste. Using a reliable method to determine the contamination depth is indispensable prior to the start of decontamination works and also for minimizing the radioactive waste volume and the total workload. The method described in this paper is based on geostatistical modeling of in situ gamma-ray spectroscopy measurements using the multiple photo peak method. The method has been tested on the floor of the waste gas surge tank room within the BR3 (Belgian Reactor 3) decommissioning project and has delivered adequate results.
Boden, Sven; Rogiers, Bart; Jacques, Diederik
2013-09-01
Decommissioning of nuclear building structures usually leads to large amounts of low level radioactive waste. Using a reliable method to determine the contamination depth is indispensable prior to the start of decontamination works and also for minimizing the radioactive waste volume and the total workload. The method described in this paper is based on geostatistical modeling of in situ gamma-ray spectroscopy measurements using the multiple photo peak method. The method has been tested on the floor of the waste gas surge tank room within the BR3 (Belgian Reactor 3) decommissioning project and has delivered adequate results. PMID:23722072
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.
Estimating malaria burden in Nigeria: a geostatistical modelling approach.
Onyiri, Nnadozie
2015-01-01
This study has produced a map of malaria prevalence in Nigeria based on available data from the Mapping Malaria Risk in Africa (MARA) database, including all malaria prevalence surveys in Nigeria that could be geolocated, as well as data collected during fieldwork in Nigeria between March and June 2007. Logistic regression was fitted to malaria prevalence to identify significant demographic (age) and environmental covariates in STATA. The following environmental covariates were included in the spatial model: the normalized difference vegetation index, the enhanced vegetation index, the leaf area index, the land surface temperature for day and night, land use/landcover (LULC), distance to water bodies, and rainfall. The spatial model created suggests that the two main environmental covariates correlating with malaria presence were land surface temperature for day and rainfall. It was also found that malaria prevalence increased with distance to water bodies up to 4 km. The malaria risk map estimated from the spatial model shows that malaria prevalence in Nigeria varies from 20% in certain areas to 70% in others. The highest prevalence rates were found in the Niger Delta states of Rivers and Bayelsa, the areas surrounding the confluence of the rivers Niger and Benue, and also isolated parts of the north-eastern and north-western parts of the country. Isolated patches of low malaria prevalence were found to be scattered around the country with northern Nigeria having more such areas than the rest of the country. Nigeria's belt of middle regions generally has malaria prevalence of 40% and above. PMID:26618305
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. PMID:25345922
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.
Singdahlsen, D.S. )
1991-06-01
The East Painter structure is a doubly plunging, asymmetric anticline formed on the hanging wall of a back-thrust imbricate near the leading edge of the Absaroka Thrust. The Jurassic Nugget Sandstone is the productive horizon in the East Painter structure. The approximately 900-ft-thick Nugget is a stratigraphically complex and heterogeneous unit deposited by eolian processes in a complex erg setting. The high degree of heterogeneity iwthin the Nugget results from variations in grain size, sorting, mineralogy, and degree and distribution of lamination. The Nugget is comprised of dune, transitional toeset, and interdune facies, each exhibiting different porosity and permeability distributions. Gacies architecture results in both vertical and horizontal stratification of the reservoir. Adequate representation of reservoir heterogeneity is the key to successful modeling of past and future production performance. In addition, a detailed geologic model, based on depositional environment, must be integrated into the simulation to ensure realistic results. Geostatistics provide a method for modeling the spatial reservoir property distirbution while honoring all data values at their sample location. Conditional simulation is a geostatistical technique that generates several equally probably realizations that observe the data and spatial constraints imposed upon them while including fractal variability. Flow simulations of multiple reservoir realizations can provide a probability distribution of reservoir performance that can be used to evaluate risk associated with a project caused by the imcomplete sampling of the reservoir property distribution.
NASA Astrophysics Data System (ADS)
Michalak, Anna M.; Kitanidis, Peter K.
2004-08-01
As the incidence of groundwater contamination continues to grow, a number of inverse modeling methods have been developed to address forensic groundwater problems. In this work the geostatistical approach to inverse modeling is extended to allow for the recovery of the antecedent distribution of a contaminant at a given point back in time, which is critical to the assessment of historical exposure to contamination. Such problems are typically strongly underdetermined, with a large number of points at which the distribution is to be estimated. To address this challenge, the computational efficiency of the new method is increased through the application of the adjoint state method. In addition, the adjoint problem is presented in a format that allows for the reuse of existing groundwater flow and transport codes as modules in the inverse modeling algorithm. As demonstrated in the presented applications, the geostatistical approach combined with the adjoint state method allow for a historical multidimensional contaminant distribution to be recovered even in heterogeneous media, where a numerical solution is required for the forward problem.
Rhea, L.; Person, M.; Marsily, G. de; Ledoux, E.; Galli, A.
1994-11-01
This paper critically evaluates the utility of two different geostatistical methods in tracing long-distance oil migration through sedimentary basins. Geostatistical models of petroleum migration based on kriging and the conditional simulation method are assessed by comparing them to {open_quotes}known{close_quotes} oil migration rates and directions through a numerical carrier bed. In this example, the numerical carrier bed, which serves as {open_quotes}ground truth{close_quotes} in the study, incorporates a synthetic permeability field generated using the method of turning bands. Different representations of lateral permeability heterogeneity of the carrier bed are incorporated into a quasi-three-dimensional model of secondary oil migration. The geometric configuration of the carrier bed is intended to represent migration conditions within the center of a saucer-shaped intracratonic sag basin. In all of the numerical experiments, oil is sourced in the lowest 10% of a saucer-shaped carrier bed and migrates 10-14 km outward in a radial fashion by buoyancy. The effects of vertical permeability variations on secondary oil migration were not considered in the study.
A comparative study of Gaussian geostatistical models and Gaussian Markov random field models1
Song, Hae-Ryoung; Fuentes, Montserrat; Ghosh, Sujit
2008-01-01
Gaussian geostatistical models (GGMs) and Gaussian Markov random fields (GM-RFs) are two distinct approaches commonly used in spatial models for modeling point referenced and areal data, respectively. In this paper, the relations between GGMs and GMRFs are explored based on approximations of GMRFs by GGMs, and approximations of GGMs by GMRFs. Two new metrics of approximation are proposed: (i) the Kullback-Leibler discrepancy of spectral densities and (ii) the chi-squared distance between spectral densities. The distances between the spectral density functions of GGMs and GMRFs measured by these metrics are minimized to obtain the approximations of GGMs and GMRFs. The proposed methodologies are validated through several empirical studies. We compare the performance of our approach to other methods based on covariance functions, in terms of the average mean squared prediction error and also the computational time. A spatial analysis of a dataset on PM2.5 collected in California is presented to illustrate the proposed method. PMID:19337581
NASA Astrophysics Data System (ADS)
Riva, Monica; Panzeri, Marco; Guadagnini, Alberto; Neuman, Shlomo P.
2011-07-01
We analyze theoretically the ability of model quality (sometimes termed information or discrimination) criteria such as the negative log likelihood NLL, Bayesian criteria BIC and KIC and information theoretic criteria AIC, AICc, and HIC to estimate (1) the parameter vector ? of the variogram of hydraulic log conductivity (Y = ln K), and (2) statistical parameters ? and ? proportional to head and log conductivity measurement error variances, respectively, in the context of geostatistical groundwater flow inversion. Our analysis extends the work of Hernandez et al. (2003, 2006) and Riva et al. (2009), who developed nonlinear stochastic inverse algorithms that allow conditioning estimates of steady state and transient hydraulic heads, fluxes and their associated uncertainty on information about conductivity and head data collected in a randomly heterogeneous confined aquifer. Their algorithms are based on recursive numerical approximations of exact nonlocal conditional equations describing the mean and (co)variance of groundwater flow. Log conductivity is parameterized geostatistically based on measured values at discrete locations and unknown values at discrete "pilot points." Optionally, the maximum likelihood function on which the inverse estimation of Y at pilot points is based may include a regularization term reflecting prior information about Y. The relative weight ? assigned to this term and its components ? and ?, as well as ? are evaluated separately from other model parameters to avoid bias and instability. This evaluation is done on the basis of criteria such as NLL, KIC, BIC, HIC, AIC, and AICc. We demonstrate theoretically that, whereas all these six criteria make it possible to estimate ?, KIC alone allows one to estimate validly ? and ? (and thus ?). We illustrate this discriminatory power of KIC numerically by using a differential evolution genetic search algorithm to minimize it in the context of a two-dimensional steady state groundwater flow
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
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.
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.
Goovaerts, Pierre; Trinh, Hoa T; Demond, Avery H; Towey, Timothy; Chang, Shu-Chi; Gwinn, Danielle; Hong, Biling; Franzblau, Alfred; Garabrant, David; Gillespie, Brenda W; Lepkowski, James; Adriaens, Peter
2008-05-15
A key component in any investigation of cause-effect relationships between point source pollution, such as an incinerator, and human health is the availability of measurements and/or accurate models of exposure at the same scale or geography as the health data. Geostatistics allows one to simulate the spatial distribution of pollutant concentrations over various spatial supports while incorporating both field data and predictions of deterministic dispersion models. This methodology was used in a companion paper to identify the census blocks that have a high probability of exceeding a given level of dioxin TEQ (toxic equivalents) around an incinerator in Midland, MI. This geostatistical model, along with population data, provided guidance for the collection of 51 new soil data, which permits the verification of the geostatistical predictions, and calibration of the model. Each new soil measurement was compared to the set of 100 TEQ values simulated at the closest grid node. The correlation between the measured concentration and the averaged simulated value is moderate (0.44), and the actual concentrations are clearly overestimated in the vicinity of the plant property line. Nevertheless, probability intervals computed from simulated TEQ values provide an accurate model of uncertainty: the proportion of observations that fall within these intervals exceeds what is expected from the model. Simulation-based probability intervals are also narrower than the intervals derived from the global histogram of the data, which demonstrates the greater precision of the geostatistical approach. Log-normal ordinary kriging provided fairly similar estimation results for the small and well-sampled area used in this validation study; however, the model of uncertainty was not always accurate. The regression analysis and geostatistical simulation were then conducted using the combined set of 53 original and 51 new soil samples, leading to an updated model for the spatial distribution of
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.
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
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.
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. PMID:22019933
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.
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.
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. PMID:25464050
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
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
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)
Hubbard, W. B.; Militzer, B.
2016-03-01
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.
Geostatistical Modeling of the Spatial Variability of Arsenic in Groundwater of Southeast Michigan
NASA Astrophysics Data System (ADS)
Avruskin, G.; Goovaerts, P.; Meliker, J.; Slotnick, M.; Jacquez, G. M.; Nriagu, J. O.
2004-12-01
The last decade has witnessed an increasing interest in assessing health risks caused by exposure to contaminants present in the soil, air, and water. A key component of any exposure study is a reliable model for the space-time distribution of pollutants. This paper compares the performances of multiGaussian and indicator kriging for modeling probabilistically the space-time distribution of arsenic concentrations in groundwater of Southeast Michigan, accounting for information collected at private residential wells and the hydrogeochemistry of the area. This model will later be combined with a space-time information system to assess the risk associated with exposure to low levels of arsenic in drinking water (typically 5-100 μ g/L), in particular for the development of bladder cancer. Because of the small changes in concentration observed in time, the study has focused on the spatial variability of arsenic. This study confirmed results in the literature that reported intense spatial non-homogeneity of As concentration, resulting in samples that greatly vary even when located a few meters apart. Indicator semivariograms further showed a better spatial connectivity of low concentrations while values exceeding 32 μ g/L (10% of wells) are spatially uncorrelated. Secondary information, such as proximity to Marshall Sandstone, helped only the prediction at a regional scale (i.e. beyond 15 kms), leaving the short-range variability largely unexplained. Several geostatistical tools were tailored to the features of the As dataset: (1) semivariogram values were standardized by the lag variance to correct for the preferential sampling of wells with high concentrations, (2) semivariogram modeling was conducted under the constraint of reproduction of the nugget effect inferred from colocated well measurements, (3) kriging systems were modified to account for repeated measurements at a series of wells while avoiding non-invertible kriging matrices, (4) kriging-based smoothing
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
Gentry, S.; Taylor, J.; Stephenson, D.
1994-06-01
A unique end-to-end LIDAR sensor model has been developed supporting the concept development stage of the CALIOPE UV DIAL and UV laser-induced-fluorescence (LIF) efforts. The model focuses on preserving the temporal and spectral nature of signals as they pass through the atmosphere, are collected by the optics, detected by the sensor, and processed by the sensor electronics and algorithms. This is done by developing accurate component sub-models with realistic inputs and outputs, as well as internal noise sources and operating parameters. These sub-models are then configured using data-flow diagrams to operate together to reflect the performance of the entire DIAL system. This modeling philosophy allows the developer to have a realistic indication of the nature of signals throughout the system and to design components and processing in a realistic environment. Current component models include atmospheric absorption and scattering losses, plume absorption and scattering losses, background, telescope and optical filter models, PMT (photomultiplier tube) with realistic noise sources, amplifier operation and noise, A/D converter operation, noise and distortion, pulse averaging, and DIAL computation. Preliminary results of the model will be presented indicating the expected model operation depicting the October field test at the NTS spill test facility. Indications will be given concerning near-term upgrades to the model.
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. PMID:26547362
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.
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.
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
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
NASA Astrophysics Data System (ADS)
Goeckede, M.; Yadav, V.; Mueller, K. L.; Gourdji, S. M.; Michalak, A. M.; Law, B. E.
2011-12-01
We designed a framework to train biogeophysics-biogeochemistry process models using atmospheric inverse modeling, multiple databases characterizing biosphere-atmosphere exchange, and advanced geostatistics. Our main objective is to reduce uncertainties in carbon cycle and climate projections by exploring the full spectrum of process representation, data assimilation and statistical tools currently available. Incorporating multiple high-quality data sources like eddy-covariance flux databases or biometric inventories has the potential to produce a rigorous data-constrained process model implementation. However, representation errors may bias spatially explicit model output when upscaling to regional to global scales. Atmospheric inverse modeling can be used to validate the regional representativeness of the fluxes, but each piece of prior information from the surface databases limits the ability of the inverse model to characterize the carbon cycle from the perspective of the atmospheric observations themselves. The use of geostatistical inverse modeling (GIM) holds the potential to overcome these limitations, replacing rigid prior patterns with information on how flux fields are correlated across time and space, as well as ancillary environmental data related to the carbon fluxes. We present results from a regional scale data assimilation study that focuses on generating terrestrial CO2 fluxes at high spatial and temporal resolution in the Pacific Northwest United States. Our framework couples surface fluxes from different biogeochemistry process models to very high resolution atmospheric transport using mesoscale modeling (WRF) and Lagrangian Particle dispersion (STILT). We use GIM to interpret the spatiotemporal differences between bottom-up and top-down flux fields. GIM results make it possible to link those differences to input parameters and processes, strengthening model parameterization and process understanding. Results are compared against independent
Giardina, Federica; Franke, Jonas; Vounatsou, Penelope
2015-11-26
The study of malaria spatial epidemiology has benefited from recent advances in geographic information system and geostatistical modelling. Significant progress in earth observation technologies has led to the development of moderate, high and very high resolution imagery. Extensive literature exists on the relationship between malaria and environmental/climatic factors in different geographical areas, but few studies have linked human malaria parasitemia survey data with remote sensing-derived land cover/land use variables and very few have used Earth Observation products. Comparison among the different resolution products to model parasitemia has not yet been investigated. In this study, we probe a proximity measure to incorporate different land cover classes and assess the effect of the spatial resolution of remotely sensed land cover and elevation on malaria risk estimation in Mozambique after adjusting for other environmental factors at a fixed spatial resolution. We used data from the Demographic and Health survey carried out in 2011, which collected malaria parasitemia data on children from 0 to 5 years old, analysing them with a Bayesian geostatistical model. We compared the risk predicted using land cover and elevation at moderate resolution with the risk obtained employing the same variables at high resolution. We used elevation data at moderate and high resolution and the land cover layer from the Moderate Resolution Imaging Spectroradiometer as well as the one produced by MALAREO, a project covering part of Mozambique during 2010-2012 that was funded by the European Union's 7th Framework Program. Moreover, the number of infected children was predicted at different spatial resolutions using AFRIPOP population data and the enhanced population data generated by the MALAREO project for comparison of estimates. The Bayesian geostatistical model showed that the main determinants of malaria presence are precipitation and day temperature. However, the presence
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
Riva, Monica; Guadagnini, Alberto; Fernandez-Garcia, Daniel; Sanchez-Vila, Xavier; Ptak, Thomas
2008-10-23
We analyze the relative importance of the selection of (1) the geostatistical model depicting the structural heterogeneity of an aquifer, and (2) the basic processes to be included in the conceptual model, to describe the main aspects of solute transport at an experimental site. We focus on the results of a forced-gradient tracer test performed at the "Lauswiesen" experimental site, near Tübingen, Germany. In the experiment, NaBr is injected into a well located 52 m from a pumping well. Multilevel breakthrough curves (BTCs) are measured in the latter. We conceptualize the aquifer as a three-dimensional, doubly stochastic composite medium, where distributions of geomaterials and attributes, e.g., hydraulic conductivity (K) and porosity (phi), can be uncertain. Several alternative transport processes are considered: advection, advection-dispersion and/or mass-transfer between mobile and immobile regions. Flow and transport are tackled within a stochastic Monte Carlo framework to describe key features of the experimental BTCs, such as temporal moments, peak time, and pronounced tailing. We find that, regardless the complexity of the conceptual transport model adopted, an adequate description of heterogeneity is crucial for generating alternative equally likely realizations of the system that are consistent with (a) the statistical description of the heterogeneous system, as inferred from the data, and (b) salient features of the depth-averaged breakthrough curve, including preferential paths, slow release of mass particles, and anomalous spreading. While the available geostatistical characterization of heterogeneity can explain most of the integrated behavior of transport (depth-averaged breakthrough curve), not all multilevel BTCs are described with equal success. This suggests that transport models simply based on integrated measurements may not ensure an accurate representation of many of the important features required in three-dimensional transport models. PMID
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)
Yu, Guirui; Zheng, Zemei; Wang, Qiufeng; Fu, Yuling; Zhuang, Jie; Sun, Xiaomin; Wang, Yuesi
2010-08-15
Quantification of the spatiotemporal pattern of soil respiration (R(s)) at the regional scale can provide a theoretical basis and fundamental data for accurate evaluation of the global carbon budget. This study summarizes the R(s) data measured in China from 1995 to 2004. Based on the data, a new region-scale geostatistical model of soil respiration (GSMSR) was developed by modifying a global scale statistical model. The GSMSR model, which is driven by monthly air temperature, monthly precipitation, and soil organic carbon (SOC) density, can capture 64% of the spatiotemporal variability of soil R(s). We evaluated the spatiotemporal pattern of R(s) in China using the GSMSR model. The estimated results demonstrate that the annual R(s) in China ranged from 3.77 to 4.00 Pg C yr(-1) between 1995 and 2004, with an average value of 3.84 +/- 0.07 Pg C yr(-1), contributing 3.92%-4.87% to the global soil CO(2) emission. Annual R(s) rate of evergreen broadleaved forest ecosystem was 698 +/- 11 g C m(-2) yr(-1), significantly higher than that of grassland (439 +/- 7 g C m(-2) yr(-1)) and cropland (555 +/- 12 g C m(-2) yr(-1)). The contributions of grassland, cropland, and forestland ecosystems to the total R(s) in China were 48.38 +/- 0.35%, 22.19 +/- 0.18%, and 20.84 +/- 0.13%, respectively. PMID:20704202
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
NASA Astrophysics Data System (ADS)
He, X.; Sonnenborg, T. O.; Jørgensen, F.; Jensen, K. H.
2013-09-01
Multiple-point geostatistic simulation (MPS) has recently become popular in stochastic hydrogeology, primarily because of its capability to derive multivariate distributions from the training image (TI). However, its application in three dimensional simulations has been constrained by the difficulty of constructing 3-D TI. The object-based TiGenerator may be a useful tool in this regard; yet the sensitivity of model predictions to the training image has not been documented. Another issue in MPS is the integration of multiple geophysical data. The best way to retrieve and incorporate information from high resolution geophysical data is still under discussion. This work shows that TI from TiGenerator delivers acceptable results when used for groundwater modeling, although the TI directly converted from high resolution geophysical data leads to better simulation. The model results also indicate that soft conditioning in MPS is a convenient and efficient way of integrating secondary data such as 3-D airborne electromagnetic data, but over conditioning has to be avoided.
NASA Astrophysics Data System (ADS)
Ly, S.; Charles, C.; Degré, A.
2011-07-01
Spatial interpolation of precipitation data is of great importance for hydrological modelling. Geostatistical methods (kriging) are widely applied in spatial interpolation from point measurement to continuous surfaces. The first step in kriging computation is the semi-variogram modelling which usually used only one variogram model for all-moment data. The objective of this paper was to develop different algorithms of spatial interpolation for daily rainfall on 1 km2 regular grids in the catchment area and to compare the results of geostatistical and deterministic approaches. This study leaned on 30-yr daily rainfall data of 70 raingages in the hilly landscape of the Ourthe and Ambleve catchments in Belgium (2908 km2). This area lies between 35 and 693 m in elevation and consists of river networks, which are tributaries of the Meuse River. For geostatistical algorithms, seven semi-variogram models (logarithmic, power, exponential, Gaussian, rational quadratic, spherical and penta-spherical) were fitted to daily sample semi-variogram on a daily basis. These seven variogram models were also adopted to avoid negative interpolated rainfall. The elevation, extracted from a digital elevation model, was incorporated into multivariate geostatistics. Seven validation raingages and cross validation were used to compare the interpolation performance of these algorithms applied to different densities of raingages. We found that between the seven variogram models used, the Gaussian model was the most frequently best fit. Using seven variogram models can avoid negative daily rainfall in ordinary kriging. The negative estimates of kriging were observed for convective more than stratiform rain. The performance of the different methods varied slightly according to the density of raingages, particularly between 8 and 70 raingages but it was much different for interpolation using 4 raingages. Spatial interpolation with the geostatistical and Inverse Distance Weighting (IDW) algorithms
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.
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
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.
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
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.
A preliminary optical visibility model
NASA Technical Reports Server (NTRS)
Cowles, K.; Levine, B. M.
1994-01-01
A model is being created to describe the effect of weather on optical communications links between space and ground sites. This article describes the process by which the model is developed and gives preliminary results for two sites. The results indicate nighttime attenuation of optical transmission at five wavelengths. It is representative of a sampling of nights at Table Mountain Observatory from January to June and Mount Lemmon Observatory from May and June. The results are designed to predict attenuation probabilities for optical communications links.
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
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.
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.
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
2015-07-01
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.
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
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.
NASA Astrophysics Data System (ADS)
Legleiter, Carl J.
2014-01-01
Fluvial geomorphology is fundamentally concerned with the association between form and process in rivers. Examining these interactions in complex, natural channels requires a means of quantifying the variability and organization of bed topography—this paper introduces a geostatistical framework for characterizing reach-scale spatial structure. Transformation to a channel-centered coordinate system allows topographic variations to be resolved into along- and across-stream components. Dimensionless variables, obtained by scaling distances by the mean channel width and de-trended elevations by the mean bankfull depth, account for channel size and allow spatial patterns to be compared over time or among sites. These patterns are effectively described by the variogram, a spatial statistic that expresses dissimilarity as a function of distance. Fitting a parametric model to the sample variogram provides a rich description of channel form. For example, multiple, nested structures can be combined to account for anisotropy, with varying degrees of spatial variability observed over different length scales along and across the channel. Integral metrics derived from the variogram model yield a more compact summary, and variogram maps a useful visualization. To guide interpretation of these metrics, I used a simple 'channel builder' to isolate the effects of specific aspects of river morphology on the variogram. This analysis indicated that geostatistical models were sensitive to changes in the size, shape, and orientation of channel features, but not to a pure translation of the morphology. The results also highlighted the importance of considering streamwise and transverse components jointly rather than in isolation.
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
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.
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. PMID:27074524
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.
Integration of geologic interpretation into geostatistical simulation
Carle, S.F.
1997-06-01
Embedded Markov chain analysis has been used to quantify geologic interpretation of juxtapositional tendencies of geologic facies. Such interpretations can also be translated into continuous-lag Markov chain models of spatial variability for use in geostatistical simulation of facies architecture.
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
NASA Astrophysics Data System (ADS)
Bellin, A.; Firmani, G.; Fiori, A.
2005-12-01
We analyze, by means of a numerical model, flow toward a pumping well in a confined three-dimensional heterogeneous aquifer. In order to model hydraulic property variations and the associated uncertainty the logconductivity field Y=ln K, where K is the hydraulic conductivity, is modelled as a stationary Random Space Function (RSF), normally distributed with constant mean and variance, σ_Y2, and an exponential axisymmetric covariance function, which identifies the geostatistical model of variability. First, we analyze how the boundary condition at the pumping (extraction) well influences the flow field. Specifically, we show that a specific water discharge through the well's envelope proportional to the local hydraulic conductivity is the condition that better approximates the flow field obtained by imposing a constant head along the well. The latter is the condition that better represents the experimental setup typically employed in pumping tests. Another result of our analysis is that the difference between the drawdown at a fully penetrating monitoring well and the ergodic solution provided by Indelman et al. (1996), which coincides with the Thiem's solution, reduces as the depth of the aquifer increases, becoming negligible as the depth grows larger than 60 vertical integral scales of the hydraulic logconductivity. With these results in mind we envision a simply to apply procedure for obtaining the parameters of the geostatistical model of spatial variability. The procedure is based on fitting the expression of the equivalent hydraulic conductivity proposed by Indelman et al. (1996) to the experimental values obtained by interpreting with the Thiem's solution the measured drawdown at a few wells . If the vertical integral scale is known independently from the pumping test the fitting procedure leads to a robust calculation of the parameters, although the horizontal integral scale is adversely affected by a wide confidence interval.
NASA Astrophysics Data System (ADS)
He, X. L.; Sonnenborg, T. O.; Jørgensen, F.; Jensen, K. H.
2014-08-01
Multiple-point geostatistical simulation (MPS) has recently become popular in stochastic hydrogeology, primarily because of its capability to derive multivariate distributions from a training image (TI). However, its application in three-dimensional (3-D) simulations has been constrained by the difficulty of constructing a 3-D TI. The object-based unconditional simulation program TiGenerator may be a useful tool in this regard; yet the applicability of such parametric training images has not been documented in detail. Another issue in MPS is the integration of multiple geophysical data. The proper way to retrieve and incorporate information from high-resolution geophysical data is still under discussion. In this study, MPS simulation was applied to different scenarios regarding the TI and soft conditioning. By comparing their output from simulations of groundwater flow and probabilistic capture zone, TI from both sources (directly converted from high-resolution geophysical data and generated by TiGenerator) yields comparable results, even for the probabilistic capture zones, which are highly sensitive to the geological architecture. This study also suggests that soft conditioning in MPS is a convenient and efficient way of integrating secondary data such as 3-D airborne electromagnetic data (SkyTEM), but over-conditioning has to be avoided.
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
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.
Geostatistical methods for hazard assessment and site characterization in mining
Riefenberg, J.
1996-12-01
Ground control hazards, coal quality, ore reserve estimation, and pollution modeling seem unrelated topics from most mining perspectives. However, geostatistical methods can be used to characterize each of these, and more topics. Exploratory drill core data, and continued drilling and field measurements, can provide a wealth of information related to each of the above areas and are often severely underutilized. Recent studies have led to the development of the Multiple Parameter Mapping (MPM) technology, which utilizes geostatistics and other numerical modeling methods, to generate a {open_quotes}hazard index{close_quotes} map, often from exploratory drill core data. This mapping has been presented for ground control hazards relating roof quality, floor quality, numerically modelled stresses due to mining geometry, and geologic features. A review of the MPM method, future directions with the MPM, and a discussion of using these and other geostatistical methods to quantify coal quality, ore reserve estimation, and pollutant modeling are presented in this paper.
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. PMID:24875258
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.
A preliminary characterization of the spatial variability of precipitation at Yucca Mountain, Nevada
Hevesi, J.A.; Flint, A.L.; Ambos, D.S.
1994-12-31
Isohyetal maps of precipitation and numerical models for simulating precipitation are needed to characterize natural infiltration at Yucca Mountain, Nevada. The objective of this study was to characterize the spatial variability of precipitation within the domain of the natural catchments overlying the potential repository, and to define preliminary geostatistical models based on differences in storm type for the numerical simulation of precipitation.
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.
Modeling the complete Otto cycle - Preliminary version
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.
2010-01-01
Background The Zambia Malaria Indicator Survey (ZMIS) of 2006 was the first nation-wide malaria survey, which combined parasitological data with other malaria indicators such as net use, indoor residual spraying and household related aspects. The survey was carried out by the Zambian Ministry of Health and partners with the objective of estimating the coverage of interventions and malaria related burden in children less than five years. In this study, the ZMIS data were analysed in order (i) to estimate an empirical high-resolution parasitological risk map in the country and (ii) to assess the relation between malaria interventions and parasitaemia risk after adjusting for environmental and socio-economic confounders. Methods The parasitological risk was predicted from Bayesian geostatistical and spatially independent models relating parasitaemia risk and environmental/climatic predictors of malaria. A number of models were fitted to capture the (potential) non-linearity in the malaria-environment relation and to identify the elapsing time between environmental effects and parasitaemia risk. These models included covariates (a) in categorical scales and (b) in penalized and basis splines terms. Different model validation methods were used to identify the best fitting model. Model-based risk predictions at unobserved locations were obtained via Bayesian predictive distributions for the best fitting model. Results Model validation indicated that linear environmental predictors were able to fit the data as well as or even better than more complex non-linear terms and that the data do not support spatial dependence. Overall the averaged population-adjusted parasitaemia risk was 20.0% in children less than five years with the highest risk predicted in the northern (38.3%) province. The odds of parasitaemia in children living in a household with at least one bed net decreases by 40% (CI: 12%, 61%) compared to those without bed nets. Conclusions The map of parasitaemia
Geostatistical analysis of Palmerton soil survey data.
Starks, T H; Sparks, A R; Brown, K W
1987-11-01
This paper describes statistical and geostatistical analyses of data from a soil sampling survey. Soil sampling was performed, in October and November of 1985, to obtain information on the level, extent, and spatial structure of metal pollution of the soil in and around the Palmerton, Pennsylvania, NPL Superfund site. Measurements of the concentrations of cadmium, copper, lead, and zinc in the soil samples were obtained. An appropriate variance stabilizing transformation was determined. Estimation of variance components was performed. Generalized convariance functions for log-transformed concentrations were estimated for each metal. Block kriging was employed using the estimated spatial structure models to obtain estimated metal concentration distributions over the central part of Palmerton.
NASA Astrophysics Data System (ADS)
Gasch, Caley K.; Hengl, Tomislav; Gräler, Benedikt; Meyer, Hanna; Magney, Troy; Brown, David J.
2015-04-01
Dynamic soil data collected using automated sensor networks can facilitate our understanding of soil processes, but highly dimensional data may be difficult to analyze in a manner that incorporates correlation in properties through 3-dimensions and time (3D+T). We demonstrate two approaches to making continuous predictions of dynamic soil properties from fixed point observations. For this analysis, we used the Cook Farm data set, which includes hourly measurements of soil volumetric water content, temperature, and electrical conductivity at 42 points and five depths, collected over five years. We compare performance of two modeling frameworks. In the first framework we used random forest algorithms to fit a 3D+T regression model to make predictions of all three soil variables from 2- and 3-dimensional, temporal, and spatio-temporal covariates. In the second framework we developed a 3D+T kriging model after detrending the observations for depth-dependent seasonal effects. The results show that both models accurately predicted soil temperature, but the kriging model outperformed the regression model according to cross-validation; it explained 37%, 96%, and 16% of the variability in water content, temperature, and electrical conductivity respectively versus 34%, 93%, and 4% explained by the random forest model. The full random forest regression model had high goodness-of-fit for all variables, which was reduced in cross-validation. Temporal model components (i.e. day of the year) explained most of the variability in observations. The seamless predictions of 3D+T data produced from this analysis can assist in understanding soil processes and how they change through a season, under different land management scenarios, and how they relate to other environmental processes.
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.
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 for high resolution geomorphometry: from spatial continuity to surface texture
NASA Astrophysics Data System (ADS)
Trevisani, Sebastiano
2015-04-01
This presentation introduces the use of geostatistics in the context of high-resolution geomorphometry. The application of geostatistics to geomorphometry permits a shift in perspective, moving our attention more toward spatial continuity description than toward the inference of a spatial continuity model. This change in perspective opens interesting directions in the application of geostatistical methods in geomorphometry. Geostatistical methodologies have been extensively applied and adapted in the context of remote sensing, leading to many interesting applications aimed at the analysis of the complex patterns characterizing imagery. Among these applications the analysis of image texture has to be mentioned. In fact, the analysis of image texture reverts to the analysis of surface texture when the analyzed image is a raster representation of a digital terrain model. The main idea is to use spatial-continuity indices as multiscale and directional descriptors of surface texture, including the important aspect related to surface roughness. In this context we introduce some examples regarding the application of geostatistics for image analysis and surface texture characterization. We also show as in presence of complex morphological settings there is the need to use alternative indices of spatial continuity, less sensitive to hotspots and to non-stationarity that often characterize surface morphology. This introduction is mainly dedicated to univariate geostatistics; however the same concepts could be exploited by means of multivariate as well as multipoint geostatistics.
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.
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.
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. PMID:27104113
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.
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.
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.
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.
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 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
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
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.
Optimization of Pilot Point Locations: an efficient and geostatistical perspective
NASA Astrophysics Data System (ADS)
Mehne, J.; Nowak, W.
2012-04-01
The pilot point method is a wide-spread method for calibrating ensembles of heterogeneous aquifer models on available field data such as hydraulic heads. The pilot points are virtual measurements of conductivity, introduced as localized carriers of information in the inverse procedure. For each heterogeneous aquifer realization, the pilot point values are calibrated until all calibration data are honored. Adequate placement and numbers of pilot points are crucial both for accurate representation of heterogeneity and to keep the computational costs of calibration at an acceptable level. Current placement methods for pilot points either rely solely on the expertise of the modeler, or they involve computationally costly sensitivity analyses. None of the existing placement methods directly addressed the geostatistical character of the placement and calibration problem. This study presents a new method for optimal selection of pilot point locations. We combine ideas from Ensemble Kalman Filtering and geostatistical optimal design with straightforward optimization. In a first step, we emulate the pilot point method with a modified Ensemble Kalman Filter for parameter estimation at drastically reduced computational costs. This avoids the costly evaluation of sensitivity coefficients often used for optimal placement of pilot points. Second, we define task-driven objective functions for the optimal placement of pilot points, based on ideas from geostatistical optimal design of experiments. These objective functions can be evaluated at speed, without carrying out the actual calibration process, requiring nothing else but ensemble covariances that are available from step one. By formal optimization, we can find pilot point placement schemes that are optimal in representing the data for the task-at-hand with minimal numbers of pilot points. In small synthetic test applications, we demonstrate the promising computational performance and the geostatistically logical choice of
Robust geostatistical analysis of spatial data
NASA Astrophysics Data System (ADS)
Papritz, A.; Künsch, H. R.; Schwierz, C.; Stahel, W. A.
2012-04-01
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. Outlying observations may results from errors (e.g. in data transcription) or from local perturbations in the processes that are responsible for a given pattern of spatial variation. As an example, the spatial distribution of some trace metal in the soils of a region may be distorted by emissions of local anthropogenic sources. Outliers affect the modelling of the large-scale spatial variation, the so-called external drift or 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) [2] 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) [1] 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. 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
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.
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.
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.
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.
NASA Astrophysics Data System (ADS)
Nickel, Stefan; Hertel, Anne; Pesch, Roland; Schröder, Winfried; Steinnes, Eiliv; Uggerud, Hilde Thelle
2014-12-01
Objective. This study explores the statistical relations between the accumulation of heavy metals in moss and natural surface soil and potential influencing factors such as atmospheric deposition by use of multivariate regression-kriging and generalized linear models. Based on data collected in 1995, 2000, 2005 and 2010 throughout Norway the statistical correlation of a set of potential predictors (elevation, precipitation, density of different land uses, population density, physical properties of soil) with concentrations of cadmium (Cd), mercury and lead in moss and natural surface soil (response variables), respectively, were evaluated. Spatio-temporal trends were estimated by applying generalized linear models and geostatistics on spatial data covering Norway. The resulting maps were used to investigate to what extent the HM concentrations in moss and natural surface soil are correlated. Results. From a set of ten potential predictor variables the modelled atmospheric deposition showed the highest correlation with heavy metals concentrations in moss and natural surface soil. Density of various land uses in a 5 km radius reveal significant correlations with lead and cadmium concentration in moss and mercury concentration in natural surface soil. Elevation also appeared as a relevant factor for accumulation of lead and mercury in moss and cadmium in natural surface soil respectively. Precipitation was found to be a significant factor for cadmium in moss and mercury in natural surface soil. The integrated use of multivariate generalized linear models and kriging interpolation enabled creating heavy metals maps at a high level of spatial resolution. The spatial patterns of cadmium and lead concentrations in moss and natural surface soil in 1995 and 2005 are similar. The heavy metals concentrations in moss and natural surface soil are correlated significantly with high coefficients for lead, medium for cadmium and moderate for mercury. From 1995 up to 2010 the
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.
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...
Functional volumes modeling: theory and preliminary assessment.
Fox, P T; Lancaster, J L; Parsons, L M; Xiong, J H; Zamarripa, F
1997-01-01
A construct for metanalytic modeling of the functional organization of the human brain, termed functional volumes modeling (FVM), is presented and preliminarily tested. FVM uses the published literature to model brain functional areas as spatial probability distributions. The FVM statistical model estimates population variance (i.e., among individuals) from the variance observed among group-mean studies, these being the most prevalent type of study in the functional imaging literature. The FVM modeling strategy is tested by: (1) constructing an FVM of the mouth region of primary motor cortex using published, group-mean, functional imaging reports as input, and (2) comparing the confidence bounds predicted by that FVM with those observed in 10 normal subjects performing overt-speech tasks. The FVM model correctly predicted the mean location and spatial distribution of per-subject functional responses. FVM has a wide range of applications, including hypothesis testing for statistical parametric images.
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 third way in…
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.
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.
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.
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
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.
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.
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.
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
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.
Probabilistic assessment of ground-water contamination. 1: Geostatistical framework
Rautman, C.A.; Istok, J.D.
1996-09-01
Characterizing the extent and severity of ground-water contamination at waste sites is expensive and time-consuming. A probabilistic approach, based on the acceptance of uncertainty and a finite probability of making classification errors (contaminated relative to a regulatory threshold vs. uncontaminated), is presented as an alternative to traditional site characterization methodology. The approach utilizes geostatistical techniques to identify and model the spatial continuity of contamination at a site (variography) and to develop alternate plausible simulations of contamination fields (conditional simulation). Probabilistic summaries of many simulations provide tools for (a) estimating the range of plausible contaminant concentrations at unsampled locations, (b) identifying the locations of boundaries between contaminated and uncontaminated portions of the site and the degree of certainty in those locations, and (c) estimating the range of plausible values for total contaminant mass. The first paper in the series presents the geostatistical framework and illustrates the approach using synthetic data for a hypothetical site. The second paper presents an application of the proposed methodology to the probabilistic assessment of ground-water contamination at a site involving ground-water contamination by nitrate and herbicide in a shallow, unconfined alluvial aquifer in an agricultural area in eastern Oregon.
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.
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
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
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.
A time-dependent ice sheet model - Preliminary results
NASA Technical Reports Server (NTRS)
Bindschadler, R. A.; Gore, R.
1982-01-01
A numerical model of ice sheet flow is developed, and preliminary results are described. This model includes vertical resolution of temperature, stress, and strain rate which represents a considerable improvement over previous vertically averaged ice sheet models. The model follows the flow of ice along a flow line within an ice sheet drainage basin. Longitudinal stresses and basal sliding are included. Basal sliding is dependent on the base shear stress and a specified distribution of basal water pressure. The numerical methods used to solve the coupled set of stress and velocity equations for the static and time-evolutionary cases are discussed. A steady state profile simulating an ice stream is calculated for a particular set of input parameters, and changes in the profile are examined for different choices of parameters. Preliminary studies of response behavior are completed using a simplified ice sheet geometry with a fixed terminus or grounding line. The results of these studies illustrate ice sheet thinning in response to a lowered sea level or to a reduction in the extent of ice rises (or pinning points) within ice shelves.
A Preliminary Shape and Spin Axis Model for 595 Polyxena
NASA Astrophysics Data System (ADS)
Warner, Brian D.
2008-10-01
Photometric observations made at the Palmer Divide Observatory during the 2006 and 2008 apparitions of the main-belt asteroid 595 Polyxena were combined with dense lightcurves from 1993 included in the Uppsala Asteroid Photometric Catalog and a sparse lightcurve based on data from the USNO to determine a preliminary shape and spin axis model. Two solutions dominated the result set, one prograde (? = 42°, ß = 8°) and one retrograde (? = 222°, ß = -4°). The uncertainty in each coordinate is ± 5°. The sidereal period was found to be 11.794162 ± 0.000023 h.
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 ...
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.
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.
Grisotto, Laura; Consonni, Dario; Cecconi, Lorenzo; Catelan, Dolores; Lagazio, Corrado; Bertazzi, Pier Alberto; Baccini, Michela; Biggeri, Annibale
2016-01-01
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. PMID:27087040
Benchmarking a geostatistical procedure for the homogenisation of annual precipitation series
NASA Astrophysics Data System (ADS)
Caineta, Júlio; Ribeiro, Sara; Henriques, Roberto; Soares, Amílcar; Costa, Ana Cristina
2014-05-01
The European project COST Action ES0601, Advances in homogenisation methods of climate series: an integrated approach (HOME), has brought to attention the importance of establishing reliable homogenisation methods for climate data. In order to achieve that, a benchmark data set, containing monthly and daily temperature and precipitation data, was created to be used as a comparison basis for the effectiveness of those methods. Several contributions were submitted and evaluated by a number of performance metrics, validating the results against realistic inhomogeneous data. HOME also led to the development of new homogenisation software packages, which included feedback and lessons learned during the project. Preliminary studies have suggested a geostatistical stochastic approach, which uses Direct Sequential Simulation (DSS), as a promising methodology for the homogenisation of precipitation data series. Based on the spatial and temporal correlation between the neighbouring stations, DSS calculates local probability density functions at a candidate station to detect inhomogeneities. The purpose of the current study is to test and compare this geostatistical approach with the methods previously presented in the HOME project, using surrogate precipitation series from the HOME benchmark data set. The benchmark data set contains monthly precipitation surrogate series, from which annual precipitation data series were derived. These annual precipitation series were subject to exploratory analysis and to a thorough variography study. The geostatistical approach was then applied to the data set, based on different scenarios for the spatial continuity. Implementing this procedure also promoted the development of a computer program that aims to assist on the homogenisation of climate data, while minimising user interaction. Finally, in order to compare the effectiveness of this methodology with the homogenisation methods submitted during the HOME project, the obtained results
Preliminary deformation model for National Seismic Hazard map of Indonesia
NASA Astrophysics Data System (ADS)
Meilano, Irwan; Susilo, Gunawan, Endra; Sarsito, Dina; Prijatna, Kosasih; Abidin, Hasanuddin Z.; Efendi, Joni
2015-04-01
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.
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.
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.
Electrically Induced Limbic Seizures: Preliminary Findings in a Rodent Model
Kowski, Alexander B; Holtkamp, Martin
2015-01-01
In epilepsy, novel pharmacological and nonpharmacological treatment approaches are commonly assessed in model systems of acute motor and often generalized seizures. We developed a rodent model with short-term electrical stimulation of the perforant path resulting in stereotyped limbic seizures. Limbic structures play a major role in human intractable epilepsy. In 10 rats, single electrical 5-second and 20-Hz stimuli to the perforant path reliably produced limbic seizures characterized by resting behavior and subtle motor signs. Electrophysiological recordings from the dentate gyrus demonstrated a seizure pattern with 4-Hz to 5-Hz discharges. Multiple inductions of seizures within 72 hours did not alter behavioral and electrophysiological seizure characteristics. Electrophysiological excitatory and inhibitory parameters assessed by evoked single and paired pulses did not change with increasing number of seizures. We present preliminary findings on a new model of electrically induced limbic seizures of mesiotemporal origin. This model may represent a reliable screening tool for new treatment approaches such as deep brain stimulation. PMID:25861223
Brooker, Simon; Clements, Archie C.A.
2009-01-01
Multiple parasite infections are widespread in the developing world and understanding their geographical distribution is important for spatial targeting of differing intervention packages. We investigated the spatial epidemiology of mono- and co-infection with helminth parasites in East Africa and developed a geostatistical model to predict infection risk. The data used for the analysis were taken from standardised school surveys of Schistosoma mansoni and hookworm (Ancylostoma duodenale/Necator americanus) carried out between 1999 and 2005 in East Africa. Prevalence of mono- and co-infection was modelled using satellite-derived environmental and demographic variables as potential predictors. A Bayesian multi-nominal geostatistical model was developed for each infection category for producing maps of predicted co-infection risk. We show that heterogeneities in co-infection with S. mansoni and hookworm are influenced primarily by the distribution of S. mansoni, rather than the distribution of hookworm, and that temperature, elevation and distance to large water bodies are reliable predictors of the spatial large-scale distribution of co-infection. On the basis of these results, we developed a validated geostatistical model of the distribution of co-infection at a scale that is relevant for planning regional disease control efforts that simultaneously target multiple parasite species. PMID:19073189
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.
Digital characterization and preliminary computer modeling of hydrocarbon bearing sandstone
NASA Astrophysics Data System (ADS)
Latief, Fourier Dzar Eljabbar; Haq, Tedy Muslim
2014-03-01
With the advancement of three dimensional imaging technologies, especially the μCT scanning systems, we have been able to obtain three-dimensional digital representation of porous rocks in the scale of micrometers. Characterization was then also possible to conduct using computational approach. Hydrocarbon bearing sandstone has become one of interesting objects to analyze in the last decade. In this research, we performed digital characterization of hydrocarbon bearing sandstone reservoir from Sumatra. The sample was digitized using a μCT scanner (Skyscan 1173) which produced series of reconstructed images with the spatial resolution of 15 μm. Using computational approaches, i.e., image processing, image analysis, and simulation of fluid flow inside the rock using Lattice Boltzmann Method, we have been able to obtain the porosity of the sandstone, which is 23.89%, and the permeability, which is 9382 mD. Based on visual inspection, the porosity value, along with the calculated specific surface area, we produce a preliminary computer model of the rock using grain based method. This method employs a reconstruction of grains using the non-spherical model, and a purely random deposition of the grains in a virtual three dimensional cube with the size of 300 × 300 × 300. The model has porosity of 23.96%, and the permeability is 7215 mD. While the error of the porosity is very small (which is only 0.3%), the permeability has error of around 23% from the real sample which is considered very significant. This suggests that the modeling based on porosity and specific surface area is not satisfactory to produce a representative model. However, this work has been a good example of how characterization and modeling of porous rock can be conducted using a non-destructive computational approach.
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. PMID:24185879
NASA Astrophysics Data System (ADS)
Jha, Sanjeev Kumar; Mariethoz, Gregoire; Evans, Jason; McCabe, Matthew F.; Sharma, Ashish
2015-08-01
A geostatistical framework is proposed to downscale daily precipitation and temperature. The methodology is based on multiple-point geostatistics (MPS), where a multivariate training image is used to represent the spatial relationship between daily precipitation and daily temperature over several years. Here the training image consists of daily rainfall and temperature outputs from the Weather Research and Forecasting (WRF) model at 50 and 10 km resolution for a 20 year period ranging from 1985 to 2004. The data are used to predict downscaled climate variables for the year 2005. The result, for each downscaled pixel, is daily time series of precipitation and temperature that are spatially dependent. Comparison of predicted precipitation and temperature against a reference data set indicates that both the seasonal average climate response together with the temporal variability are well reproduced. The explicit inclusion of time dependence is explored by considering the climate properties of the previous day as an additional variable. Comparison of simulations with and without inclusion of time dependence shows that the temporal dependence only slightly improves the daily prediction because the temporal variability is already well represented in the conditioning data. Overall, the study shows that the multiple-point geostatistics approach is an efficient tool to be used for statistical downscaling to obtain local-scale estimates of precipitation and temperature from General Circulation Models.
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
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.
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.
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.
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.
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
NASA Astrophysics Data System (ADS)
Fienen, Michael N.; Clemo, Tom; Kitanidis, Peter K.
2008-12-01
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.
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.
Increasing confidence in mass discharge estimates using geostatistical methods.
Cai, Zuansi; Wilson, Ryan D; Cardiff, Michael A; Kitanidis, Peter K
2011-01-01
Mass discharge is one metric rapidly gaining acceptance for assessing the performance of in situ groundwater remediation systems. Multilevel sampling transects provide the data necessary to make such estimates, often using the Thiessen Polygon method. This method, however, does not provide a direct estimate of uncertainty. We introduce a geostatistical mass discharge estimation approach that involves a rigorous analysis of data spatial variability and selection of an appropriate variogram model. High-resolution interpolation was applied to create a map of measurements across a transect, and the magnitude and uncertainty of mass discharge were quantified by conditional simulation. An important benefit of the approach is quantified uncertainty of the mass discharge estimate. We tested the approach on data from two sites monitored using multilevel transects. We also used the approach to explore the effect of lower spatial monitoring resolution on the accuracy and uncertainty of mass discharge estimates. This process revealed two important findings: (1) appropriate monitoring resolution is that which yielded an estimate comparable with the full dataset value, and (2) high-resolution sampling yields a more representative spatial data structure descriptor, which can then be used via conditional simulation to make subsequent mass discharge estimates from lower resolution sampling of the same transect. The implication of the latter is that a high-resolution multilevel transect needs to be sampled only once to obtain the necessary spatial data descriptor for a contaminant plume exhibiting minor temporal variability, and thereafter less spatially intensely to reduce costs.
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.
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.
NASA Astrophysics Data System (ADS)
Ostrowski, J.; Shlomi, S.; Michalak, A.
2007-12-01
The process of estimating the release history of a contaminant in groundwater relies on coupling a limited number of concentration measurements with a groundwater flow and transport model in an inverse modeling framework. The information provided by available measurements is generally not sufficient to fully characterize the unknown release history; therefore, an accurate assessment of the estimation uncertainty is required. The modeler's level of confidence in the transport parameters, expressed as pdfs, can be incorporated into the inverse model to improve the accuracy of the release estimates. In this work, geostatistical inverse modeling is used in conjunction with Monte Carlo sampling of transport parameters to estimate groundwater contaminant release histories. Concentration non-negativity is enforced using a Gibbs sampling algorithm based on a truncated normal distribution. The method is applied to two one-dimensional test cases: a hypothetical dataset commonly used in validating contaminant source identification methods, and data collected from a tetrachloroethylene and trichloroethylene plume at the Dover Air Force Base in Delaware. The estimated release histories and associated uncertainties are compared to results from a geostatistical inverse model where uncertainty in transport parameters is ignored. Results show that the a posteriori uncertainty associated with the model that accounts for parameter uncertainty is higher, but that this model provides a more realistic representation of the release history based on available data. This modified inverse modeling technique has many applications, including assignment of liability in groundwater contamination cases, characterization of groundwater contamination, and model calibration.
G STL: the geostatistical template library in C++
NASA Astrophysics Data System (ADS)
Remy, Nicolas; Shtuka, Arben; Levy, Bruno; Caers, Jef
2002-10-01
The development of geostatistics has been mostly accomplished by application-oriented engineers in the past 20 years. The focus on concrete applications gave birth to many algorithms and computer programs designed to address different issues, such as estimating or simulating a variable while possibly accounting for secondary information such as seismic data, or integrating geological and geometrical data. At the core of any geostatistical data integration methodology is a well-designed algorithm. Yet, despite their obvious differences, all these algorithms share many commonalities on which to build a geostatistics programming library, lest the resulting library is poorly reusable and difficult to expand. Building on this observation, we design a comprehensive, yet flexible and easily reusable library of geostatistics algorithms in C++. The recent advent of the generic programming paradigm allows us elegantly to express the commonalities of the geostatistical algorithms into computer code. Generic programming, also referred to as "programming with concepts", provides a high level of abstraction without loss of efficiency. This last point is a major gain over object-oriented programming which often trades efficiency for abstraction. It is not enough for a numerical library to be reusable, it also has to be fast. Because generic programming is "programming with concepts", the essential step in the library design is the careful identification and thorough definition of these concepts shared by most geostatistical algorithms. Building on these definitions, a generic and expandable code can be developed. To show the advantages of such a generic library, we use G STL to build two sequential simulation programs working on two different types of grids—a surface with faults and an unstructured grid—without requiring any change to the G STL code.
Geostatistical inspired metamodeling and optimization of nanoscale analog circuits
NASA Astrophysics Data System (ADS)
Okobiah, Oghenekarho
The current trend towards miniaturization of modern consumer electronic devices significantly affects their design. The demand for efficient all-in-one appliances leads to smaller, yet more complex and powerful nanoelectronic devices. The increasing complexity in the design of such nanoscale Analog/Mixed-Signal Systems-on-Chip (AMS-SoCs) presents difficult challenges to designers. One promising design method used to mitigate the burden of this design effort is the use of metamodeling (surrogate) modeling techniques. Their use significantly reduces the time for computer simulation and design space exploration and optimization. This dissertation addresses several issues of metamodeling based nanoelectronic based AMS design exploration. A surrogate modeling technique which uses geostatistical based Kriging prediction methods in creating metamodels is proposed. Kriging prediction techniques take into account the correlation effects between input parameters for performance point prediction. We propose the use of Kriging to utilize this property for the accurate modeling of process variation effects of designs in the deep nanometer region. Different Kriging methods have been explored for this work such as simple and ordinary Kriging. We also propose another metamodeling technique Kriging-Bootstrapped Neural Network that combines the accuracy and process variation awareness of Kriging with artificial neural network models for ultra-fast and accurate process aware metamodeling design. The proposed methodologies combine Kriging metamodels with selected algorithms for ultra-fast layout optimization. The selected algorithms explored are: Gravitational Search Algorithm (GSA), Simulated Annealing Optimization (SAO), and Ant Colony Optimization (ACO). Experimental results demonstrate that the proposed Kriging metamodel based methodologies can perform the optimizations with minimal computational burden compared to traditional (SPICE-based) design flows.
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.
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.
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.
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
Verifying the high-order consistency of training images with data for multiple-point geostatistics
NASA Astrophysics Data System (ADS)
Pérez, Cristian; Mariethoz, Gregoire; Ortiz, Julián M.
2014-09-01
Parameter inference is a key aspect of spatial modeling. A major appeal of variograms is that they allow inferring the spatial structure solely based on conditioning data. This is very convenient when the modeler does not have a ready-made geological interpretation. To date, such an easy and automated interpretation is not available in the context of most multiple-point geostatistics applications. Because training images are generally conceptual models, their preparation is often based on subjective criteria of the modeling expert. As a consequence, selection of an appropriate training image is one of the main issues one must face when using multiple-point simulation. This paper addresses the development of a geostatistical tool that addresses two separate problems. It allows (1) ranking training images according to their relative compatibility to the data, and (2) obtaining an absolute measure quantifying the consistency between training image and data in terms of spatial structure. For both, two alternative implementations are developed. The first one computes the frequency of each pattern in each training image. This method is statistically sound but computationally demanding. The second implementation obtains similar results at a lesser computational cost using a direct sampling approach. The applicability of the methodologies is successfully evaluated in two synthetic 2D examples and one real 3D mining example at the Escondida Norte deposit.
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)
Lee, J. H.; Kitanidis, P. K.
2014-12-01
The geostatistical approach (GA) to inversion has been applied to many engineering applications to estimate unknown parameter functions and quantify the uncertainty in estimation. Thanks to recent advances in sensor technology, large-scale/joint inversions have become more common and the implementation of the traditional GA algorithm would require thousands of expensive numerical simulation runs, which would be computationally infeasible. To overcome the computational challenges, we present the Principal Component Geostatistical Approach (PCGA) that makes use of leading principal components of the prior information to avoid expensive sensitivity computations and obtain an approximate GA solution and its uncertainty with a few hundred numerical simulation runs. As we show in this presentation, the PCGA estimate is close to, even almost same as the estimate obtained from full-model implemented GA while one can reduce the computation time by the order of 10 or more in most practical cases. Furthermore, our method is "black-box" in the sense that any numerical simulation software can be linked to PCGA to perform the geostatistical inversion. This enables a hassle-free implementation of GA to multi-physics problems and joint inversion with different types of measurements such as hydrologic, chemical, and geophysical data obviating the need to explicitly compute the sensitivity of measurements through expensive coupled numerical simulations. Lastly, the PCGA is easily implemented to run the numerical simulations in parallel, thus taking advantage of high performance computing environments. We show the effectiveness and efficiency of our method with several examples such as 3-D transient hydraulic tomography, joint inversion of head and tracer data and geochemical heterogeneity identification.
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.
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.
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
Map on predicted deposition of Cs-137 in Spanish soils from geostatistical analyses.
Caro, A; Legarda, F; Romero, L; Herranz, M; Barrera, M; Valiño, F; Idoeta, R; Olondo, C
2013-01-01
The knowledge of the distribution of (137)Cs deposition over Spanish mainland soils along with the geographical, physical and morphological terrain information enable us to know the (137)Cs background content in soil. This could be useful as a tool in a hypothetical situation of an accident involving a radioactive discharge or in soil erosion studies. A Geographic Information System (GIS) would allow the gathering of all the mentioned information. In this work, gamma measurements of (137)Cs on 34 Spanish mainland soils, rainfall data taken from 778 weather stations, soil types and geographical and physical terrain information were input into a GIS. Geostatistical techniques were applied to interpolate values of (137)Cs activity at unsampled places, obtaining prediction maps of (137)Cs deposition. Up to now, geostatistical methods have been used to model spatial continuity of data. Through semivariance and cross-covariance functions the spatial correlation of such data can be studied and described. Ordinary and simple kriging techniques were carried out to map spatial patterns of (137)Cs deposition, and ordinary and simple co-kriging were used to improve the prediction map obtained through a second related variable: namely the rainfall. To choose the best prediction map of (137)Cs deposition, the spatial dependence of the variable, the correlation coefficient and the prediction errors were evaluated using the different models previously mentioned. The best result for (137)Cs deposition map was obtained when applying the co-kriging techniques.
Bayesian Geostatistical Analysis and Prediction of Rhodesian Human African Trypanosomiasis
Wardrop, Nicola A.; Atkinson, Peter M.; Gething, Peter W.; Fèvre, Eric M.; Picozzi, Kim; Kakembo, Abbas S. L.; Welburn, Susan C.
2010-01-01
Background The persistent spread of Rhodesian human African trypanosomiasis (HAT) in Uganda in recent years has increased concerns of a potential overlap with the Gambian form of the disease. Recent research has aimed to increase the evidence base for targeting control measures by focusing on the environmental and climatic factors that control the spatial distribution of the disease. Objectives One recent study used simple logistic regression methods to explore the relationship between prevalence of Rhodesian HAT and several social, environmental and climatic variables in two of the most recently affected districts of Uganda, and suggested the disease had spread into the study area due to the movement of infected, untreated livestock. Here we extend this study to account for spatial autocorrelation, incorporate uncertainty in input data and model parameters and undertake predictive mapping for risk of high HAT prevalence in future. Materials and Methods Using a spatial analysis in which a generalised linear geostatistical model is used in a Bayesian framework to account explicitly for spatial autocorrelation and incorporate uncertainty in input data and model parameters we are able to demonstrate a more rigorous analytical approach, potentially resulting in more accurate parameter and significance estimates and increased predictive accuracy, thereby allowing an assessment of the validity of the livestock movement hypothesis given more robust parameter estimation and appropriate assessment of covariate effects. Results Analysis strongly supports the theory that Rhodesian HAT was imported to the study area via the movement of untreated, infected livestock from endemic areas. The confounding effect of health care accessibility on the spatial distribution of Rhodesian HAT and the linkages between the disease's distribution and minimum land surface temperature have also been confirmed via the application of these methods. Conclusions Predictive mapping indicates an
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.
Preliminary Modeling and Simulation Study on Olfactory Cell Sensation
NASA Astrophysics Data System (ADS)
Zhou, Jun; Yang, Wei; Chen, Peihua; Liu, Qingjun; Wang, Ping
2009-05-01
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.
Simulation of Soil Macropore Networks Using Multi-Point Geostatistics
NASA Astrophysics Data System (ADS)
Luo, L.; Lin, H.; Singha, K.
2006-12-01
Two-point correlation functions have been used to quantitatively evaluate the spatial variability of soil structure but cannot characterize its specific geometry. Multi-point (MP) geostatistics can be used to consider both spatial correlation and the specific shape of objects. The algorithm, SNESIM, developed by Strebelle (2002) was used to simulate complex geological patterns, reflecting different scales of variability and types of heterogeneity. Soil macropores, especially earthworm burrows and root channels, are critical to preferential flow and transport in soils. Accurate simulation of soil macropore network can allow us better simulate soil hydraulic properties and address scaling issues of structured soils. However, little work has been done to simulate soil macropore network using MP geostatistics. 3D soil macropore network of an agricultural soil in Pennsylvania, Hagerstown silt loam, was extracted from the spatially exhaustive data collected with Micro Computing Tomography. 3D training image was scanned by 3D template to obtain the 3D patterns. Permeability of the simulated macropore network was calculated using Lattice-Boltzmann method. SNESIM was able to simulate different types of macropores, the earthworm burrows and inter-aggregate pores. However, SNESIM requires that training images must have a stationary character, which may limit its ability to simulate soil macropore network. SNESIM is still very computationally consuming for the 3D structure simulation. While computationally expensive, MP geostatistics has great potential for simulating 3D soil macropore network, which is useful to understand and predict the hydraulic behavior of structured soils.
Assessment of Geostatistical Methods in Drought Monitoring Systems
NASA Astrophysics Data System (ADS)
Shahabfar, A.; Eitzinger, J.
2009-09-01
One of the essential components of drought risk management is drought monitoring and drought phenomenon has become a recurrent phenomenon in Iran in the last few decades. As the aim of construction of a drought monitoring system over Iran, in this paper according to last results that have been obtained by authors, three drought indices include China-Z index (CZI), modified CZI (MCZI), Z-Score which have high performance in detecting and measuring of drought intensity, have been calculated over 180 weather stations located in 10 separate agro-climatic zones in Iran. For finding, evaluating and refining an appropriate interpolation method, several geostatistical methods including ordinary kriging (Spherical, Circular, Exponential, Gaussian and Linear), Inverse Distance Weighed (IDW) and Spline have been applied and all of calculated drought indices have been interpolated over 10 different agro-climatic zones. The performance of the seven mentioned methods was evaluated and compared using the monthly data and the cross-validation technique. The comparison criterions were Mean Absolute Error (MAE) and Mean Biased Error (MBE). The results indicate that although ordinary kriging is the most accurate method but Inverse Distance Weighed and Spline have reasonable and more accurate results in several agro-climatic zones and can be used as high performance geostatistical tools for interpolation of different drought indices in Iran. Key words: drought monitoring, drought indices, geostatistical methods, interpolation.
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.
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.
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.
Reconstructing Holocene climate using a climate model: Model strategy and preliminary results
NASA Astrophysics Data System (ADS)
Haberkorn, K.; Blender, R.; Lunkeit, F.; Fraedrich, K.
2009-04-01
An Earth system model of intermediate complexity (Planet Simulator; PlaSim) is used to reconstruct Holocene climate based on proxy data. The Planet Simulator is a user friendly general circulation model (GCM) suitable for palaeoclimate research. Its easy handling and the modular structure allow for fast and problem dependent simulations. The spectral model is based on the moist primitive equations conserving momentum, mass, energy and moisture. Besides the atmospheric part, a mixed layer-ocean with sea ice and a land surface with biosphere are included. The present-day climate of PlaSim, based on an AMIP II control-run (T21/10L resolution), shows reasonable agreement with ERA-40 reanalysis data. Combining PlaSim with a socio-technological model (GLUES; DFG priority project INTERDYNAMIK) provides improved knowledge on the shift from hunting-gathering to agropastoral subsistence societies. This is achieved by a data assimilation approach, incorporating proxy time series into PlaSim to initialize palaeoclimate simulations during the Holocene. For this, the following strategy is applied: The sensitivities of the terrestrial PlaSim climate are determined with respect to sea surface temperature (SST) anomalies. Here, the focus is the impact of regionally varying SST both in the tropics and the Northern Hemisphere mid-latitudes. The inverse of these sensitivities is used to determine the SST conditions necessary for the nudging of land and coastal proxy climates. Preliminary results indicate the potential, the uncertainty and the limitations of the method.
ERIC Educational Resources Information Center
Rensselaer Research Corp., Troy, NY.
The purpose of this study was to develop the schema and methodology for the construction of a computerized mathematical model designed to project college and university enrollments in New York State and to meet the future increased demands of higher education planners. This preliminary report describes the main structure of the proposed computer…
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,…
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
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.
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
Semi-automated mapping of landforms using multiple point geostatistics
NASA Astrophysics Data System (ADS)
Vannametee, E.; Babel, L. V.; Hendriks, M. R.; Schuur, J.; de Jong, S. M.; Bierkens, M. F. P.; Karssenberg, D.
2014-09-01
This study presents an application of a multiple point geostatistics (MPS) to map landforms. MPS uses information at multiple cell locations including morphometric attributes at a target mapping cell, i.e. digital elevation model (DEM) derivatives, and non-morphometric attributes, i.e. landforms at the neighboring cells, to determine the landform. The technique requires a training data set, consisting of a field map of landforms and a DEM. Mapping landforms proceeds in two main steps. First, the number of cells per landform class, associated with a set of observed attributes discretized into classes (e.g. slope class), is retrieved from the training image and stored in a frequency tree, which is a hierarchical database. Second, the algorithm visits the non-mapped cells and assigns to these a realization of a landform class, based on the probability function of landforms conditioned to the observed attributes as retrieved from the frequency tree. The approach was tested using a data set for the Buëch catchment in the French Alps. We used four morphometric attributes extracted from a 37.5-m resolution DEM as well as two non-morphometric attributes observed in the neighborhood. The training data set was taken from multiple locations, covering 10% of the total area. The mapping was performed in a stochastic framework, in which 35 map realizations were generated and used to derive the probabilistic map of landforms. Based on this configuration, the technique yielded a map with 51.2% of correct cells, evaluated against the field map of landforms. The mapping accuracy is relatively high at high elevations, compared to the mid-slope and low-lying areas. Debris slope was mapped with the highest accuracy, while MPS shows a low capability in mapping hogback and glacis. The mapping accuracy is highest for training areas with a size of 7.5-10% of the total area. Reducing the size of the training images resulted in a decreased mapping quality, as the frequency database only
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
Preliminary mixed-layer model results for FIRE marine stratocumulus IFO conditions
NASA Technical Reports Server (NTRS)
Barlow, R.; Nicholls, S.
1990-01-01
Some preliminary results from the Turton and Nicholls mixed layer model using typical FIRE boundary conditions are presented. The model includes entrainment and drizzle parametrizations as well as interactive long and shortwave radiation schemes. A constraint on the integrated turbulent kinetic energy balance ensures that the model remains energetically consistent at all times. The preliminary runs were used to identify the potentially important terms in the heat and moisture budgets of the cloud layer, and to assess the anticipated diurnal variability. These are compared with typical observations from the C130. Sensitivity studies also revealed the remarkable stability of these cloud sheets: a number of negative feedback mechanisms appear to operate to maintain the cloud over an extended time period. These are also discussed. The degree to which such a modelling approach can be used to explain observed features, the specification of boundary conditions and problems of interpretation in non-horizontally uniform conditions is also raised.
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
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.
Expertise and sexual offending: a preliminary empirical model.
Bourke, Patrice; Ward, Tony; Rose, Chelsea
2012-08-01
Rehabilitation and treatment perspectives and interventions have concentrated efforts on areas where perpetrators of sexual abuse are deficient, neglecting those where offenders actively seek and strategically plan sexual offence situations and scenarios. Whereas sexual offenders may display deficiencies in some aspects of their lives, there are domain-relevant competencies such as the selection and manipulation of victims, decision making and problem solving, and eluding detection, in which some individuals appear to excel. Semistructured interviews are conducted with 47 male child sexual offenders in New Zealand, and data are analyzed using grounded theory to generate a model of offence-specific decision making. The outcome of the research is a descriptive model of expertise-related competency (ERC) of child sexual offending. The model identifies and emphasizes the variability of knowledge and skill acquisition among offenders.
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 geochemical/geophysical model of Yucca Mountain
Greenwade, L.E.; Cederberg, G.A.
1987-12-31
A comprehensive geochemical/geophysical model incorporates the current and relevant stratigraphic, petrologic, hydrogeologic, geochemical, and material data associated with a candidate repository at Yucca Mountain, Nevada. A geochemical/geophysical model will provide support and confidence to the Systems Performance calculations, determine whether the data collected as part of the site characterization provide the information needed by the design and performance assessment task, and provide the most accurate and referenced foundation on which to base the radionuclide transport calculations. In this report, the known repository data are compiled and unknown parameter values are estimated based on the available data. It is concluded that more data are needed before the geochemical/geophysical model of Yucca Mountain can be regarded as satisfactory and suitable base for multidimensional predicative flow and transport simulations. Recommendations for future studies concerning site characterization and data acquisition are presented. 36 refs., 1 fig., 2 tabs.
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.
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 Models of Conceptual Linkages among Proxemic Variables
ERIC Educational Resources Information Center
Evans, Gary W.; Eichelman, William
1976-01-01
Current models of human spatial behavior including stress, information overload, and micro-macro analysis are critically examined. An alternative functional orientation is developed which suggests that seeking to understand the adaptive value of various proxemic phenomena may provide some insight as to how the various proxemic variables are…
Simulating lightning into the RAMS model: implementation and preliminary results
NASA Astrophysics Data System (ADS)
Federico, S.; Avolio, E.; Petracca, M.; Panegrossi, G.; Sanò, P.; Casella, D.; Dietrich, S.
2014-05-01
This paper shows the results of a tailored version of a previously published methodology, designed to simulate lightning activity, implemented into the Regional Atmospheric Modeling System (RAMS). The method gives the flash density at the resolution of the RAMS grid-scale allowing for a detailed analysis of the evolution of simulated lightning activity. The system is applied in detail to two case studies occurred over the Lazio Region, in Central Italy. Simulations are compared with the lightning activity detected by the LINET network. The cases refer to two thunderstorms of different intensity. Results show that the model predicts reasonably well both cases and that the lightning activity is well reproduced especially for the most intense case. However, there are errors in timing and positioning of the convection, whose magnitude depends on the case study, which mirrors in timing and positioning errors of the lightning distribution. To assess objectively the performance of the methodology, standard scores are presented for four additional case studies. Scores show the ability of the methodology to simulate the daily lightning activity for different spatial scales and for two different minimum thresholds of flash number density. The performance decreases at finer spatial scales and for higher thresholds. The comparison of simulated and observed lighting activity is an immediate and powerful tool to assess the model ability to reproduce the intensity and the evolution of the convection. This shows the importance of the use of computationally efficient lightning schemes, such as the one described in this paper, in forecast models.
Preliminary Modulus and Breakage Calculations on Cellulose Models
Technology Transfer Automated Retrieval System (TEKTRAN)
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...
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.
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 .
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.
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.
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. PMID:11317721
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.
Simulating lightning into the RAMS model: implementation and preliminary results
NASA Astrophysics Data System (ADS)
Federico, S.; Avolio, E.; Petracca, M.; Panegrossi, G.; Sanò, P.; Casella, D.; Dietrich, S.
2014-11-01
This paper shows the results of a tailored version of a previously published methodology, designed to simulate lightning activity, implemented into the Regional Atmospheric Modeling System (RAMS). The method gives the flash density at the resolution of the RAMS grid scale allowing for a detailed analysis of the evolution of simulated lightning activity. The system is applied in detail to two case studies occurred over the Lazio Region, in Central Italy. Simulations are compared with the lightning activity detected by the LINET network. The cases refer to two thunderstorms of different intensity which occurred, respectively, on 20 October 2011 and on 15 October 2012. The number of flashes simulated (observed) over Lazio is 19435 (16231) for the first case and 7012 (4820) for the second case, and the model correctly reproduces the larger number of flashes that characterized the 20 October 2011 event compared to the 15 October 2012 event. There are, however, errors in timing and positioning of the convection, whose magnitude depends on the case study, which mirrors in timing and positioning errors of the lightning distribution. For the 20 October 2011 case study, spatial errors are of the order of a few tens of kilometres and the timing of the event is correctly simulated. For the 15 October 2012 case study, the spatial error in the positioning of the convection is of the order of 100 km and the event has a longer duration in the simulation than in the reality. To assess objectively the performance of the methodology, standard scores are presented for four additional case studies. Scores show the ability of the methodology to simulate the daily lightning activity for different spatial scales and for two different minimum thresholds of flash number density. The performance decreases at finer spatial scales and for higher thresholds. The comparison of simulated and observed lighting activity is an immediate and powerful tool to assess the model ability to reproduce the
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.
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.
Yates, M V; Yates, S R; Warrick, A W; Gerba, C P
1986-09-01
Water samples were collected from 71 public drinking-water supply wells in the Tucson, Ariz., basin. Virus decay rates in the water samples were determined with MS-2 coliphage as a model virus. The correlations between the virus decay rates and the sample locations were shown by fitting a spherical model to the experimental semivariogram. Kriging, a geostatistical technique, was used to calculate virus decay rates at unsampled locations by using the known values at nearby wells. Based on the regional characteristics of groundwater flow and the kriged estimates of virus decay rates, a contour map of the area was constructed. The map shows the variation in separation distances that would have to be maintained between wells and sources of contamination to afford similar degrees of protection from viral contamination of the drinking water in wells throughout the basin.
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 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.
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 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 Chaotic Model of Snapover on High Voltage Solar Cells
NASA Technical Reports Server (NTRS)
Mackey, Willie R.
1995-01-01
High voltage power systems in space will interact with the space plasma in a variety of ways. One of these, Snapover, is characterized by a sudden enlargement of the electron current collection area across normally insulating surfaces. A power drain on solar array power systems will results from this enhanced current collection. Optical observations of the snapover phenomena in the laboratory indicates a functional relation between bia potential and surface glow area. This paper shall explore the potential benefits of modeling the relation between current and bia potential as an aspect of bifurcation analysis in chaos theory. Successful characterizations of snapover as a chaotic phenomena may provide a means of snapover prevention and control through chaotic synchronization.
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.
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.
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
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.
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.
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
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
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...
NASA Astrophysics Data System (ADS)
Hashemi, Seyyedhossein; Javaherian, Abdolrahim; Ataee-pour, Majid; Khoshdel, Hossein
2014-12-01
Facies models try to explain facies architectures which have a primary control on the subsurface heterogeneities and the fluid flow characteristics of a given reservoir. In the process of facies modeling, geostatistical methods are implemented to integrate different sources of data into a consistent model. The facies models should describe facies interactions; the shape and geometry of the geobodies as they occur in reality. Two distinct categories of geostatistical techniques are two-point and multiple-point (geo) statistics (MPS). In this study, both of the aforementioned categories were applied to generate facies models. A sequential indicator simulation (SIS) and a truncated Gaussian simulation (TGS) represented two-point geostatistical methods, and a single normal equation simulation (SNESIM) selected as an MPS simulation representative. The dataset from an extremely channelized carbonate reservoir located in southwest Iran was applied to these algorithms to analyze their performance in reproducing complex curvilinear geobodies. The SNESIM algorithm needs consistent training images (TI) in which all possible facies architectures that are present in the area are included. The TI model was founded on the data acquired from modern occurrences. These analogies delivered vital information about the possible channel geometries and facies classes that are typically present in those similar environments. The MPS results were conditioned to both soft and hard data. Soft facies probabilities were acquired from a neural network workflow. In this workflow, seismic-derived attributes were implemented as the input data. Furthermore, MPS realizations were conditioned to hard data to guarantee the exact positioning and continuity of the channel bodies. A geobody extraction workflow was implemented to extract the most certain parts of the channel bodies from the seismic data. These extracted parts of the channel bodies were applied to the simulation workflow as hard data. This
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
Developing ontological model of computational linear algebra - preliminary considerations
NASA Astrophysics Data System (ADS)
Wasielewska, K.; Ganzha, M.; Paprzycki, M.; Lirkov, I.
2013-10-01
The aim of this paper is to propose a method for application of ontologically represented domain knowledge to support Grid users. The work is presented in the context provided by the Agents in Grid system, which aims at development of an agent-semantic infrastructure for efficient resource management in the Grid. Decision support within the system should provide functionality beyond the existing Grid middleware, specifically, help the user to choose optimal algorithm and/or resource to solve a problem from a given domain. The system assists the user in at least two situations. First, for users without in-depth knowledge about the domain, it should help them to select the method and the resource that (together) would best fit the problem to be solved (and match the available resources). Second, if the user explicitly indicates the method and the resource configuration, it should "verify" if her choice is consistent with the expert recommendations (encapsulated in the knowledge base). Furthermore, one of the goals is to simplify the use of the selected resource to execute the job; i.e., provide a user-friendly method of submitting jobs, without required technical knowledge about the Grid middleware. To achieve the mentioned goals, an adaptable method of expert knowledge representation for the decision support system has to be implemented. The selected approach is to utilize ontologies and semantic data processing, supported by multicriterial decision making. As a starting point, an area of computational linear algebra was selected to be modeled, however, the paper presents a general approach that shall be easily extendable to other domains.
Plante, T G; Booth, J
1995-06-01
This study investigated the association of nine biopsychosocial variables and athletic performance among 40 elite collegiate baseball players. High scores on confidence and perceived fitness and low scores on repressive denial, strength of religious faith, and sensitivity to glare were reliably associated with ratings of superior athletic performance by four coaches. Preliminary results suggest that the biopsychosocial model may prove useful in predicting athletic performance.
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.
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
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
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.
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
Terrain data conflation using an improved pattern-based multiple-point geostatistical approach
NASA Astrophysics Data System (ADS)
Tang, Yunwei; Zhang, Jingxiong; Li, Hui; Ding, Haifeng; Jing, Linhai
2014-11-01
The aim of data conflation is to synergise geospatial information from different sources into a common framework, which can be realised using multivariate geostatistics. Recently, multiple-point geostatistics (MPG) has been proposed for data conflation. Instead of the variogram, MPG borrows structures from the training image, so the spatial correlation is characterised by multiple-point statistics. In pattern-based MPG, two sets of data can be integrated by utilising the secondary data as a locally varying mean (LVM). The training image provides a spatial correlation model and is incorporated to facilitate reproduction of similar local patterns in the predicted image. However, the current patternbased MPG gathers similar patterns based on a prototype class, which extracts spatial structures in an arbitrary way. In this paper, we proposed an improved pattern-based MPG for conflation of digital elevation models (DEMs). In this approach, a new strategy for forming prototype class is applied, which is based on the residual surface, vector ruggedness measure (VRM) and ridge valley class (RVC) of terrain data. The method was tested on the SRTM and GMTED2010 data. SRTM data at the spatial resolution of 3 arc-second was simulated by conflating sparse elevation point data and GMTED2010 data at a coarser spatial resolution of 7.5 arc-second. The proposed MPG method was compared with the traditional pattern-based MPG simulation. Several kriging predictors were applied to provide LVMs for MPG simulation. The result shows that the new method can achieve more precise prediction and retain more spatial details than the benchmarks.
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.
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.
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.
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.
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)
Erdin, R.; Frei, C.; Sideris, I.; Kuensch, H.-R.
2010-09-01
gauge locations. These scores assess different characteristics such as bias, distinction between dry and wet areas (HK, SLEEPS), accuracy of values at wet locations (SCATTER) and overall performance (RMSE, MAD). Special attention is paid to the subject of appropriate case-dependent transformation of variables in order to fulfill model assumptions. Our analyses show that geostatistical merging techniques can provide significant added value compared to pure radar and pure rain gauge data - also in mountainous terrain. Yet, the high a-priori quality of the radar product may have been essential for the good performance of methods. The comparison between the two combination methods shows better results in general for KED, the more flexible of the two methods. However, there are features, such as the differentiation between wet and dry areas (HK), and situations, such as small isolated convective cells, where OKRE outperforms KED. Our discussion conveys interesting insights into the potential and limitations of the two analyzed methods and leads to suggestions for further improvements of combination techniques.
Ho, C.K.; Altman, S.J.; Arnold, B.W.
1995-09-01
Groundwater travel time (GWTT) calculations will play an important role in addressing site-suitability criteria for the potential high-level nuclear waste repository at Yucca Mountain,Nevada. In support of these calculations, Preliminary assessments of the candidate codes and models are presented in this report. A series of benchmark studies have been designed to address important aspects of modeling flow through fractured media representative of flow at Yucca Mountain. Three codes (DUAL, FEHMN, and TOUGH 2) are compared in these benchmark studies. DUAL is a single-phase, isothermal, two-dimensional flow simulator based on the dual mixed finite element method. FEHMN is a nonisothermal, multiphase, multidimensional simulator based primarily on the finite element method. TOUGH2 is anon isothermal, multiphase, multidimensional simulator based on the integral finite difference method. Alternative conceptual models of fracture flow consisting of the equivalent continuum model (ECM) and the dual permeability (DK) model are used in the different codes.
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.
Preliminary testing of turbulence and radionuclide transport modeling in deep ocean environment
Onishi, Y.; Dummuller, D.C.; Trent, D.S.; Washington State Univ., Pullman, WA; Pacific Northwest Lab., Richland, WA )
1989-03-01
Pacific Northwest Laboratory (PNL) performed a study for the US Environmental Protection Agency's Office of Radiation Programs to (1) identify candidate models for regional modeling of low-level waste ocean disposal sites in the mid-Atlantic ocean; (2) evaluate mathematical representation of the model's eddy viscosity/dispersion coefficients; and (3) evaluate the adequacy of the k-{epsilon} turbulence model and the feasibility of one of the candidate models, TEMPEST{copyright}/FLESCOT{copyright}, to deep-ocean applications on a preliminary basis. PNL identified the TEMPEST{copyright}/FLESCOT{copyright}, FLOWER, Blumberg's, and RMA 10 models as appropriate candidates for the regional radionuclide modeling. Among these models, TEMPEST/FLESCOT is currently the only model that solves distributions of flow, turbulence (with the k-{epsilon} model), salinity, water temperature, sediment, dissolved contaminants, and sediment-sorbed contaminants. Solving the Navier-Stokes equations using higher order correlations is not practical for regional modeling because of the prohibitive computational requirements; therefore, the turbulence modeling is a more practical approach. PNL applied the three-dimensional code, TEMPEST{copyright}/FLESCOT{copyright} with the k-{epsilon} model, to a very simple, hypothetical, two-dimensional, deep-ocean case, producing at least qualitatively appropriate results. However, more detailed testing should be performed for the further testing of the code. 46 refs., 39 figs., 6 tabs.
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.
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.
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
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.
Indoor radon variations in central Iran and its geostatistical map
NASA Astrophysics Data System (ADS)
Hadad, Kamal; Mokhtari, Javad
2015-02-01
We present the results of 2 year indoor radon survey in 10 cities of Yazd province in Central Iran (covering an area of 80,000 km2). We used passive diffusive samplers with LATEX polycarbonate films as Solid State Nuclear Track Detector (SSNTD). This study carried out in central Iran where there are major minerals and uranium mines. Our results indicate that despite few extraordinary high concentrations, average annual concentrations of indoor radon are within ICRP guidelines. When geostatistical spatial distribution of radon mapped onto geographical features of the province it was observed that risk of high radon concentration increases near the Saqand, Bafq, Harat and Abarkooh cities, this depended on the elevation and vicinity of the ores and mines.
Multivariate geostatistical simulation by minimising spatial cross-correlation
NASA Astrophysics Data System (ADS)
Sohrabian, Babak; Tercan, Abdullah Erhan
2014-03-01
Joint simulation of attributes in multivariate geostatistics can be achieved by transforming spatially correlated variables into independent factors. In this study, a new approach for this transformation, Minimum Spatial Cross-correlation (MSC) method, is suggested. The method is based on minimising the sum of squares of cross-variograms at different distances. In the approach, the problem in higher space (N × N) is reduced to N×N-1/2 problems in the two-dimensional space and the reduced problem is solved iteratively using Gradient Descent Algorithm. The method is applied to the joint simulation of a set of multivariate data in a marble quarry and the results are compared with Minimum/Maximum Autocorrelation Factors (MAF) method.
Geostatistical analyses reveal nutrient-vegetation relationships in savanna soils
NASA Astrophysics Data System (ADS)
Mladenov, N.; Okin, G. S.; Cassel, D.; Caylor, K. C.; Clausen, K. M.
2005-12-01
The uniform Kalahari sands that underlay the Kalahari Transect (KT) are a unique test bed for examining soil nutrient and vegetation cover relationships in a savanna ecosystem. Transects (300m x 100m) were sampled and mapped for each of four sites located along an aridity gradient from Mongu, Zambia in the north (1600 mm/yr precipitation), to Tshane, Botswana in the south (250 mm/yr precipitation). Geostatistical analyses of soil chemistry and tree, shrub, and grass cover at four sites along the moisture gradient of the KT indicated an accumulation of nutrients below tree/shrub cover. In general, areas where grasses dominated between canopies were negatively correlated with soil nutrients at the drier sites. Here we examine effects of vegetation cover on soil productivity and differences in soil nutrients along the moisture gradient of the KT.
Principal Component Geostatistical Approach for large-dimensional inverse problems
Kitanidis, P K; Lee, J
2014-01-01
The quasi-linear geostatistical approach is for weakly nonlinear underdetermined inverse problems, such as Hydraulic Tomography and Electrical Resistivity Tomography. It provides best estimates as well as measures for uncertainty quantification. However, for its textbook implementation, the approach involves iterations, to reach an optimum, and requires the determination of the Jacobian matrix, i.e., the derivative of the observation function with respect to the unknown. Although there are elegant methods for the determination of the Jacobian, the cost is high when the number of unknowns, m, and the number of observations, n, is high. It is also wasteful to compute the Jacobian for points away from the optimum. Irrespective of the issue of computing derivatives, the computational cost of implementing the method is generally of the order of m2n, though there are methods to reduce the computational cost. In this work, we present an implementation that utilizes a matrix free in terms of the Jacobian matrix Gauss-Newton method and improves the scalability of the geostatistical inverse problem. For each iteration, it is required to perform K runs of the forward problem, where K is not just much smaller than m but can be smaller that n. The computational and storage cost of implementation of the inverse procedure scales roughly linearly with m instead of m2 as in the textbook approach. For problems of very large m, this implementation constitutes a dramatic reduction in computational cost compared to the textbook approach. Results illustrate the validity of the approach and provide insight in the conditions under which this method perform best. PMID:25558113
Principal Component Geostatistical Approach for large-dimensional inverse problems
NASA Astrophysics Data System (ADS)
Kitanidis, P. K.; Lee, J.
2014-07-01
The quasi-linear geostatistical approach is for weakly nonlinear underdetermined inverse problems, such as Hydraulic Tomography and Electrical Resistivity Tomography. It provides best estimates as well as measures for uncertainty quantification. However, for its textbook implementation, the approach involves iterations, to reach an optimum, and requires the determination of the Jacobian matrix, i.e., the derivative of the observation function with respect to the unknown. Although there are elegant methods for the determination of the Jacobian, the cost is high when the number of unknowns, m, and the number of observations, n, is high. It is also wasteful to compute the Jacobian for points away from the optimum. Irrespective of the issue of computing derivatives, the computational cost of implementing the method is generally of the order of m2n, though there are methods to reduce the computational cost. In this work, we present an implementation that utilizes a matrix free in terms of the Jacobian matrix Gauss-Newton method and improves the scalability of the geostatistical inverse problem. For each iteration, it is required to perform K runs of the forward problem, where K is not just much smaller than m but can be smaller that n. The computational and storage cost of implementation of the inverse procedure scales roughly linearly with m instead of m2 as in the textbook approach. For problems of very large m, this implementation constitutes a dramatic reduction in computational cost compared to the textbook approach. Results illustrate the validity of the approach and provide insight in the conditions under which this method perform best.
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.
Heterosexual male perpetrators of childhood sexual abuse: a preliminary neuropsychiatric model.
Cohen, Lisa J; Nikiforov, Konstantin; Gans, Sniezyna; Poznansky, Olga; McGeoch, Pamela; Weaver, Carrie; King, Enid Gertmanian; Cullen, Ken; Galynker, Igor
2002-01-01
This paper presents data from a series of preliminary neuropsychiatric studies, including neuropsychological, personality, sexual history, plethysmographic and neuroimaging investigations, on a sample of 22 male, heterosexual, nonexclusive pedophiles and 24 demographically similar healthy controls. A psychobiological model of pedophilia is proposed, positing that early childhood sexual abuse leads to neurodevelopmental abnormalities in the temporal regions mediating sexual arousal and erotic discrimination and the frontal regions mediating the cognitive aspects of sexual desire and behavioral inhibition. In this way, pedophiles develop deviant pedophilic arousal. Subsequently, if there is comorbid personality pathology, specifically sociopathy and cognitive distortions, there will be failure to inhibit pedophilic behavior. PMID:12418359
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.
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.
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
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 Astrophysics Data System (ADS)
Parker, L. N.; Zank, G. P.
2013-12-01
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 (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).
NASA Astrophysics Data System (ADS)
Kumari, Madhuri; Singh, Chander Kumar; Bakimchandra, Oinam; Basistha, Ashoke
2016-07-01
In mountainous region with heterogeneous topography, the geostatistical modeling of the rainfall using global data set may not confirm to the intrinsic hypothesis of stationarity. This study was focused on improving the precision of the interpolated rainfall maps by spatial stratification in complex terrain. Predictions of the normal annual rainfall data were carried out by ordinary kriging, universal kriging, and co-kriging, using 80-point observations in the Indian Himalayas extending over an area of 53,484 km2. A two-step spatial clustering approach is proposed. In the first step, the study area was delineated into two regions namely lowland and upland based on the elevation derived from the digital elevation model. The delineation was based on the natural break classification method. In the next step, the rainfall data was clustered into two groups based on its spatial location in lowland or upland. The terrain ruggedness index (TRI) was incorporated as a co-variable in co-kriging interpolation algorithm. The precision of the kriged and co-kriged maps was assessed by two accuracy measures, root mean square error and Chatfield's percent better. It was observed that the stratification of rainfall data resulted in 5-20 % of increase in the performance efficiency of interpolation methods. Co-kriging outperformed the kriging models at annual and seasonal scale. The result illustrates that the stratification of the study area improves the stationarity characteristic of the point data, thus enhancing the precision of the interpolated rainfall maps derived using geostatistical methods.
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.
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
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
2012-03-01
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.
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.
Implementation of DSSAT CERES-maize model in CWRF and off-line preliminary results
NASA Astrophysics Data System (ADS)
Xu, M.; Liang, X.; Gao, W.
2009-12-01
To study the crop-climate interactions, the CERES (Crop-Environment REsource Synthesis) maize growth model in DSSAT (Decision Support System for Agrotechnology Transfer) was recoded in software to follow the modular FORTRAN90 standard and improved in physics representation to facilitate full coupling with a state-of-the-art regional climate model CWRF (Climate extension of the Weather Research and Forecasting model). The new CWRF CERES-maize module has many advantages compared with the original one that include: (1) the upgrade from the original point crop growth model to advanced spatial two-dimension model makes it possible to couple with complex climate models such as CWRF; (2) the modular structure instead of a number of subroutines makes it easy and flexible to couple with external models such as climate and impact models to build a comprehensive impact modeling systems; (3) the advanced data structures including the derived data type and pointer make it easy to update and implement the latest and advanced argotechnologies; (4) the parallel computing skills implemented in the new module improve its performance in simulations over a large spatial area with fine grid spaces. In this study, the CERES-maize module was driven by the NARR (North American Regional Reanalysis) meteorological data to simulate the maize yield in the U. S. Midwest. The preliminary results show that the simulated yields agree well with the observed data. Further validations and improvements will be applied to CWRF-maize coupling system in near future.
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.
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
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).
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.
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 (HO). 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 HO 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
Niemi, A.; Bodvarsson, G.S.
1991-11-01
As part of the code development and modeling work being carried out to characterize the flow in the unsaturated zone at Yucca Mountain, Nevada, capillary hysteresis models simulating the history-dependence of the characteristic curves have been developed. The objective of the work has been both to develop the hysteresis models, as well as to obtain some preliminary estimates of the possible hysteresis effects in the fractured rocks at Yucca Mountain given the limitations of presently available data. Altogether three different models were developed based on work of other investigators reported in the literature. In these three models different principles are used for determining the scanning paths: in model (1) the scanning paths are interpolated from tabulated first-order scanning curves, in model (2) simple interpolation functions are used for scaling the scanning paths from the expressions of the main wetting and main drying curves and in model (3) the scanning paths are determined from expressions derived based on the dependent domain theory of hysteresis.
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.
Ensemble variational data assimilation with a shallow-water model : preliminary results
NASA Astrophysics Data System (ADS)
Brajard, Julien; Sirven, Jérôme; Talagrand, Olivier
2016-04-01
The objective of ensemble data assimilation is to produce an ensemble of analysis from observations and a numerical model which is representative of the uncertainty of the system. In a bayesian framework, the ensemble represents a sampling of the state vector probability distribution conditioned to the available knowledge of the system, denoted the a-posteriori probability distribution. Ensemble variational data assimilation (EnsVar) consists in producing such an ensemble by perturbating N times the observations according to their error law, and run a standard variationnal assimilation for each perturbation. An ensemble of N members is then produced. In the case of linear models, there is a theoretical guarantee that this ensemble is a sampling of the a-posteriori probability. But there is no theoretical result in the non-linear case. Numerical experiments using non-linear numerical models suggest that the conclusion reached for linear models still stands for non linear toy models. The objective of the present work is to show preliminary results of EnsVar applied to a more realistic model : a shallow-water model. Some statistical properties of the ensemble are presented, and the sensitivity to the main features of the assimilation system (number, distribution of observations, size of the assimilation window, ...) are also studied.
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.
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
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. PMID:19046937
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.
A preliminary model for predicting heavy metal contaminant loading from an urban catchment.
Yuan, Y; Hall, K; Oldham, C
2001-02-01
The toxicity of heavy metals to biota in urban catchments has been regarded as a very important non-point source pollution issue. Numerous studies on heavy metal pollution in urban receiving waters have found that metal transport by surface runoff is closely correlated to the partitioning of the metal forms between dissolved and particulate phases, where sediment plays an important role in the transport process. Sediment cycling on urban streets, metal binding form, and rainfall character in the catchment area are considered to be the key factors for metal transport. A preliminary model is developed based on these considerations. Starting from classical build-up and wash-off processes for the suspended sediment (SS) on the urban impervious surface, the model links the transport of suspended sediment to the transport of metal species. Monitoring data from a small highway catchment were used in the model development. A total of 47 rain events over 1 year were monitored intensively at short time intervals (5-10 min) for hydrological data, rainfall intensity, and stormwater quality. In developing the model, lead was used for the metal load prediction, as it has been a common fuel additive for urban transportation. Agreement between model results and monitoring data indicates that the model can be used in predicting metal load from impervious urban areas, such as streets and roadways, on a long-term basis.
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.
NASA Astrophysics Data System (ADS)
Khairoutdinov, Marat F.; Randall, David A.
Preliminary results of a short climate simulation with a 2-D cloud resolving model (CRM) installed into each grid column of an NCAR Community Climate System Model (CCSM) are presented. The CRM replaces the conventional convective and stratiform cloud parameterizations, and allows for explicit computation of the global cloud fraction distribution for radiation computations. The extreme computational cost of the combined CCSM/CRM model has thus far limited us to a two-month long climate simulation (December-January) using 2.8° × 2.8° resolution. The simulated geographical distributions of the total rainfall, precipitable water, cloud cover, and Earth radiation budget, for the month of January, look very reasonable.
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
Geostatistical techniques to assess the influence of soil density on sugarcane productivity
NASA Astrophysics Data System (ADS)
Marques, Karina; Silva, Wellington; Almeida, Ceres; Bezerra, Joel; Almeida, Brivaldo; Siqueira, Glecio
2013-04-01
Spatial variation in some soil properties on small distances occur even on homogeneous areas with same soil class that can influence to crop productivity. This variability must be incorporated into the procedures and techniques used in agriculture. Thus, it is necessary to know it to optimize agricultural practices. This study aimed to evaluate the influence of soil density on the sugarcane productivity by geostatistical techniques. The area is located on Rio Formoso city, Pernambuco (Brazil), at latitude 08°38'91"S and longitude 35°16'08"W, where the climate is rainy tropical. About of 243 georeferenced undisturbed soil samples (clods) were collected on lowland area at three depths (0-20, 20-40 and 40-60cm) grid spacing of 15 x 30 m. The total area has 7.5 ha, divided equally into three subareas. Statistical and geostatistics analysis were done. It was found that soil density increased with depth Bulk density and can be used as an index of relative compaction. Machine weight, track or tire design and soil water content at the time of traffic are some of the factors that determine the amount of soil compaction and resulting changes in the plant root environment. These points can have influenced the highest soil density found in subarea 1. This subarea was intensively mechanized and it presents poor drainage and seasonal flood. Based on semivariograms models fitted, we can say that soil density showed spatial dependence in subarea 1 at all depths (Gaussian (0-20cm) and spherical both 20-40 and 40-60cm). Unlike this, the models fitted to subarea 2 were to 0-20 and 40-60cm depths, exponential and on subarea 3, at 0-20cm (Gaussian). Pure nugget effect was found on 20-40cm depth at the subareas 2 and 3, and 40-60cm on the subarea 3. Subarea 1 had higher soil density and lower sugarcane productivity thus, it is known that root development and nutrient uptake are directly influenced by soil density.
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.
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.
Soil Organic Carbon Mapping by Geostatistics in Europe Scale
NASA Astrophysics Data System (ADS)
Aksoy, E.; Panagos, P.; Montanarella, L.
2013-12-01
Accuracy in assessing the distribution of soil organic carbon (SOC) is an important issue because SOC is an important soil component that plays key roles in the functions of both natural ecosystems and agricultural systems. The SOC content varies from place to place and it is strongly related with climate variables (temperature and rainfall), terrain features, soil texture, parent material, vegetation, land-use types, and human management (management and degradation) at different spatial scales. Geostatistical techniques allow for the prediction of soil properties using soil information and environmental covariates. In this study, assessment of SOC distribution has been predicted with Regression-Kriging method in Europe scale. In this prediction, combination of the soil samples which were collected from the LUCAS (European Land Use/Cover Area frame statistical Survey) & BioSoil Projects, with local soil data which were collected from six different CZOs in Europe and ten spatial predictors (slope, aspect, elevation, CTI, CORINE land-cover classification, parent material, texture, WRB soil classification, annual average temperature and precipitation) were used. Significant correlation between the covariates and the organic carbon dependent variable was found. Moreover, investigating the contribution of local dataset in watershed scale into regional dataset in European scale was an important challenge.
Eastern gas shales geostatistical lineament analysis. Final report
Pratt, S.; Robey, E.; Wojewodka, R.
1986-12-01
A geostatistical analysis was conducted to determine if proximity to lineaments affects the production of Devonian shale gas wells. Production data, including open flow and 10-year cumulative production, were gathered for 847 gas wells in three areas of different tectonic stress: southwestern West Virginia, eastern Meigs County, Ohio, and Floyd county, Kentucky. Three experienced mappers were contracted to independently identify lineaments in each study area for the purpose of comparing the mappers and their techniques. Data pertaining to the proximity and density of lineaments near each well were collected from maps provided by each contractor. Gas production was found not to be consistently associated with the proximity of lineaments nor linement intersections. The lack of correlation may be attributed to the fact that the corresponding maps provided by each contractor were vastly different. These difference may account for inconsistent findings of past lineament studies. The lack of observable relationships between lineaments and production, coupled with the degree of disparity in the lineaments seen by three experienced mappers, indicated many viable avenues for future research. The analysis clearly shows a need for more accurate and systematic methods of mapping lineaments before they become useful for gas exploration. 26 refs., 11 figs., 5 tabs.
NASA Astrophysics Data System (ADS)
Kravitz, B.; Robock, A.; Tilmes, S.; Boucher, O.; English, J. M.; Irvine, P. J.; Jones, A.; Lawrence, M. G.; MacCracken, M.; Muri, H.; Moore, J. C.; Niemeier, U.; Phipps, S. J.; Sillmann, J.; Storelvmo, T.; Wang, H.; Watanabe, S.
2015-06-01
We present a suite of new climate model experiment designs for the Geoengineering Model Intercomparison Project (GeoMIP). This set of experiments, named GeoMIP6 (to be consistent with the Coupled Model Intercomparison Project Phase 6), builds on the previous GeoMIP simulations, and has been expanded to address several further important topics, including key uncertainties in extreme events, the use of geoengineering as part of a portfolio of responses to climate change, and the relatively new idea of cirrus cloud thinning to allow more longwave radiation to escape to space. We discuss experiment designs, as well as the rationale for those designs, showing preliminary results from individual models when available. We also introduce a new feature, called the GeoMIP Testbed, which provides a platform for simulations that will be performed with a few models and subsequently assessed to determine whether the proposed experiment designs will be adopted as core (Tier 1) GeoMIP experiments. This is meant to encourage various stakeholders to propose new targeted experiments that address their key open science questions, with the goal of making GeoMIP more relevant to a broader set of communities.
Kravitz, Benjamin S.; Robock, Alan; Tilmes, S.; Boucher, Olivier; English, J.; Irvine, Peter; Jones, Andrew; Lawrence, M. G.; Maccracken, Michael C.; Muri, Helene O.; Moore, John; Niemeier, Ulrike; Phipps, Steven; Sillmann, Jana; Storelvmo, Trude; Wang, Hailong; Watanabe, Shingo
2015-10-27
We present a suite of new climate model experiment designs for the Geoengineering Model Intercomparison Project (GeoMIP). This set of experiments, named GeoMIP6 (to be consistent with the Coupled Model Intercomparison Project Phase 6), builds on the previous GeoMIP project simulations, and has been expanded to address several further important topics, including key uncertainties in extreme events, the use of geoengineering as part of a portfolio of responses to climate change, and the relatively new idea of cirrus cloud thinning to allow more longwave radiation to escape to space. We discuss experiment designs, as well as the rationale for those designs, showing preliminary results from individual models when available. We also introduce a new feature, called the GeoMIP Testbed, which provides a platform for simulations that will be performed with a few models and subsequently assessed to determine whether the proposed experiment designs will be adopted as core (Tier 1) GeoMIP experiments. This is meant to encourage various stakeholders to propose new targeted experiments that address their key open science questions, with the goal of making GeoMIP more relevant to a broader set of communities.
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.
Kravitz, Benjamin S.; Robock, Alan; Tilmes, S.; Boucher, Olivier; English, J. M.; Irvine, Peter J.; Jones, Andrew; Lawrence, M. G.; MacCracken, Michael C.; Muri, Helene O.; et al
2015-10-27
We present a suite of new climate model experiment designs for the Geoengineering Model Intercomparison Project (GeoMIP). This set of experiments, named GeoMIP6 (to be consistent with the Coupled Model Intercomparison Project Phase 6), builds on the previous GeoMIP project simulations, and has been expanded to address several further important topics, including key uncertainties in extreme events, the use of geoengineering as part of a portfolio of responses to climate change, and the relatively new idea of cirrus cloud thinning to allow more long wave radiation to escape to space. We discuss experiment designs, as well as the rationale formore » those designs, showing preliminary results from individual models when available. We also introduce a new feature, called the GeoMIP Testbed, which provides a platform for simulations that will be performed with a few models and subsequently assessed to determine whether the proposed experiment designs will be adopted as core (Tier 1) GeoMIP experiments. In conclusion, this is meant to encourage various stakeholders to propose new targeted experiments that address their key open science questions, with the goal of making GeoMIP more relevant to a broader set of communities.« less
Kravitz, Benjamin S.; Robock, Alan; Tilmes, S.; Boucher, Olivier; English, J. M.; Irvine, Peter J.; Jones, Andrew; Lawrence, M. G.; MacCracken, Michael C.; Muri, Helene O.; Moore, John C.; Niemeier, Ulrike; Phipps, Steven J.; Sillmann, Jana; Storelvmo, Trude; Wang, Hailong; Watanabe, Shingo
2015-10-27
We present a suite of new climate model experiment designs for the Geoengineering Model Intercomparison Project (GeoMIP). This set of experiments, named GeoMIP6 (to be consistent with the Coupled Model Intercomparison Project Phase 6), builds on the previous GeoMIP project simulations, and has been expanded to address several further important topics, including key uncertainties in extreme events, the use of geoengineering as part of a portfolio of responses to climate change, and the relatively new idea of cirrus cloud thinning to allow more long wave radiation to escape to space. We discuss experiment designs, as well as the rationale for those designs, showing preliminary results from individual models when available. We also introduce a new feature, called the GeoMIP Testbed, which provides a platform for simulations that will be performed with a few models and subsequently assessed to determine whether the proposed experiment designs will be adopted as core (Tier 1) GeoMIP experiments. In conclusion, this is meant to encourage various stakeholders to propose new targeted experiments that address their key open science questions, with the goal of making GeoMIP more relevant to a broader set of communities.
Anderson, Christine A; Whall, Ann L
2013-10-01
Opinion leaders are informal leaders who have the ability to influence others' decisions about adopting new products, practices or ideas. In the healthcare setting, the importance of translating new research evidence into practice has led to interest in understanding how opinion leaders could be used to speed this process. Despite continued interest, gaps in understanding opinion leadership remain. Agent-based models are computer models that have proven to be useful for representing dynamic and contextual phenomena such as opinion leadership. The purpose of this paper is to describe the work conducted in preparation for the development of an agent-based model of nursing opinion leadership. The aim of this phase of the model development project was to clarify basic assumptions about opinions, the individual attributes of opinion leaders and characteristics of the context in which they are effective. The process used to clarify these assumptions was the construction of a preliminary nursing opinion leader model, derived from philosophical theories about belief formation.
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.
NASA Astrophysics Data System (ADS)
Kravitz, B.; Robock, A.; Tilmes, S.; Boucher, O.; English, J. M.; Irvine, P. J.; Jones, A.; Lawrence, M. G.; MacCracken, M.; Muri, H.; Moore, J. C.; Niemeier, U.; Phipps, S. J.; Sillmann, J.; Storelvmo, T.; Wang, H.; Watanabe, S.
2015-10-01
We present a suite of new climate model experiment designs for the Geoengineering Model Intercomparison Project (GeoMIP). This set of experiments, named GeoMIP6 (to be consistent with the Coupled Model Intercomparison Project Phase 6), builds on the previous GeoMIP project simulations, and has been expanded to address several further important topics, including key uncertainties in extreme events, the use of geoengineering as part of a portfolio of responses to climate change, and the relatively new idea of cirrus cloud thinning to allow more longwave radiation to escape to space. We discuss experiment designs, as well as the rationale for those designs, showing preliminary results from individual models when available. We also introduce a new feature, called the GeoMIP Testbed, which provides a platform for simulations that will be performed with a few models and subsequently assessed to determine whether the proposed experiment designs will be adopted as core (Tier 1) GeoMIP experiments. This is meant to encourage various stakeholders to propose new targeted experiments that address their key open science questions, with the goal of making GeoMIP more relevant to a broader set of communities.
Preliminary evidence for an emotion dysregulation model of generalized anxiety disorder.
Mennin, Douglas S; Heimberg, Richard G; Turk, Cynthia L; Fresco, David M
2005-10-01
Three studies provide preliminary support for an emotion dysregulation model of generalized anxiety disorder (GAD). In study 1, students with GAD reported heightened intensity of emotions, poorer understanding of emotions, greater negative reactivity to emotional experience, and less ability to self-soothe after negative emotions than controls. A composite emotion regulation score significantly predicted the presence of GAD, after controlling for worry, anxiety, and depressive symptoms. In study 2, these findings were largely replicated with a clinical sample. In study 3, students with GAD, but not controls, displayed greater increases in self-reported physiological symptoms after listening to emotion-inducing music than after neutral mood induction. Further, GAD participants had more difficulty managing their emotional reactions. Implications for GAD and psychopathology in general are discussed.
A Combination of Preliminary Electroweak Measurements and Constraints on the Standard Model
Su, Dong
2003-05-15
This note presents a combination of published and preliminary electroweak results from the four LEP collaborations and the SLD collaboration which were prepared for the 1997 summer conferences. Averages are derived for hadronic and leptonic cross-sections, the leptonic forward-backward asymmetries, the {tau} polarisation asymmetries, the b{bar b} and c{bar c} partial widths and forward-backward asymmetries and the q{bar q} charge asymmetry. The major changes with respect to results presented last year are updated results of A{sub LR} from SLD, and the inclusion of the first direct measurements of the W mass and triple-gauge-boson couplings performed at LEP. The results are compared with precise electroweak measurements from other experiments. The parameters of the Standard Model are evaluated, first using the combined LEP electroweak measurements, and then using the full set of electroweak results.
Ice-sheet driven enhancement of geothermal flux: preliminary model results
NASA Astrophysics Data System (ADS)
Stevens, N. T.; Parizek, B. R.; Alley, R. B.; Pollard, D.; Anandakrishnan, S.
2013-12-01
Previous observations in parts of West Antarctica and Greenland, including near the head of the Northeast Greenland Ice Stream, have indicated rapid basal melting, suggesting higher geothermal flux than typical for the expected geological setting. Growth and decay of ice sheets over ice-age cycles cause large and geologically rapid changes in loading and flexure beneath and nearby. Oscillating load will cause oscillating melt volume in deep rocks, and because melt extraction increases with melt volume more rapidly than linearly, ice-age cycling will tend to move melt upward. Melt motion may be greatly aided by fracturing promoted by flexural stresses from the varying ice sheets. Preliminary results from ice-sheet models coupled to lithosphere and asthenosphere will be presented, suggesting that ice-sheet changes may be affecting their basal heat flux.
Preliminary Modeling of Two-Phase Flow at the Main Endeavour Vent Field
NASA Astrophysics Data System (ADS)
Singh, S.; Lowell, R. P.
2011-12-01
The high temperature hydrothermal vents of Main Endeavour Field (MEF), Juan de Fuca ridge exhibited quasi-steady North-South trending spatial gradients of both temperature and salinity for more than a decade before a magmatic event changed the vent characteristics. In order to explain these observations, we construct two-dimensional numerical models of two-phase hydrothermal flow of the MEF. We consider both along-axis and across-axis simulations, taking into account the vent field geometry and incorporating various parameters, such as different basal temperature distributions and permeability structures that might affect the vent fluid temperature and chemistry. Preliminary results from across-axis models, in which the basal temperature decreases linearly away from the ridge axis and results in a single high-temperature plume, indicate that basal temperature alone does not affect steady-state vent temperature and salinity of the vents. Simulations that include the presence of a high-permeability extrusive layer 2A atop the spreading ridge results in a zone of narrower and lower temperature venting. The effect of a low permeability zone of anhydrite would tend to mitigate the decrease in temperature, however. Along-axis simulations performed to date, with an extended uniform high temperature basal boundary, produce multiple plumes; but the plumes do not exhibit a strong along-axis gradient in vent salinity or temperature as observed at the MEF. These preliminary results suggest that the observed N-S gradient in temperature and salinity at MEF reflects interplay between heat source and either near the surface or deep-seated heterogeneous permeability structures. Three-dimensional simulations might ultimately be required to understand hydrothermal circulation at the MEF.
NASA Astrophysics Data System (ADS)
Cross, A. J.; Prior, D. J.; Ellis, S. M.
2012-12-01
It is widely accepted that changes in stress and grain size can induce a switch between grain-size insensitive (GSI) and sensitive (GSS) creep mechanisms. Under steady-state conditions, grains evolve to an equilibrium size in the boundary region between GSS and GSI, described by the paleopiezometer for a given material. Under these conditions, significant rheological weakening is not expected, as grain size reduction processes are balanced by grain growth processes. However, it has been shown that the stress field surrounding faults varies through the seismic cycle, with both rapid loading and unloading of stress possible in the co- and post-seismic stages. We propose that these changes in stress in the region of the brittle-ductile transition zone may be sufficient to force a deviation from the GSI-GSS boundary and thereby cause a change in grain size and creep mechanism prior to system re-equilibration. Here we present preliminary findings from numerical modelling of stress and grain size changes in response to loading of mechanical inhomogeneities. Our results are attained using a grain-size evolution (GSE) subroutine incorporated into the SULEC finite-element code developed by Susan Ellis and Susanne Buiter, which utilises an iterative approach of solving for spatial and temporal changes in differential stress, grain size and active creep mechanism. Preliminary models demonstrate that stress changes in response to the opening of a fracture in a flowing medium can be significant enough to cause a switch from GSI to GSS creep. These results are significant in the context of understanding spatial variations and feedback between stress, grain size and deformation mechanisms through the seismic cycle.
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.
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.
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.
Geostatistical estimates of future recharge for the Death Valley region
Hevesi, J.A.; Flint, A.L.
1998-12-01
Spatially distributed estimates of regional ground water recharge rates under both current and potential future climates are needed to evaluate a potential geologic repository for high-level nuclear waste at Yucca Mountain, Nevada, which is located within the Death Valley ground-water region (DVGWR). Determining the spatial distribution of recharge is important for regional saturated-zone ground-water flow models. In the southern Nevada region, the Maxey-Eakin method has been used for estimating recharge based on average annual precipitation. Although this method does not directly account for a variety of location-specific factors which control recharge (such as bedrock permeability, soil cover, and net radiation), precipitation is the primary factor that controls in the region. Estimates of recharge obtained by using the Maxey-Eakin method are comparable to estimates of recharge obtained by using chloride balance studies. The authors consider the Maxey-Eakin approach as a relatively simple method of obtaining preliminary estimates of recharge on a regional scale.
Preliminary Results from Electric Arc Furnace Off-Gas Enthalpy Modeling
Nimbalkar, Sachin U; Thekdi, Arvind; Keiser, James R; Storey, John Morse
2015-01-01
This article describes electric arc furnace (EAF) off-gas enthalpy models developed at Oak Ridge National Laboratory (ORNL) to calculate overall heat availability (sensible and chemical enthalpy) and recoverable heat values (steam or power generation potential) for existing EAF operations and to test ORNL s new EAF waste heat recovery (WHR) concepts. ORNL s new EAF WHR concepts are: Regenerative Drop-out Box System and Fluidized Bed System. The two EAF off-gas enthalpy models described in this paper are: 1.Overall Waste Heat Recovery Model that calculates total heat availability in off-gases of existing EAF operations 2.Regenerative Drop-out Box System Model in which hot EAF off-gases alternately pass through one of two refractory heat sinks that store heat and then transfer it to another gaseous medium These models calculate the sensible and chemical enthalpy of EAF off-gases based on the off-gas chemical composition, temperature, and mass flow rate during tap to tap time, and variations in those parameters in terms of actual values over time. The models provide heat transfer analysis for the aforementioned concepts to confirm the overall system and major component sizing (preliminary) to assess the practicality of the systems. Real-time EAF off-gas composition (e.g., CO, CO2, H2, and H2O), volume flow, and temperature data from one EAF operation was used to test the validity and accuracy of the modeling work. The EAF off-gas data was used to calculate the sensible and chemical enthalpy of the EAF off-gases to generate steam and power. The article provides detailed results from the modeling work that are important to the success of ORNL s EAF WHR project. The EAF WHR project aims to develop and test new concepts and materials that allow cost-effective recovery of sensible and chemical heat from high-temperature gases discharged from EAFs.
Parallel Fractures Model for Tracer Flow Through Geothermal Reservoirs - Preliminary Results
Rivera, J.R.; Ramirez, J.S.; Rodriguez, F.G.
1987-01-20
A parallel fractures model, having equal width and spacing, has been developed to study the flow of tracers through naturally fractured geothermal reservoirs. The model is capable of handling either a single fracture or a system of two or more parallel fractures, interacting with associated porous bodies. The reservoir is treated as being composed of two regions a mobile region where diffusion and convection are allowed and a stagnant or immobile region where only diffusion and adsorption are allowed. Both regions are interconnected by means of a very thin fluid film contained within the immobile region which controls the fluid and mass transfer between both regions. The mobile region represents the system of fractures, where tracer is free to flow reaching high velocities, whereas non-homogeneities of the reservoir rock, such as microfractures and dead-end fractures are represented by means of an equivalent porous body where fluid remains immobile. The boundary-value problem for the system is stated and its solution into Laplace’s space is presented. Numerical inversion of this solution was performed by means of the Stehfest algorithm. Preliminary results showing results obtained from the proposed model are included. Further work is underway to apply the model for interpretation of actual tracer flow field data. 4 figs., 1 tab., 11 refs.
A Preliminary SPARROW Model of Suspended Sediment for the Conterminous United States
Schwarz, Gregory E.
2008-01-01
This report describes the results of a preliminary Spatially Referenced Regression on Watershed attributes (SPARROW) model of suspended sediment for the conterminous United States. The analysis is based on flux estimates compiled from more than 1,800 long-term monitoring stations operated by the U.S. Geological Survey (USGS) during the period 1975-2007. The SPARROW model is structured on the Reach File 1 (RF1) stream network, consisting of approximately 62,000 reach segments. The reach network has been modified to include more than 4,000 reservoirs, an important landscape feature affecting the delivery of suspended sediment. The model identifies six sources of sediment, including the stream channel and five classes of land use: urban, forested, Federal nonforested, agricultural and other, and noninundated land. The delivery of sediment from landform sources to RF1 streams is mediated by soil permeability, erodibility, slope, and rainfall; streamflow is found to affect the amount of sediment mobilized from the stream channel. The results show agricultural land and the stream channel to be major sources of sediment flux. Per unit area, Federal nonforested and urban lands are the largest landform sediment sources. Reservoirs are identified as major sites for sediment attenuation. This report includes a description for how the model results can be used to assess changes in instream sediment flux and concentration resulting from proposed changes in the regulation of sediment discharge from construction sites.
Tucci, Patrick
1986-01-01
Shallow land burial of low-level radioactive waste has been practiced since 1951 in Melton Valley. Groundwater flow modeling was used to better understand the geohydrology of the valley, and to provide a foundation for future contaminant transport modeling. The three-dimensional, finite difference model simulates the aquifer as a two layer system that represents the regolith and bedrock. Transmissivities, which were adjusted during model calibration, range from 8 to 16 sq ft/day for the regolith, and from 0.2 to 1.5 sq ft/day for bedrock. An anisotropy ratio of 1:3 for strike-normal to strike-parallel transmissivity values, in conjunction with recharge rate = 6% of precipitation that is uniformly distributed over the model area, produces the best match between simulated and observed water levels. Simulated water levels generally compare well to observed or estimated 1978 groundwater conditions. Simulated water levels for the regolith for 39 of 69 comparison points are within +/- 10 ft of average 1978 levels. Simulated vertical flow components are in the observed direction for 9 of 11 comparison points. Preliminary simulations indicate that nearly all groundwater flow is within the regolith and discharges to either the Clinch River or the White Oak Creek-Melton Branch drainage systems. Less than 3% of the flow is between the regolith and bedrock, and < 1% of total groundwater flow discharges to the Clinch River through bedrock. Additional data needed to refine and further calibrate the model, include: (1) quantity and areal distribution of recharge; (2) water levels in the regolith near the model boundaries and beyond the Clinch River; (3) water levels and aquifer characteristics for bedrock; and (4) additional surface water data. (Author 's abstract)
Arthur S. Rood; Swen O. Magnuson
2009-07-01
This document is in response to a request by Ming Zhu, DOE-EM to provide a preliminary review of existing models and data used in completed or soon to be completed Performance Assessments and Composite Analyses (PA/CA) documents, to identify codes, methodologies, main assumptions, and key data sets used.
NASA Astrophysics Data System (ADS)
Theodoridou, Panagiota G.; Karatzas, George P.; Varouchakis, Emmanouil A.; Corzo Perez, Gerald A.
2015-04-01
Groundwater level is an important information in hydrological modelling. Geostatistical methods are often employed to map the free surface of an aquifer. In geostatistical analysis using Kriging techniques the selection of the optimal variogram model is very important for the optimal method performance. This work compares three different criteria, the least squares sum method, the Akaike Information Criterion and the Cressie's Indicator, to assess the theoretical variogram that fits to the experimental one and investigates the impact on the prediction results. Moreover, five different distance functions (Euclidean, Minkowski, Manhattan, Canberra, and Bray-Curtis) are applied to calculate the distance between observations that affects both the variogram calculation and the Kriging estimator. Cross validation analysis in terms of Ordinary Kriging is applied by using sequentially a different distance metric and the above three variogram fitting criteria. The spatial dependence of the observations in the tested dataset is studied by fitting classical variogram models and the Matérn model. The proposed comparison analysis performed for a data set of two hundred fifty hydraulic head measurements distributed over an alluvial aquifer that covers an area of 210 km2. The study area is located in the Prefecture of Drama, which belongs to the Water District of East Macedonia (Greece). This area was selected in terms of hydro-geological data availability and geological homogeneity. The analysis showed that a combination of the Akaike information Criterion for the variogram fitting assessment and the Brays-Curtis distance metric provided the most accurate cross-validation results. The Power-law variogram model provided the best fit to the experimental data. The aforementioned approach for the specific dataset in terms of the Ordinary Kriging method improves the prediction efficiency in comparison to the classical Euclidean distance metric. Therefore, maps of the spatial
Geostatistics as a tool to study mite dispersion in physic nut plantations.
Rosado, J F; Picanço, M C; Sarmento, R A; Pereira, R M; Pedro-Neto, M; Galdino, T V S; de Sousa Saraiva, A; Erasmo, E A L
2015-08-01
Spatial distribution studies in pest management identify the locations where pest attacks on crops are most severe, enabling us to understand and predict the movement of such pests. Studies on the spatial distribution of two mite species, however, are rather scarce. The mites Polyphagotarsonemus latus and Tetranychus bastosi are the major pests affecting physic nut plantations (Jatropha curcas). Therefore, the objective of this study was to measure the spatial distributions of P. latus and T. bastosi in the physic nut plantations. Mite densities were monitored over 2 years in two different plantations. Sample locations were georeferenced. The experimental data were analyzed using geostatistical analyses. The total mite density was found to be higher when only one species was present (T. bastosi). When both the mite species were found in the same plantation, their peak densities occurred at different times. These mites, however, exhibited uniform spatial distribution when found at extreme densities (low or high). However, the mites showed an aggregated distribution in intermediate densities. Mite spatial distribution models were isotropic. Mite colonization commenced at the periphery of the areas under study, whereas the high-density patches extended until they reached 30 m in diameter. This has not been reported for J. curcas plants before. PMID:25895655
Applying Geostatistical Analysis to Crime Data: Car-Related Thefts in the Baltic States.
Kerry, Ruth; Goovaerts, Pierre; Haining, Robert P; Ceccato, Vania
2010-01-01
Geostatistical methods have rarely been applied to area-level offense data. This article demonstrates their potential for improving the interpretation and understanding of crime patterns using previously analyzed data about car-related thefts for Estonia, Latvia, and Lithuania in 2000. The variogram is used to inform about the scales of variation in offense, social, and economic data. Area-to-area and area-to-point Poisson kriging are used to filter the noise caused by the small number problem. The latter is also used to produce continuous maps of the estimated crime risk (expected number of crimes per 10,000 habitants), thereby reducing the visual bias of large spatial units. In seeking to detect the most likely crime clusters, the uncertainty attached to crime risk estimates is handled through a local cluster analysis using stochastic simulation. Factorial kriging analysis is used to estimate the local- and regional-scale spatial components of the crime risk and explanatory variables. Then regression modeling is used to determine which factors are associated with the risk of car-related theft at different scales.
Usage of multivariate geostatistics in interpolation processes for meteorological precipitation maps
NASA Astrophysics Data System (ADS)
Gundogdu, Ismail Bulent
2015-09-01
Long-term meteorological data are very important both for the evaluation of meteorological events and for the analysis of their effects on the environment. Prediction maps which are constructed by different interpolation techniques often provide explanatory information. Conventional techniques, such as surface spline fitting, global and local polynomial models, and inverse distance weighting may not be adequate. Multivariate geostatistical methods can be more significant, especially when studying secondary variables, because secondary variables might directly affect the precision of prediction. In this study, the mean annual and mean monthly precipitations from 1984 to 2014 for 268 meteorological stations in Turkey have been used to construct country-wide maps. Besides linear regression, the inverse square distance and ordinary co-Kriging (OCK) have been used and compared to each other. Also elevation, slope, and aspect data for each station have been taken into account as secondary variables, whose use has reduced errors by up to a factor of three. OCK gave the smallest errors (1.002 cm) when aspect was included.
Wu, Wenyong; Yin, Shiyang; Liu, Honglu; Niu, Yong; Bao, Zhe
2014-10-01
The purpose of this study was to determine and evaluate the spatial changes in soil salinity by using geostatistical methods. The study focused on the suburb area of Beijing, where urban development led to water shortage and accelerated wastewater reuse to farm irrigation for more than 30 years. The data were then processed by GIS using three different interpolation techniques of ordinary kriging (OK), disjunctive kriging (DK), and universal kriging (UK). The normality test and overall trend analysis were applied for each interpolation technique to select the best fitted model for soil parameters. Results showed that OK was suitable for soil sodium adsorption ratio (SAR) and Na(+) interpolation; UK was suitable for soil Cl(-) and pH; DK was suitable for soil Ca(2+). The nugget-to-sill ratio was applied to evaluate the effects of structural and stochastic factors. The maps showed that the areas of non-saline soil and slight salinity soil accounted for 6.39 and 93.61%, respectively. The spatial distribution and accumulation of soil salt were significantly affected by the irrigation probabilities and drainage situation under long-term wastewater irrigation.
Wu, Wenyong; Yin, Shiyang; Liu, Honglu; Niu, Yong; Bao, Zhe
2014-10-01
The purpose of this study was to determine and evaluate the spatial changes in soil salinity by using geostatistical methods. The study focused on the suburb area of Beijing, where urban development led to water shortage and accelerated wastewater reuse to farm irrigation for more than 30 years. The data were then processed by GIS using three different interpolation techniques of ordinary kriging (OK), disjunctive kriging (DK), and universal kriging (UK). The normality test and overall trend analysis were applied for each interpolation technique to select the best fitted model for soil parameters. Results showed that OK was suitable for soil sodium adsorption ratio (SAR) and Na(+) interpolation; UK was suitable for soil Cl(-) and pH; DK was suitable for soil Ca(2+). The nugget-to-sill ratio was applied to evaluate the effects of structural and stochastic factors. The maps showed that the areas of non-saline soil and slight salinity soil accounted for 6.39 and 93.61%, respectively. The spatial distribution and accumulation of soil salt were significantly affected by the irrigation probabilities and drainage situation under long-term wastewater irrigation. PMID:25127658
Hack, Daniel R.
2005-01-01
Sand-and-gravel (aggregate) resources are a critical component of the Nation's infrastructure, yet aggregate-mining technologies lag far behind those of metalliferous mining and other sectors. Deposit-evaluation and site-characterization methodologies are antiquated, and few serious studies of the potential applications of spatial-data analysis and geostatistics have been published. However, because of commodity usage and the necessary proximity of a mine to end use, aggregate-resource exploration and evaluation differ fundamentally from comparable activities for metalliferous ores. Acceptable practices, therefore, can reflect this cruder scale. The increasing use of computer technologies is colliding with the need for sand-and-gravel mines to modernize and improve their overall efficiency of exploration, mine planning, scheduling, automation, and other operations. The emergence of megaquarries in the 21st century will also be a contributing factor. Preliminary research into the practical applications of exploratory-data analysis (EDA) have been promising. For example, EDA was used to develop a linear-regression equation to forecast freeze-thaw durability from absorption values for Lower Paleozoic carbonate rocks mined for crushed aggregate from quarries in Oklahoma. Applications of EDA within a spatial context, a method of spatial-data analysis, have also been promising, as with the investigation of undeveloped sand-and-gravel resources in the sedimentary deposits of Pleistocene Lake Bonneville, Utah. Formal geostatistical investigations of sand-and-gravel deposits are quite rare, and the primary focus of those studies that have been completed is on the spatial characterization of deposit thickness and its subsequent effect on ore reserves. A thorough investigation of a gravel deposit in an active aggregate-mining area in central Essex, U.K., emphasized the problems inherent in the geostatistical characterization of particle-size-analysis data. Beyond such factors
NASA Astrophysics Data System (ADS)
Yadav, V.; Mueller, K. L.; Dragoni, D.; Michalak, A. M.
2010-09-01
A coupled Bayesian model selection and geostatistical regression modeling approach is adopted for empirical analysis of gross primary productivity (GPP) at six AmeriFlux sites, including the Kennedy Space Center Scrub Oak, Vaira Ranch, Tonzi Ranch, Blodgett Forest, Morgan Monroe State Forest, and Harvard Forest sites. The analysis is performed at a continuum of temporal scales ranging from daily to monthly, for a period of seven years. A total of 10 covariates representing environmental stimuli and indices of plant physiology are considered in explaining variations in GPP. Similarly to other statistical methods, the presented approach estimates regression coefficients and uncertainties associated with the covariates in a selected regression model. Unlike traditional regression methods, however, the approach also estimates the uncertainty associated with the selection of a single "best" model of GPP. In addition, the approach provides an enhanced understanding of how the importance of specific covariates changes with the examined timescale (i.e. temporal resolution). An examination of changes in the importance of specific covariates across timescales reveals thresholds above or below which covariates become important in explaining GPP. Results indicate that most sites (especially those with a stronger seasonal cycle) exhibit at least one prominent scaling threshold between the daily and 20-day temporal scales. This demonstrates that environmental variables that explain GPP at synoptic scales are different from those that capture its seasonality. At shorter time scales, radiation, temperature, and vapor pressure deficit exert the most significant influence on GPP at most examined sites. At coarser time scales, however, the importance of these covariates in explaining GPP declines. Overall, unique best models are identified at most sites at the daily scale, whereas multiple competing models are identified at longer time scales.
Using Geostatistical Data Fusion Techniques and MODIS Data to Upscale Simulated Wheat Yield
NASA Astrophysics Data System (ADS)
Castrignano, A.; Buttafuoco, G.; Matese, A.; Toscano, P.
2014-12-01
Population growth increases food request. Assessing food demand and predicting the actual supply for a given location are critical components of strategic food security planning at regional scale. Crop yield can be simulated using crop models because is site-specific and determined by weather, management, length of growing season and soil properties. Crop models require reliable location-specific data that are not generally available. Obtaining these data at a large number of locations is time-consuming, costly and sometimes simply not feasible. An upscaling method to extend coverage of sparse estimates of crop yield to an appropriate extrapolation domain is required. This work is aimed to investigate the applicability of a geostatistical data fusion approach for merging remote sensing data with the predictions of a simulation model of wheat growth and production using ground-based data. The study area is Capitanata plain (4000 km2) located in Apulia Region, mostly cropped with durum wheat. The MODIS EVI/NDVI data products for Capitanata plain were downloaded from the Land Processes Distributed Active Archive Center (LPDAAC) remote for the whole crop cycle of durum wheat. Phenological development, biomass growth and grain quantity of durum wheat were simulated by the Delphi system, based on a crop simulation model linked to a database including soil properties, agronomical and meteorological data. Multicollocated cokriging was used to integrate secondary exhaustive information (multi-spectral MODIS data) with primary variable (sparsely distributed biomass/yield model predictions of durum wheat). The model estimates looked strongly spatially correlated with the radiance data (red and NIR bands) and the fusion data approach proved to be quite suitable and flexible to integrate data of different type and support.
Cronkite-Ratcliff, C.; Phelps, G.A.; Boucher, A.
2012-01-01
This report provides a proof-of-concept to demonstrate the potential application of multiple-point geostatistics for characterizing geologic heterogeneity and its effect on flow and transport simulation. The study presented in this report is the result of collaboration between the U.S. Geological Survey (USGS) and Stanford University. This collaboration focused on improving the characterization of alluvial deposits by incorporating prior knowledge of geologic structure and estimating the uncertainty of the modeled geologic units. In this study, geologic heterogeneity of alluvial units is characterized as a set of stochastic realizations, and uncertainty is indicated by variability in the results of flow and transport simulations for this set of realizations. This approach is tested on a hypothetical geologic scenario developed using data from the alluvial deposits in Yucca Flat, Nevada. Yucca Flat was chosen as a data source for this test case because it includes both complex geologic and hydrologic characteristics and also contains a substantial amount of both surface and subsurface geologic data. Multiple-point geostatistics is used to model geologic heterogeneity in the subsurface. A three-dimensional (3D) model of spatial variability is developed by integrating alluvial units mapped at the surface with vertical drill-hole data. The SNESIM (Single Normal Equation Simulation) algorithm is used to represent geologic heterogeneity stochastically by generating 20 realizations, each of which represents an equally probable geologic scenario. A 3D numerical model is used to simulate groundwater flow and contaminant transport for each realization, producing a distribution of flow and transport responses to the geologic heterogeneity. From this distribution of flow and transport responses, the frequency of exceeding a given contaminant concentration threshold can be used as an indicator of uncertainty about the location of the contaminant plume boundary.
The South Georgia Wave Experiment (SG-WEX) - Preliminary Satellite and Modelling Studies
NASA Astrophysics Data System (ADS)
Wright, C.; Mitchell, N. J.
2014-12-01
Internal gravity waves, and the energy and momentum they transport, are a key process governing the dynamics and structure of the middle and upper atmosphere, but are significantly underconstrained in current weather and climate models due to their small physical scale relative to typical model grids. In particular, the simulation of such waves is believed to underlie a major momentum deficit in the high-latitude southern hemisphere, due to underestimation of the gravity wave drag provided by waves generated by sub-gridscale island sources. The South Georgia Wave Experiment (SG-WEX) is a coordinated programme to investigate the most important such small island source, South Georgia Island. This will be carried out via the deployment of a mesospheric-sensing meteor radar, stratospheric satellite measurements, and two month-long tropospheric radiosonde campaigns, backed up by detailed high-resolution modelling of the region at all altitudes. The ultimate goal of the project is to significantly enhance our geophysical understanding of wave dynamics in the region and, more directly, to provide a basis for a dramatic improvement in the parameterisation of the wave momentum flux generated by such small-island sources, with consequent effects on the skill of global weather and climate models both in this region and globally. Here, we present preliminary results from the satellite and modelling components of the project. In particular, we use combined measurements from the HIRDLS, SABER, AIRS and COSMIC satellite instruments and analyses from the HadGEM GCM to produce a detailed temporal and spatial climatology of wave fluxes in the region, due both to South Georgia and to the nearby Andes and Antarctic Peninsula. We also briefly look ahead to the deployment of the meteor radar and first radiosonde campaign in January 2015, and discuss how these are expected to enhance our understanding of the dynamics of the region.
A Multilayered Box Model for Calculating Preliminary RemediationGoals in Soil Screening
Shan, Chao; Javandel, Iraj
2004-05-21
In the process of screening a soil against a certain contaminant, we define the health-risk based preliminary remediation goal (PRG) as the contaminant concentration above which some remedial action may be required. PRG is thus the first standard (or guidance) for judging a site. An over-estimated PRG (a too-large value) may cause us to miss some contaminated sites that can threaten human health and the environment. An under-estimated PRG (a too-small value), on the other hand, may lead to unnecessary cleanup and waste tremendous resources. The PRGs for soils are often calculated on the assumption that the contaminant concentration in soil does not change with time. However, that concentration usually decreases with time as a result of different chemical and transport mechanisms. The static assumption thus exaggerates the long-term exposure dose and results in a too-small PRG. We present a box model that considers all important transport processes and obeys the law of mass conservation. We can use the model as a tool to estimate the transient contaminant concentrations in air, soil and groundwater. Using these concentrations in conjunction with appropriate health risk parameters, we may estimate the PRGs for different contaminants. As an example, we calculated the tritium PRG for residential soils. The result is quite different from, but within the range of, the two versions of the corresponding PRG previously recommended by the U.S. EPA.
Preliminary clinical nursing leadership competency model: a qualitative study from Thailand.
Supamanee, Treeyaphan; Krairiksh, Marisa; Singhakhumfu, Laddawan; Turale, Sue
2011-12-01
This qualitative study explored the clinical nursing leadership competency perspectives of Thai nurses working in a university hospital. To collect data, in-depth interviews were undertaken with 23 nurse administrators, and focus groups were used with 31 registered nurses. Data were analyzed using content analysis, and theory development was guided by the Iceberg model. Nurses' clinical leadership competencies emerged, comprising hidden characteristics and surface characteristics. The hidden characteristics composed three elements: motive (respect from the nursing and healthcare team and being secure in life), self-concept (representing positive attitudes and values), and traits (personal qualities necessary for leadership). The surface characteristics comprised specific knowledge of nurse leaders about clinical leadership, management and nursing informatics, and clinical skills, such as coordination, effective communication, problem solving, and clinical decision-making. The study findings help nursing to gain greater knowledge of the essence of clinical nursing leadership competencies, a matter critical for theory development in leadership. This study's results later led to the instigation of a training program for registered nurse leaders at the study site, and the formation of a preliminary clinical nursing leadership competency model.
Planning Genomic Study in an Animal Model of Depression: a Preliminary Report
Farnam, Alireza; Fakhari, Ali; Roshangar, Leila; Kahni, Sajjad; Farhang, Sara
2011-01-01
Introduction Interaction of several genes is responsible for psychiatric diseases such as depression. Despite the numerous microarray studies in this field, findings are controversial and hard to conclude. Methods Male Wistar rats were randomly selected to receive Chronic Mild Stress model for 4 weeks. Different aspects of depression were measured by forced swimming test, open field trial and sucrose preference tests in the experience group and controls. Results Sucrose was preferred by 40% of CMS group and 80% of controls (p=0.025). Twenty percent of CMS group and 80% of controls were “active” (p=0.001). Last escape was at minute 238 for CMS group and minute 245 for controls and controls had more escape efforts. Conclusion This paper is a preliminary report of a genomic study on animal model of depression which tries to achieve reliable results by a joint of clinical view with recent techniques. Predicted challenges in this procedure and the solutions as well as the limitations may be helpful for future researches. PMID:23678424
Preliminary development of digital elevation and relief models for ICESat-2 onboard processing
NASA Astrophysics Data System (ADS)
Leigh, H. W.; Magruder, L. A.; Carabajal, C. C.
2012-12-01
ATLAS (Advanced Topographic Laser Altimeter System) is a photon-counting laser ranging instrument that will fly onboard NASA's ICESat-2 mission to collect global altimetry data for the primary purpose of determining volumetric changes in the Polar Regions. While photon-counting systems provide the advantage of using small, low power lasers, they are typically much more susceptible to noise and require the use of sophisticated algorithms both onboard and in ground based processing to ensure capture of valid data and production of accurate data products. An onboard receiver algorithm is being developed for ATLAS to ensure that valid data is returned while adhering to the 577 Gb/day limit on data telemetry. The onboard receiver algorithm makes use of multiple onboard databases, two of which are the DEM (Digital Elevation Model) and the DRM (Digital Relief Map). The DEM provides start and stop times for software-induced range gating on the ATLAS detectors, and is a nested, three-tiered grid to account for a 6 km overall constraint on the allowable limit for ranging acquisition. The DRM contains the maximum values of relief seen across 140m- and 700m-long flight path segments, which are used in statistically determining the presence of a valid surface return and in deciding which bands to telemeter. Both onboard databases are to be primarily constructed from existing digital elevation models and must provide global coverage referenced to latitude and longitude. Production of the grids is complicated by the lack of global data products of sufficient resolution and accuracy such that preliminary analysis is required for DEM selection and usage in addition to the determination of how to intelligently merge differing data sets. This initial investigation is also focused on determining the impact of the selected DEM quality on the ICESat-2 onboard algorithms as well as the precipitated error induced on the DRM. These results are required in order to determine the expected
NASA Astrophysics Data System (ADS)
Ruggeri, Paolo; Irving, James; Holliger, Klaus
2015-08-01
We critically examine the performance of sequential geostatistical resampling (SGR) as a model proposal mechanism for Bayesian Markov-chain-Monte-Carlo (MCMC) solutions to near-surface geophysical inverse problems. Focusing on a series of simple yet realistic synthetic crosshole georadar tomographic examples characterized by different numbers of data, levels of data error and degrees of model parameter spatial correlation, we investigate the efficiency of three different resampling strategies with regard to their ability to generate statistically independent realizations from the Bayesian posterior distribution. Quite importantly, our results show that, no matter what resampling strategy is employed, many of the examined test cases require an unreasonably high number of forward model runs to produce independent posterior samples, meaning that the SGR approach as currently implemented will not be computationally feasible for a wide range of problems. Although use of a novel gradual-deformation-based proposal method can help to alleviate these issues, it does not offer a full solution. Further, we find that the nature of the SGR is found to strongly influence MCMC performance; however no clear rule exists as to what set of inversion parameters and/or overall proposal acceptance rate will allow for the most efficient implementation. We conclude that although the SGR methodology is highly attractive as it allows for the consideration of complex geostatistical priors as well as conditioning to hard and soft data, further developments are necessary in the context of novel or hybrid MCMC approaches for it to be considered generally suitable for near-surface geophysical inversions.
Eschard, R.; Desaubliaux, G.; Eemouzy, P. ); Bacchiana, C.; Parpant, J.; Chautru, J.M.
1993-09-01
Chaunoy field, the largest oil field of the Paris basin, is exploiting heterogeneous reservoirs deposited during the Triassic in a large alluvial fan/lacustrine complex. The construction of a realistic reservoir model is difficult in such a setting because of the highly complex architecture of single reservoir units. Geostatistical simulations therefore have been performed to take into account the reservoir heterogeneities in the fluid flow modeling. A first layering has been determined from sedimentological and sequence stratigraphic analysis. The series was deposited in an alluvial outer fan environment. A lower siliciclastic member shows four heterogeneous sand sheets (7 m thick), which have been correlated across the field. Each of them is made up of stacked single channel sequences. The sand sheets are separated by extensive lacustrine and flood plain mudstone layers acting as permeability barriers. An upper siliciclastic/dolomitic member has been divided into two units with porous conglomeratic channels interfingered with cemented lagoonal dolomites. Proportional curves in lithofacies have confirmed this layering, showing the continuity of the permeability barriers, and the variogram analysis has shown that the well spacing is larger than the channel width. Simulations in lithofacies have been performed with the Heresim software using three different variogram ranges (small, medium, and large values). Because a good correlation exists between the lithofacies and the petrophysical attributes, a transcription of the lithofacies simulations into petrophysical attributes therefore was easy and realistic. Scaling-up techniques have given fluid-flow models corresponding to the three correlation ranges. Comparison of the global results of the fluid flow simulations with the observed production history enabled us to choose the more relevant case. The the model using the selected correlation range helped determine optimum well spacing.
Rhodes, Elena M; Liburd, Oscar E; Grunwald, Sabine
2011-08-01
Flower thrips (Frankliniella spp.) are one of the key pests of southern highbush blueberries (Vaccinium corymbosum L. x V. darrowii Camp), a high-value crop in Florida. Thrips' feeding and oviposition injury to flowers can result in fruit scarring that renders the fruit unmarketable. Flower thrips often form areas of high population, termed "hot spots", in blueberry plantings. The objective of this study was to model thrips spatial distribution patterns with geostatistical techniques. Semivariogram models were used to determine optimum trap spacing and two commonly used interpolation methods, inverse distance weighting (IDW) and ordinary kriging (OK), were compared for their ability to model thrips spatial patterns. The experimental design consisted of a grid of 100 white sticky traps spaced at 15.24-m and 7.61-m intervals in 2008 and 2009, respectively. Thirty additional traps were placed randomly throughout the sampling area to collect information on distances shorter than the grid spacing. The semivariogram analysis indicated that, in most cases, spacing traps at least 28.8 m apart would result in spatially independent samples. Also, the 7.61-m grid spacing captured more of the thrips spatial variability than the 15.24-m grid spacing. IDW and OK produced maps with similar accuracy in both years, which indicates that thrips spatial distribution patterns, including "hot spots," can be modeled using either interpolation method. Future studies can use this information to determine if the formation of "hot spots" can be predicted using flower density, temperature, and other environmental factors. If so, this development would allow growers to spot treat the "hot spots" rather than their entire field.
Monforti, F; Vitali, L; Bellasio, R; Bianconi, R
2006-02-21
In this paper a new approach to photochemical modeling is investigated and a lagrangian particle model named Photochemical Lagrangian Particle Model (PLPM) is described. Lagrangian particle models are a consolidated tool to deal with the dispersion of pollutants in the atmosphere. Good results have been obtained dealing with inert pollutants. In recent years, a number of pioneering works have shown as Lagrangian models can be of great interest when dealing with photochemistry, provided that special care is given in the reconstruction of chemicals concentration in the atmosphere. Density reconstruction can be performed through the so called ''box counting'' method: an Eulerian grid for chemistry is introduced and density is computed counting particles in each box. In this way one of the main advantages of the Lagrangian approach, the grid independence, is lost. Photochemical reactions are treated in PLPM by means of the complex chemical mechanism SAPRC90 and four density reconstruction methods have been developed, based on the kernel density estimator approach, in order to obtain grid-free accurate concentrations. These methods are all fully grid-free but they differ each other in considering local or global features of the particles distribution, in treating the Cartesian directions separately or together and in being based on receptors or particles positions in space.
Mathematical Model of Cold Cap—Preliminary One-Dimensional Model Development
Pokorny, Richard; Hrma, Pavel R.
2011-03-25
The ultimate goal of batch-melting studies, laboratory-scale, large-scale, or mathematical modeling is to increase the rate of glass processing in an energy-efficient manner. Mathematical models are not merely an intermediate step between laboratory-scale and large-scale studies, but are also an important tool for assessing the responses of melters to vast combinations of process parameters. In the simplest melting situation considered in this study, a cold cap of uniform thickness rests on a pool of molten glass from which it receives a steady uniform heat flux. Thus, as the feed-to-glass conversion proceeds, the temperature, velocity, and extent of feed reactions are functions of the position along the vertical coordinate, and these functions do not vary with time. This model is used for the sensitivity analyses on the effects of key parameters on the cold-cap behavior.
Wolter X-Ray Microscope Computed Tomography Ray-Trace Model with Preliminary Simulation Results
Jackson, J A
2006-02-27
code, (5) description of the modeling code, (6) the results of a number of preliminary imaging simulations, and (7) recommendations for future Wolter designs and for further modeling studies.
Geostatistical analysis of spatial and temporal variations of groundwater level.
Ahmadi, Seyed Hamid; Sedghamiz, Abbas
2007-06-01
Groundwater and water resources management plays a key role in conserving the sustainable conditions in arid and semi-arid regions. Applying management tools which can reveal the critical and hot conditions seems necessary due to some limitations such as labor and funding. In this study, spatial and temporal analysis of monthly groundwater level fluctuations of 39 piezometric wells monitored during 12 years was carried out. Geostatistics which has been introduced as a management and decision tool by many researchers has been applied to reveal the spatial and temporal structure of groundwater level fluctuation. Results showed that a strong spatial and temporal structure existed for groundwater level fluctuations due to very low nugget effects. Spatial analysis showed a strong structure of groundwater level drop across the study area and temporal analysis showed that groundwater level fluctuations have temporal structure. On average, the range of variograms for spatial and temporal analysis was about 9.7 km and 7.2 months, respectively. Ordinary and universal kriging methods with cross-validation were applied to assess the accuracy of the chosen variograms in estimation of the groundwater level drop and groundwater level fluctuations for spatial and temporal scales, respectively. Results of ordinary and universal krigings revealed that groundwater level drop and groundwater level fluctuations were underestimated by 3% and 6% for spatial and temporal analysis, respectively, which are very low and acceptable errors and support the unbiasedness hypothesis of kriging. Although, our results demonstrated that spatial structure was a little bit stronger than temporal structure, however, estimation of groundwater level drop and groundwater level fluctuations could be performed with low uncertainty in both space and time scales. Moreover, the results showed that kriging is a beneficial and capable tool for detecting those critical regions where need more attentions for
Fugacity based modeling for pollutant fate and transport during floods. Preliminary results
NASA Astrophysics Data System (ADS)
Deda, M.; Fiorini, M.; Massabo, M.; Rudari, R.
2010-09-01
Fugacity based modeling for pollutant fate and transport during floods. Preliminary results Miranda Deda, Mattia Fiorini, Marco Massabò, Roberto Rudari One of the concerns that arises during floods is whether the wide-spreading of chemical contamination is associated with the flooding. Many potential sources of toxics releases during floods exists in cities or rural area; hydrocarbons fuel storage system, distribution facilities, commercial chemical storage, sewerage system are only few examples. When inundated homes and vehicles can also be source of toxics contaminants such as gasoline/diesel, detergents and sewage. Hazardous substances released into the environment are transported and dispersed in complex environmental systems that include air, plant, soil, water and sediment. Effective environmental models demand holistic modelling of the transport and transformation of the materials in the multimedia arena. Among these models, fugacity-based models are distribution based models incorporating all environmental compartments and are based on steady-state fluxes of pollutants across compartment interfaces (Mackay "Multimedia Environmental Models" 2001). They satisfy the primary objective of environmental chemistry which is to forecast the concentrations of pollutants in the environments with respect to space and time variables. Multimedia fugacity based-models has been used to assess contaminant distribution at very different spatial and temporal scales. The applications range from contaminant leaching to groundwater, runoff to surface water, partitioning in lakes and streams, distribution at regional and even global scale. We developped a two-dimensional fugacity based model for fate and transport of chemicals during floods. The model has three modules: the first module estimates toxins emission rates during floods; the second modules is the hydrodynamic model that simulates the water flood and the third module simulate the dynamic distribution of chemicals in
Flare Comparisons of the Flare Irradiance Spectral Model (FISM) to Preliminary SDO EVE Data
NASA Technical Reports Server (NTRS)
Chamberlon, Phillip C.
2010-01-01
The Solar Dynamics Observatory (SDO) launched February 11, 2010 from Kennedy Space Center and started normal science operations in April 2010. One of the instruments onboard SDO, the EUV Variability- Experiment (EVE), will measure the solar EUV irradiance from 0.1-105 nm with 0.1 nm spectral resolution as well as a measure of the broad-band Lyman-Alpha emission (121.0 rim), all with less than 10 percent uncertainties. One of the biggest improvements of EVE over its predecessors is its ability to continuously measure the complete spectrum ever y 10 seconds, 24 hours a day, 7 days a week. This temporal coverage and cadence will greatly enhance the knowledge of the solar EUV variations during solar flares. This paper will present a comparison of the Flare Irradiance Spectral Model (FISM), which can produce an estimated EUV spectrum at 10 seconds temporal resolution, to the preliminary flare observation results from SDO EVE. The discussion will focus on the short-term EUV flare variations and evolution.
Comparisons of the Flare Irradiance Spectral Model (FISM) to Preliminary SDO EVE Data
NASA Technical Reports Server (NTRS)
Chamberlin, Phillip
2010-01-01
The Solar Dynamics Observatory (SDO) launched February 11,2010 from Kennedy Space Center and started normal science operations in April 2010. One of the instruments onboard SDO, the EUV Variability Experiment (EVE), will measure the solar EUV irradiance from 0.1-105 nm with 0.1 nm spectral resolution as well as a measure of the broad-band Lyman-Alpha emission (121.6 nm), all with less than 10 percent uncertainties. One of the biggest improvements of EVE over its predecessors is its ability to continuously measure the complete spectrum every 10 seconds, 24 hours a day, 7 days a week. This temporal coverage and cadence will greatly enhance the knowledge of the solar EUV variations during solar flares. This paper will present a comparison of the Flare Irradiance Spectral Model (FISM), which can produce an estimated EUV spectrum at 10 seconds temporal resolution, to the preliminary results from SDO EVE. The discussion will focus on the short-term EUV flare variations and evolution.
Field survey and preliminary modeling of the 2011 Tohoku tsunami at Jayapura, Papua, Indonesia
NASA Astrophysics Data System (ADS)
Prasetya, T.; Harjadi, P.; Nugroho, C.; Okal, E.; Synolakis, C.; Kalligeris, N.
2011-12-01
The 2011 Tohoku tsunami was unexpectedly severe at Jayapura (2 S; 140 E), in the Easternmost part of the province of Papua, Indonesia, although it is clearly located outside the lobe of main radiation from the source. Although tidal gauge records did not exceed 80 cm at the harbor, run-up reached 2.8 m in the Southern part of the bay, characterized by very shallow dipping bathymetry. Several houses were destroyed in an ocean village, which fortunately had been evacuated following a warning issued by BMKG. One person was killed when the motorcycle he was riding was swept by the wave, apparently the only casualty of the tsunami in the far field. A river bridge was destroyed, and large shipping containers moved more than 100 m inland. A remarkable aspect of these observations is the papparent delay (at least 2 hours and possibly up to 7) of the main destruction, relative to the first arrival of the wave under the shallow water approximation. This tentatively suggests the influence of dispersed higher-frequency components. We will present the result of the survey and a discussion of preliminary modeling of the effect of bathymetric features in the Caroline basin, and of the non-linear response of the Bay of Jayapura in an attempt to explain the exceptional character of the tsunami at that location.
NASA Technical Reports Server (NTRS)
Alexander, J. Iwan D.; Ouazzani, Jalil
1988-01-01
It has become clear from measurements of the acceleration environment in the Spacelab that the residual gravity levels on board a spacecraft in low Earth orbit can be significant and should be of concern to experimenters who wish to take advantage of the low gravity conditions on future Spacelab missions and on board the Space Station. The basic goals are to better understand the low gravity tolerance of three classes of materials science experiments: crystal growth from a melt, a vapor, and a solution. The results of the research will provide guidance toward the determination of the sensitivity of the low gravity environment, the design of the laboratory facilites, and the timelining of materials science experiments. To data, analyses of the effects of microgravity environment were, with a few exceptions, restricted to order of magnitude estimates. Preliminary results obtained from numerical models of the effects of residual steady and time dependent acceleration are reported on: heat, mass, and momentum transport during the growth of a dilute alloy by the Bridgman-Stockbarger technique, and the response of a simple fluid physics experiment involving buoyant convection in a square cavity.
Simulating Late Ordovician deep ocean O2 with an earth system climate model. Preliminary results.
NASA Astrophysics Data System (ADS)
D'Amico, Daniel F.; Montenegro, Alvaro
2016-04-01
The geological record provides several lines of evidence that point to the occurrence of widespread and long lasting deep ocean anoxia during the Late Ordovician, between about 460-440 million years ago (ma). While a series of potential causes have been proposed, there is still large uncertainty regarding how the low oxygen levels came about. Here we use the University of Victoria Earth System Climate Model (UVic ESCM) with Late Ordovician paleogeography to verify the impacts of paleogeography, bottom topography, nutrient loading and cycling and atmospheric concentrations of O2 and CO2 on deep ocean oxygen concentration during the period of interest. Preliminary results so far are based on 10 simulations (some still ongoing) covering the following parameter space: CO2 concentrations of 2240 to 3780 ppmv (~8x to 13x pre-industrial), atmospheric O2 ranging from 8% to 12% per volume, oceanic PO4 and NO3 loading from present day to double present day, reductions in wind speed of 50% and 30% (winds are provided as a boundary condition in the UVic ESCM). For most simulations the deep ocean remains well ventilated. While simulations with higher CO2, lower atmospheric O2 and greater nutrient loading generate lower oxygen concentration in the deep ocean, bottom anoxia - here defined as concentrations <10 μmol L-1 - in these cases is restricted to the high-latitue northern hemisphere. Further simulations will address the impact of greater nutrient loads and bottom topography on deep ocean oxygen concentrations.
Chihi, Hayet; Galli, Alain; Ravenne, Christian; Tesson, Michel; Marsily, Ghislain de
2000-03-15
The object of this study is to build a three-dimensional (3D) geometric model of the stratigraphic units of the margin of the Rhone River on the basis of geophysical investigations by a network of seismic profiles at sea. The geometry of these units is described by depth charts of each surface identified by seismic profiling, which is done by geostatistics. The modeling starts by a statistical analysis by which we determine the parameters that enable us to calculate the variograms of the identified surfaces. After having determined the statistical parameters, we calculate the variograms of the variable Depth. By analyzing the behavior of the variogram we then can deduce whether the situation is stationary and if the variable has an anisotropic behavior. We tried the following two nonstationary methods to obtain our estimates: (a) The method of universal kriging if the underlying variogram was directly accessible. (b) The method of increments if the underlying variogram was not directly accessible. After having modeled the variograms of the increments and of the variable itself, we calculated the surfaces by kriging the variable Depth on a small-mesh estimation grid. The two methods then are compared and their respective advantages and disadvantages are discussed, as well as their fields of application. These methods are capable of being used widely in earth sciences for automatic mapping of geometric surfaces or for variables such as a piezometric surface or a concentration, which are not 'stationary,' that is, essentially, possess a gradient or a tendency to develop systematically in space.
NASA Astrophysics Data System (ADS)
Mariethoz, G.; Jha, S. K.; McCabe, M. F.; Evans, J. P.
2012-12-01
Recent advances in multiple-point geostatistics (MPS) offer new possibilities in remote sensing, surface hydrology and climate modeling. MPS is an ensemble of tools for the characterization of spatial phenomena. Its most prominent characteristic is the use of training images for defining what type of spatial patterns are deemed to result from the processes under study. In the last decade, MPS have been increasingly used to characterize 3D subsurface structures consisting of geological facies, with application primarily to reservoir engineering, hydrogeology and mining. Although the methods show good results, a consistent difficulty relates to finding appropriate training images to describe largely unknown geological formations. Despite this issue, the growing interest in MPS triggered a series of different methodological advances, leading to improved computational performance and increased flexibility. With these recent improvements, the scientific community now has unprecedented numerical tools that allow dealing with a wide range of problems outside the realm of subsurface applications. These include the simulation of continuous variables as well as complex non-linear ensembles of multivariate properties. It is found that these new tools are ideal to address a number of issues in scientific fields related to surface modeling of environmental systems and geophysical data. Shifting focus and investigating the application of MPS to surface hydrology results in a wealth of training images that are readily available, thanks to global networks of remote sensing measurements. This presentation will delineate recent results in this direction, including MPS applications to the stochastic downscaling of climate models, the completion of partially informed remote sensing images and the processing of geophysical data. A major advantage is the use of satellite images taken at regular intervals, which can be used to inform both the spatial and temporal variability of
Low Noise Results From IMS Site Surveys: A Preliminary New High-Frequency Low Noise Model
NASA Astrophysics Data System (ADS)
Ebeling, C.; Astiz, L.; Starovoit, Y.; Tavener, N.; Perez, G.; Given, H. K.; Barrientos, S.; Yamamoto, M.; Hfaiedh, M.; Stewart, R.; Estabrook, C.
2002-12-01
Zimbabwe (Archaean granite). Based on a composite of the results from these five surveys, we propose a preliminary IMS Low-Noise Model (pIMS-LNM) consisting of a revision downward of Peterson's NLNM in the passband from 0.1 to about 0.7 s and an extension of Peterson's NLNM above 0.1 to 0.07 s. As these low noise results are derived from data recorded at temporary installations, improved resolution of this model will be possible when data from final installations become available. Preliminary International Monitoring System Low Noise Model (pIMS-LNM) for periods from 0.07 to 0.70 s. Decibels are relative to ground acceleration ((m/s2)2/Hz). Values presented in (Period, dB) format. Figure in bold is from Peterson's NLNM. [(0.07,-167.0),(0.08,-168.0),(0.09,-169.0),(0.10,-169.5), (0.11,-170.5),(0.13,-171.0),(0.14,-171.5),(0.17,-172.0), (0.20,-1 72.5),(0.25,-173.0),(0.30,-173.5),(0.40,-173.0), (0.50,-172.0),(0.60,-171.0),(0.70,-170.0),(0.80,-169.2)] Reference Peterson, J., 1993. Observations and Modeling of Seismic Background Noise, U.S. Geological Survey Open-File Report 93-322, 47 p.
Fugacity based modeling for pollutant fate and transport during floods. Preliminary results
NASA Astrophysics Data System (ADS)
Deda, M.; Fiorini, M.; Massabo, M.; Rudari, R.
2010-09-01
Fugacity based modeling for pollutant fate and transport during floods. Preliminary results Miranda Deda, Mattia Fiorini, Marco Massabò, Roberto Rudari One of the concerns that arises during floods is whether the wide-spreading of chemical contamination is associated with the flooding. Many potential sources of toxics releases during floods exists in cities or rural area; hydrocarbons fuel storage system, distribution facilities, commercial chemical storage, sewerage system are only few examples. When inundated homes and vehicles can also be source of toxics contaminants such as gasoline/diesel, detergents and sewage. Hazardous substances released into the environment are transported and dispersed in complex environmental systems that include air, plant, soil, water and sediment. Effective environmental models demand holistic modelling of the transport and transformation of the materials in the multimedia arena. Among these models, fugacity-based models are distribution based models incorporating all environmental compartments and are based on steady-state fluxes of pollutants across compartment interfaces (Mackay "Multimedia Environmental Models" 2001). They satisfy the primary objective of environmental chemistry which is to forecast the concentrations of pollutants in the environments with respect to space and time variables. Multimedia fugacity based-models has been used to assess contaminant distribution at very different spatial and temporal scales. The applications range from contaminant leaching to groundwater, runoff to surface water, partitioning in lakes and streams, distribution at regional and even global scale. We developped a two-dimensional fugacity based model for fate and transport of chemicals during floods. The model has three modules: the first module estimates toxins emission rates during floods; the second modules is the hydrodynamic model that simulates the water flood and the third module simulate the dynamic distribution of chemicals in
A preliminary model for the development of sporadic serous ovarian adenocarcinoma
Chenevix-Trench, G.; Kerr, J.; Hurst, T.
1994-09-01
The genetic events that give rise to ovarian adenocarcinoma are poorly understood, nor is it known whether the benign, low malignant potential (LMP) and malignant tumors represent a continuum. This study reports K-ras mutation and loss of heterozygosity (LOH) analysis in 116 tumors, including benign and LMP tumors, on twelve chromosomes chosen mainly because they contain candidate suppressor genes. The data were analyzed with respect to clinicopathological information. The highest rates of LOH were on chromosomes 17 and 18. With the exception of chromosomes 2 and X, all the others were deleted in 25-50% of tumors. Significant associations were found between LOH on chromosomes 7 and 9p, and between chromosomes 5, 17 and 18. LOH was observed in benign and LMP tumors on chromosomes 7 and 9, and homozygous deletions of markers at 9p21 were detected in 2/10 ovarian tumor cell lines. The target of the 9p deletions is likely to be the MTS1/p16 cdk4 inhibitor gene and mutation analysis is under way. A single tumor had a rearrangement of the plasminogen activator inhibitor type 1 locus on chromosome 7 but deletion mapping indicates that this may not be the target of chromosome 7 LOH. K-ras mutations were detected in nine tumors, and were significantly more common in LMP than in malignant tumors. A preliminary genetic model for ovarian tumorigenesis in presented based on these and published data. This model proposes that LMP and malignant tumors arise independently in benign neoplasms in which LOH of chromosomes 7 and 9 has occurred. LMP tumors then develop following activation of the K-ras oncogene, while malignant tumors arise from inactivation of p53 and many other tumor suppressor genes.
Preliminary study of verteporfin photodynamic therapy in a canine prostate model
NASA Astrophysics Data System (ADS)
Huang, Zheng; Hetzel, Fred; Dole, Ken; Luck, David; Beckers, Jill; Maul, Don
2009-06-01
Photodynamic therapy (PDT) mediated with verteporfin was investigated as an alternative modality for the treatment of prostate cancer. Materials and Methods: Vertoporfin-mediated photodynamic effects on the prostate and its adjacent structures (underlying colon and bladder) were evaluated in a healthy canine model. Interstitial prostate PDT was performed by irradiating individual lobes with a diode laser (689 nm) and 1-cm cylindrical diffuser fibers at various light doses and drug-light intervals (DLI) to activate the IV administrated photosensitizer (0.5 or 2 mg/kg). The sensitivity of the adjacent tissues to Vertoporfin-PDT was determined by superficially irradiating the serosal surface of the bladder and colon with a microlens fiber. The prostate and adjacent tissues were harvested one-week after the treatment and subjected to histopathological examination. Results: Histopathological examinations confirmed that verteporfin PDT could destroy a clinically significant volume of prostatic tissue in the animal model. At the drug dose of 0.5 mg/kg, the light irradiation of 100 J/cm could induce a lesion diameter of 2 cm at DLI of 15 min and 1.2 cm at DLI of 3 hrs, respectively. This implies a strong influence of DLI on the lesion volume. The shorter DLI might produce stronger vascular effect and therefore more severe tissue damage. The colon was more sensitive to verteporfin PDT than the bladder. At the possible light dose level caused by light scattering during intra-prostate irradiation, the damage to the bladder and colon were superficial and minimal. Conclusions: The preliminary results clearly demonstrate that verteporfin PDT could be an effective means to destroy prostate gland and its usefulness for the treatment of prostate cancer is worth further investigation.
Singh, R B; Colls, J J
2000-10-01
Although modeling of gaseous emissions from motor vehicles is now quite advanced, prediction of particulate emissions is still at an unsophisticated stage. Emission factors for gasoline vehicles are not reliably available, since gasoline vehicles are not included in the European Union (EU) emission test procedure. Regarding diesel vehicles, emission factors are available for different driving cycles but give little information about change of emissions with speed or engine load. We have developed size-specific speed-dependent emission factors for gasoline and diesel vehicles. Other vehicle-generated emission factors are also considered and the empirical equation for re-entrained road dust is modified to include humidity effects. A methodology is proposed to calculate modal (accelerating, cruising, or idling) emission factors. The emission factors cover particle size ranges up to 10 microns, either from published data or from user-defined size distributions. A particulate matter emission factor model (PMFAC), which incorporates virtually all the available information on particulate emissions for European motor vehicles, has been developed. PMFAC calculates the emission factors for five particle size ranges [i.e., total suspended particulates (TSP), PM10, PM5, PM2.5, and PM1] from both vehicle exhaust and nonexhaust emissions, such as tire wear, brake wear, and re-entrained road dust. The model can be used for an unlimited number of roads and lanes, and to calculate emission factors near an intersection in user-defined elements of the lane. PMFAC can be used for a variety of fleet structures. Hot emission factors at the user-defined speed can be calculated for individual vehicles, along with relative cold-to-hot emission factors. The model accounts for the proportions of distance driven with cold engines as a function of ambient temperature and road type (i.e., urban, rural, or motorway). A preliminary evaluation of PMFAC with an available dispersion model to predict
Massively Parallel Geostatistical Inversion of Coupled Processes in Heterogeneous Porous Media
NASA Astrophysics Data System (ADS)
Ngo, A.; Schwede, R. L.; Li, W.; Bastian, P.; Ippisch, O.; Cirpka, O. A.
2012-04-01
The quasi-linear geostatistical approach is an inversion scheme that can be used to estimate the spatial distribution of a heterogeneous hydraulic conductivity field. The estimated parameter field is considered to be a random variable that varies continuously in space, meets the measurements of dependent quantities (such as the hydraulic head, the concentration of a transported solute or its arrival time) and shows the required spatial correlation (described by certain variogram models). This is a method of conditioning a parameter field to observations. Upon discretization, this results in as many parameters as elements of the computational grid. For a full three dimensional representation of the heterogeneous subsurface it is hardly sufficient to work with resolutions (up to one million parameters) of the model domain that can be achieved on a serial computer. The forward problems to be solved within the inversion procedure consists of the elliptic steady-state groundwater flow equation and the formally elliptic but nearly hyperbolic steady-state advection-dominated solute transport equation in a heterogeneous porous medium. Both equations are discretized by Finite Element Methods (FEM) using fully scalable domain decomposition techniques. Whereas standard conforming FEM is sufficient for the flow equation, for the advection dominated transport equation, which rises well known numerical difficulties at sharp fronts or boundary layers, we use the streamline diffusion approach. The arising linear systems are solved using efficient iterative solvers with an AMG (algebraic multigrid) pre-conditioner. During each iteration step of the inversion scheme one needs to solve a multitude of forward and adjoint problems in order to calculate the sensitivities of each measurement and the related cross-covariance matrix of the unknown parameters and the observations. In order to reduce interprocess communications and to improve the scalability of the code on larger clusters
NASA Astrophysics Data System (ADS)
Esbrí, José M.; Higueras, Pablo; López-Berdonces, Miguel A.; García-Noguero, Eva M.; González-Corrochano, Beatriz; Fernández-Calderón, Sergio; Martínez-Coronado, Alba
2015-04-01
Puertollano is the biggest industrial city of Castilla-La Mancha, with 48,086 inhabitants. It is located 250 km South of Madrid in the North border of the Ojailén River valley. The industrial area includes a big coal open pit (ENCASUR), two power plants (EON and ELCOGAS), a petrochemical complex (REPSOL) and a fertiliser factory (ENFERSA), all located in the proximities of the town. These industries suppose a complex scenario in terms of metals and metalloids emissions. For instance, mercury emissions declared to PRTR inventory during 2010 were 210 kg year-1 (REPSOL), 130 kg year-1 (ELCOGAS) and 11,9 kg year-1 (EON). Besides it still remains an unaccounted possibly of diffuse sources of other potentially toxic elements coming from the different industrial sites. Multielemental analyses of soils from two different depths covering the whole valley were carried out by means of XRF with a portable Oxford Instruments device. Geostatistical data treatment was performed using SURFER software, applying block kriging to obtain interpolation maps for the study area. Semivariograms of elemental concentrations make a clear distinction between volatile (Hg, Se) and non-volatile elements (Cu, Ni), with differences in scales and variances between the two soil horizons considered. Semivariograms also show different models for elements emitted by combustion processes (Ni) and for anomalous elements from geological substrate (Pb, Zn). In addition to differences in anisotropy of data, these models reflect different forms of elemental dispersion; despite this, identification of particular sources for the different elements is not possible for this geochemical data set.
Estimating Solar PV Output Using Modern Space/Time Geostatistics (Presentation)
Lee, S. J.; George, R.; Bush, B.
2009-04-29
This presentation describes a project that uses mapping techniques to predict solar output at subhourly resolution at any spatial point, develop a methodology that is applicable to natural resources in general, and demonstrate capability of geostatistical techniques to predict the output of a potential solar plant.
H-Area/ITP Geostatistical Assessment of In-Situ and Engineering Properties
Wyatt, D.; Bartlett, S.F.; Rouhani, S.; Lin, Y.
1995-09-05
This report describes the results of a geostatistical investigation conducted in response to tasks and requirements set out in the SRS Request for Proposal Number 93044EQ, dated January 4, 1994. The project tasks were defined in the Statement of Scope (SOS) Number 93044, Revision 0, dated December 1, 1993, developed by the Savannah River Site Geotechnical Services Department.
NASA Astrophysics Data System (ADS)
Fernández-Trincado, J. G.; Robin, A. C.; Bienaymé, O.; Reylé, C.; Valenzuela, O.; Pichardo, B.
2014-07-01
In this contributed poster we present a preliminary attempt to compute a non-axisymmetric potential together with previous axisymmetric potential of the Besançon galaxy model. The contribution by non-axisymmetric components are modeled by the superposition of inhomogeneous ellipsoids to approximate the triaxial bar and superposition of homogeneous oblate spheroids for a stellar halo, possibly triaxial. Finally, we have computed the potential and force field for these non-axisymmetric components in order to constraint the total mass of the Milky Way. We present preliminary results for the rotation curve and the contribution of the bar to it. This approach will allow future studies of dynamical constraints from comparisons of kinematical simulations with upcoming surveys such as RAVE, BRAVA, APOGEE, and GAIA in the near future. More details, are presented in https://gaia.ub.edu/Twiki/pub/GREATITNFC/ProgramFinalconference/Poster_JG.Fern%e1ndez.pdf.
NASA Astrophysics Data System (ADS)
Agustsson, K. S.; Gordon, S. M.; Long, S. P.; Seward, G. G.; Zeiger, K. J.; Penfold, M. L.
2013-12-01
The study of modern continent-continent collision provides insight into the links between the upper and lower crust, including the processes involved in the deep burial and exhumation of crustal rocks. Rocks of the Greater Himalayan Sequence (GHS), which were buried to mid- to lower-crustal levels, are exposed throughout the Himalayan orogenic belt, between the top-to-the-south Main Central Thrust and the top-to-the-north South Tibetan Detachment. The GHS consists of orthogneiss, metasedimentary rocks, and large-scale (>100 km2) leucogranite bodies. Within the Bhutan Himalaya, the top-to-the south Kakhtang Thrust (KT) separates the GHS into upper (GHSu) and lower (GHSl) structural levels. Previous studies have mapped the location of the KT by the crossing of the second sillimanite isograd and by a significant increase in the volume of crystallized melt. Previous work in Bhutan has mainly focused on the GHSl, whereas the extrusion of the higher-temperature GHSu has not been well studied, and there is little quantitative data describing the P-T history of these rocks. In order to test between different end-member models for the exhumation of the GHSu, including channel flow and critical taper, new thermobarometry data was collected from a transect of samples across the KT. The channel-flow model predicts that the GHSu would have achieved peak upper-amphibolite facies P-T conditions followed by retrograde, near-isothermal decompression. In contrast, the critical-taper model predicts near-isobaric cooling of the GHSu. The electron microprobe at UC-Santa Barbara was used to measure the composition of and test for zoning within garnet, plagioclase, and biotite. Garnets in all four samples are typically subhedral to euhedral and show relatively weak zonation and flat Mg, Fe, and Ca profiles. A few garnets do exhibit bell-shaped Mn and Ca profiles. In addition, a ca. 100 μm rim high in Mg, Ca and Mn but low in Fe is present on all garnets and is indicative of diffusional
Burlon, A A; Girola, S; Valda, A A; Minsky, D M; Kreiner, A J; Sánchez, G
2011-12-01
This work reports on the characterisation of a neutron beam shaping assembly (BSA) prototype and on the preliminary modelling of a treatment room for BNCT within the framework of a research programme for the development and construction of an accelerator-based BNCT irradiation facility in Buenos Aires, Argentina. The BSA prototype constructed has been characterised by means of MCNP simulations as well as a set of experimental measurements performed at the Tandar accelerator at the National Atomic Energy Commission of Argentina.
A Combination of Preliminary Electroweak Measurements And Constraints on the Standard Model (2004)
Abbaneo, D.
2005-03-07
This note presents a combination of published and preliminary electroweak results from the four LEP collaborations and the SLD collaboration which were prepared for the 2004 summer conferences. Averages from Z resonance results are derived for hadronic and leptonic cross sections, the leptonic forward-backward asymmetries, the {tau} polarization asymmetries, the b{bar b} and c{bar c} partial widths and forward-backward asymmetries and the q{bar q} charge asymmetry. Above the Z resonance, averages are derived for di-fermion cross sections and forward-backward asymmetries, photon-pair, W-pair, Z-pair, single-W and single-Z cross sections, electroweak gauge boson couplings, W mass and width and W decay branching ratios. Also, an investigation of the interference of photon and Z-boson exchange is presented, and colour reconnection and Bose-Einstein correlation analyses in W-pair production are combined. The main changes with respect to the experimental results presented in summer 2003 are updates to the W branching fractions and four-fermion cross sections measured at LEP-2, and the SLD/LEP heavy-flavour results measured at the Z pole. The results are compared with precise electroweak measurements from other experiments, notably the final result on the electroweak mixing angle determined in neutrino-nucleon scattering by the NuTeV collaboration, the latest result in atomic parity violation in Caesium, and the measurement of the electroweak mixing angle in Moller scattering. The parameters of the Standard Model are evaluated, first using the combined LEP electroweak measurements, and then using the full set of high-Q{sup 2} electroweak results.
NASA Astrophysics Data System (ADS)
Caseri, Angelica; Ramos, Maria Helena; Javelle, Pierre; Leblois, Etienne
2016-04-01
Floods are responsible for a major part of the total damage caused by natural disasters. Nowcasting systems providing public alerts to flash floods are very important to prevent damages from extreme events and reduce their socio-economic impacts. The major challenge of these systems is to capture high-risk situations in advance, with good accuracy in the intensity, location and timing of future intense precipitation events. Flash flood forecasting has been studied by several authors in different affected areas. The majority of the studies combines rain gauge data with radar imagery advection to improve prediction for the next few hours. Outputs of Numerical Weather Prediction (NWP) models have also been increasingly used to predict ensembles of extreme precipitation events that might trigger flash floods. One of the challenges of the use of NWP for ensemble nowcasting is to successfully generate ensemble forecasts of precipitation in a short time calculation period to enable the production of flood forecasts with sufficient advance to issue flash flood alerts. In this study, we investigate an alternative space-time geostatistical framework to generate multiple scenarios of future rainfall for flash floods nowcasting. The approach is based on conditional simulation and an advection method applied within the Turning Bands Method (TBM). Ensemble forecasts of precipitation fields are generated based on space-time properties given by radar images and precipitation data collected from rain gauges during the development of the rainfall event. The results show that the approach developed can be an interesting alternative to capture precipitation uncertainties in location and intensity and generate ensemble forecasts of rainfall that can be useful to improve alerts for flash floods, especially in small areas.
Oil Spill Detection and Modelling: Preliminary Results for the Cercal Accident
NASA Astrophysics Data System (ADS)
da Costa, R. T.; Azevedo, A.; da Silva, J. C. B.; Oliveira, A.
2013-03-01
two-dimensional surface plume transport model VOILS [1] with the oil spreading formulation enabled. The remaining oil weathering processes (evaporation, emulsification, dispersion and dissolution in the water column) and shoreline retention were disregarded. The computational structure of the model is based on Eulerian-Lagrangian formulations, horizontal unstructured mesh discretization and it is soft-coupled with the tri-dimensional hydrodynamic model SELFE - Semi-Implicit Eulerian Lagrangian Finite Element [15] that uses hybrid sigma-Z coordinates in the vertical. The preliminary results of this hindcast simulation for the Cercal oil spill are presented and compared with available satellite SAR images. The forcings used play an important role in the final results. During the late stage spreading phases of the oil, about one month after the spill, the Douro River outflow is best seen in the SAR images. The morphology of the river outflow is discussed according to traditional coastal dynamics, and compared with model results. In addition to several interesting physical features that were identified, we report on the generation of Internal Solitary Waves (ISW) in the vicinity of the Douro River Plume (DRP). It is well known that trains of short-period internal waves can be generated by river plumes (such as the Columbia River). The internal structure of the observed internal waves (elevation waves or mode-2 versus mode-1 internal waves) is discussed based on the SAR signatures and available stratification. The present work has been conducted under an FCT - Fundaç ão para a Ciência e a Tecnologia / MCTES - Ministério da Ciência, Tecnologia e Ensino Superior (PIDDAC - Programa de Investimentos e Despesas de Desenvolvimento da Administraç ão Central) Portuguese funded project entitled PAC:MAN Pollution accidents in coastal areas: a Risk management system (PTDC/AACAMB/113469/2008).
Geostatistical description of lithofacies distribution in the aquifer system of Cremona, Italy.
NASA Astrophysics Data System (ADS)
Guadagnini, L.; Riva, M.; Salmaso, M.; Saraceni, F.; Straface, S.; Guadagnini, A.
2009-04-01
We develop two alternative conceptual models to describe the heterogeneous spatial distribution of geomaterials within the groundwater system in the proximity of the city of Cremona, Italy. The key hydrogeologic feature of the region is the occurrence of the Springs Belt which develops across the entire Lombardia region and provides a major source of fresh water for agricultural needs. During recent years the natural springs of the Cremona aquifer have been increasingly threatened by over-abstraction and contamination by agricultural fertilizers. The area investigated includes the main natural springs in the region, and is located between the Adda and the Serio rivers, covering a surface of approximately 785 km2. The groundwater system is constituted by two main productive aquifers, which are separated by a locally discontinuous aquitard. The vertical variability of geomaterials distribution inferred from available well logs suggests that the system is relatively heterogeneous on the given observation scale. Lithofacies distribution within each identified aquifer is estimated upon considering two alternative conceptual models: (a) a composite medium scheme, and (b) a multiple-continua approach. In the former scenario, the system is conceptualized as composed by disjoint blocks of different materials, the boundaries of which can be uncertain. The latter approach assumes that the porous medium is composed by a set of overlapping continua, whose relative fraction at a given location can be uncertain. We start by classifying available sedimentological information and group the various identified lithotypes into five separate clusters. An extension of the indicator-based approach of Guadagnini et al. [2004] is then developed in order to provide a geostatistical chracterization of lithotypes distribution when the system is described as a composite medium. A multi-continua description is achieved by means of multiple indicator Kriging techniques. With the aid of formal
Gd uptake experiments for preliminary set of functionalized adsorbents (with content model)
Clinton Noack
2015-03-16
These data summarize adsorption experiments conducted with Gd in 0.5 M NaCl. Results represent preliminary, proof-of-concept data utilizing fine-powder silica gel as the adsorbent support. Future testing will focus on larger, application-appropriate beads.
NASA Astrophysics Data System (ADS)
Lee, J.; Mukerji, T.; Tompkins, M. J.
2012-12-01
Joint integration of seismic and electromagnetic (EM) data has been studied to better characterize hydrocarbon reservoirs because they are sensitive to different reservoir properties. Most of them, however, applied deterministic joint inversion which provides the best estimate of the spatial distribution of reservoir properties in least square sense. Although this way of integrating two different data helps to obtain a more improved reservoir model matching both data, it gives only one reservoir model. But, numerous reservoirs can be consistent with seismic and EM data obtained from field measurements. Therefore, uncertainty associated with reservoir models should be quantified for reducing risks of making wrong decisions in reservoir management. We suggest statistical integration with a new upscaling scheme, which simulates the joint probability distribution of field scale seismic and EM data as well as reservoir properties, such as facies, porosity, and fluid saturation, not only to estimate reservoir properties but also to assess uncertainty of the estimates. Statistical data integration has been used (e.g., Lucet and Mavko, 1991; Avseth et al., 2001a, 2001b; Mukerji et al., 1998, 2001; Eidsvik et al., 2004) to characterize reservoirs from seismic attributes in geophysics. Main issue in applying statistical integration to joint seismic and EM data is the scale difference of two data because seismic (crosswell or surface seismic) and EM measurements (crosswell EM or CSEM) represent different volumes of a reservoir. In this research, geologically analogous reservoirs to the target reservoir were generated by unconstrained simulation with multipoint geostatistical algorithm, SNESIM (Strebelle, 2000, 2002). Well-log scale seismic and EM attributes were randomly assigned to the analogous reservoirs using conditional probability distributions of the attributes given facies obtained from well log analysis. Forward modeling and inversion of the analogous reservoirs were
NASA Astrophysics Data System (ADS)
Comunian, Alessandro; Felletti, Fabrizio; Giacobbo, Francesca; Giudici, Mauro; Bersezio, Riccardo
2015-04-01
When building a geostatistical model of the hydrofacies distribution in a volume block it is important to include all the relevant available information. Localised information about the observed hydrofacies (hard data) are routinely included in the simulation procedures. Non stationarities in the hydrofacies distribution can be handled by considering auxiliary (soft) data extracted, for example, from the results of geophysical surveys. This piece of information can be included as auxiliary data both in variogram based methods (i.e. co-Kriging) and in multiple-point statistics (MPS) methods. The latter methods allow to formalise some soft knowledge about the considered model of heterogeneity using a training image. However, including information related to the stratigraphic hierarchy in the training image is rarely straightforward. In this work, a methodology to include the information about the stratigraphic hierarchy in the simulation process is formalised and implemented in a MPS framework. The methodology is applied and tested by reconstructing two model blocks of alluvial sediments with an approximate volume of few cubic meters. The external faces of the blocks, exposed in a quarry, were thoroughly mapped and their stratigraphic hierarchy was interpreted in a previous study. The bi-dimensional (2D) maps extracted from the faces, which are used as training images and as hard data, present a vertical trend and complex stratigraphic architectures. The training images and the conditioning data are classified according to the proposed stratigraphic hierarchy, and the hydrofacies codes are grouped to allow a sequence of interleaved binary MPS simulation. Every step of the simulation sequence corresponds to a group of hydrofacies defined in the stratigraphic hierarchy. The blocks simulated with the proposed methodology are compared with blocks simulated with a standard MPS approach. The comparisons are performed on many realisations using connectivity indicators and
Edwards, Lloyd A.; Paresol, Bernard
2014-09-01
This report of the geostatistical analysis results of the fire fuels response variables, custom reaction intensity and total dead fuels is but a part of an SRS 2010 vegetation inventory project. For detailed description of project, theory and background including sample design, methods, and results please refer to USDA Forest Service Savannah River Site internal report “SRS 2010 Vegetation Inventory GeoStatistical Mapping Report”, (Edwards & Parresol 2013).
RAST RS; RINKER MW; BAPANAALLI SK; DEIBLER JE; GUZMAN-LEONG CE; JOHNSON KI; KARRI NK; PILLI SP; SANBORN SE
2010-10-22
This document is a Phase I deliverable for the Single-Shell Tank Analysis of Record effort. This document is not the Analysis of Record. The intent of this document is to guide the Phase II detailed modeling effort. Preliminary finite element models for each of the tank types were developed and different case studies were performed on one or more of these tank types. Case studies evaluated include thermal loading, waste level variation, the sensitivity of boundary effects (soil radial extent), excavation slope or run to rise ratio, soil stratigraphic (property and layer thickness) variation at different farm locations, and concrete material property variation and their degradation under thermal loads. The preliminary analysis document reviews and preliminary modeling analysis results are reported herein. In addition, this report provides recommendations for the next phase of the SST AOR project, SST detailed modeling. Efforts and results discussed in this report do not include seismic modeling as seismic modeling is covered by a separate report. The combined results of both static and seismic models are required to complete this effort. The SST AOR project supports the US Department of Energy's (DOE) Office of River Protection (ORP) mission for obtaining a better understanding of the structural integrity of Hanford's SSTs. The 149 SSTs, with six different geometries, have experienced a range of operating histories which would require a large number of unique analyses to fully characterize their individual structural integrity. Preliminary modeling evaluations were conducted to determine the number of analyses required for adequate bounding of each of the SST tank types in the Detailed Modeling Phase of the SST AOR Project. The preliminary modeling was conducted in conjunction with the Evaluation Criteria report, Johnson et al. (2010). Reviews of existing documents were conducted at the initial stage of preliminary modeling. These reviews guided the topics that were
Mapping Oxygen-18 in Meteoric Precipitation over Peninsular Spain using Geostatistical Tools
NASA Astrophysics Data System (ADS)
Capilla, J. E.; Rodriguez Arevalo, J.; Castaño Castaño, S.; Diaz Teijeiro, M.; Heredia Diaz, J.; Sanchez del Moral, R.
2011-12-01
Rainfall isotopic composition is a valuable source of information to understand and model the complexity of natural hydrological systems. The identification of water recharge origins, water flow trajectories, residence times and pollutants movement in the hydrologic cycle can greatly benefit from this information. It is also very useful in other environmental issues associated to water movement in the biosphere. Although the potential of stable isotopes data in hydrology and climatic studies is promising, so far, it has been strongly limited by the availability of data. A major challenge is to extend sparse measurements of stable isotopes data to surrounding geographic areas taking into account other secondary variables as latitude, altitude and climate related parameters. Current state-of-the-art provides different approaches mainly based in deterministic interpolation techniques. In Spain a network called REVIP, made up by 16 nodes, is maintained by CEDEX (Centro de Estudios y Experimentación de Obras públicas). At REVIP nodes, stable isotopes in meteoric precipitation (Oxygen-18 and Deuterium) have been continuously recorded since the year 2000. This network provides a rare opportunity to study the patterns of spatial distribution over the whole country. In fact, some accurate regression models have already been proposed that map stable isotopes against latitude and altitude. Yet, these regressions maintain small residuals at the network nodes that are possibly caused by the local average features of climatic events. There is an ongoing effort to improve these maps that includes the identification of relevant climatic parameters and the application of geostatistical techniques. This paper describes the application of a regression kriging methodology to map oxygen-18 over peninsular Spain using REVIP data. The methodology includes a prior process to obtain normalized stable isotope concentrations that are independent of latitude and altitude, a structural
Weiland, E.F.; Connors, R.A.; Robinson, M.L.; Lindemann, J.W.; Meyer, W.T.
1982-01-01
A mineral assessment of the Arkansas Canyon Planning Unit was undertaken by Barringer Resources Inc., under the terms of contract YA-553-CTO-100 with the Bureau of Land Management, Colorado State Office. The study was based on a geochemical-geostatistical survey in which 700 stream sediment samples were collected and analyzed for 25 elements. Geochemical results were interpreted by statistical processing which included factor, discriminant, multiple regression and characteristic analysis. The major deposit types evaluated were massive sulfide-base metal, sedimentary and magmatic uranium, thorium vein, magmatic segregation, and carbonatite related deposits. Results of the single element data and multivariate geostatistical analysis indicate that limited potential exists for base metal mineralization near the Horseshoe, El Plomo, and Green Mountain Mines. Thirty areas are considered to be anomalous with regard to one or more of the geochemical parameters evaluated during this study. The evaluation of carbonatite related mineralization was restricted due to the lack of geochemical data specific to this environment.
2011-10-01
The Alden turbine was developed through the U.S. Department of Energy's (DOE's) former Advanced Hydro Turbine Systems Program (1994-2006) and, more recently, through the Electric Power Research Institute (EPRI) and the DOE's Wind & Water Power Program. The primary goal of the engineering study described here was to provide a commercially competitive turbine design that would yield fish passage survival rates comparable to or better than the survival rates of bypassing or spilling flow. Although the turbine design was performed for site conditions corresponding to 92 ft (28 m) net head and a discharge of 1500 cfs (42.5 cms), the design can be modified for additional sites with differing operating conditions. During the turbine development, design modifications were identified for the spiral case, distributor (stay vanes and wicket gates), runner, and draft tube to improve turbine performance while maintaining features for high fish passage survival. Computational results for pressure change rates and shear within the runner passage were similar in the original and final turbine geometries, while predicted minimum pressures were higher for the final turbine. The final turbine geometry and resulting flow environments are expected to further enhance the fish passage characteristics of the turbine. Computational results for the final design were shown to improve turbine efficiencies by over 6% at the selected operating condition when compared to the original concept. Prior to the release of the hydraulic components for model fabrication, finite element analysis calculations were conducted for the stay vanes, wicket gates, and runner to verify that structural design criteria for stress and deflections were met. A physical model of the turbine was manufactured and tested with data collected for power and efficiency, cavitation limits, runaway speed, axial and radial thrust, pressure pulsations, and wicket gate torque. All parameters were observed to fall within ranges
NASA Technical Reports Server (NTRS)
Betts, M.; Tsegaye, T.; Tadesse, W.; Coleman, T. L.; Fahsi, A.
1998-01-01
The spatial and temporal distribution of near surface soil moisture is of fundamental importance to many physical, biological, biogeochemical, and hydrological processes. However, knowledge of these space-time dynamics and the processes which control them remains unclear. The integration of geographic information systems (GIS) and geostatistics together promise a simple mechanism to evaluate and display the spatial and temporal distribution of this vital hydrologic and physical variable. Therefore, this research demonstrates the use of geostatistics and GIS to predict and display soil moisture distribution under vegetated and non-vegetated plots. The research was conducted at the Winfred Thomas Agricultural Experiment Station (WTAES), Hazel Green, Alabama. Soil moisture measurement were done on a 10 by 10 m grid from tall fescue grass (GR), alfalfa (AA), bare rough (BR), and bare smooth (BS) plots. Results indicated that variance associated with soil moisture was higher for vegetated plots than non-vegetated plots. The presence of vegetation in general contributed to the spatial variability of soil moisture. Integration of geostatistics and GIS can improve the productivity of farm lands and the precision of farming.
NASA Astrophysics Data System (ADS)
Ewers, B. E.; Adelman, J. D.; Mackay, D. S.; Loranty, M.; Traver, E.; Kruger, E. L.
2005-12-01
As direct measurements of tree transpiration via sap flux have become routine, the sample size of sap flux studies has dramatically increased. These large sample sizes now provide sufficient data for geostatistical analyses when measurement points are spatially explicit. In this study we tested whether 1) center-of-stand approaches to sap flux measurements and scaling are sufficient for characterizing stand level transpiration and 2) geostatistical techniques provide the information necessary for quantifying important stand and landscape level gradients. To maximize the inferences from our tests, we compared two contrasting forests: one dominated by lodgepole pine (Pinus contorta) in Wyoming and another dominated by trembling aspen (Populus tremuloides) in Wisconsin. The forest in Wyoming is characterized by low precipitation regimes dominated by snowfall while the forest in Wisconsin is characterized by moderate precipitation throughout the growing season. Analyses of the relationship between sample size and variance indicated an inflection point of 30 point pairs but the variance continued a shallow decline even at 100 point pairs. There was also evidence of species impacts on variance of transpiration that changed with environmental conditions. We used variogram analyses to quantify the spatial range of transpiration and found a variable spatial range across days with mean of approximately 30 m. The variability in spatial range could be partially explained by tree species' responses to soil moisture, vapor pressure deficit, and light based on current plant hydraulic knowledge. Our results confirm the utility of geostatistical techniques for quantifying and explaining transpiration in time and space.
Geostatistical estimation of signal-to-noise ratios for spectral vegetation indices
Ji, Lei; Zhang, Li; Rover, Jennifer R.; Wylie, Bruce K.; Chen, Xuexia
2014-01-01
In the past 40 years, many spectral vegetation indices have been developed to quantify vegetation biophysical parameters. An ideal vegetation index should contain the maximum level of signal related to specific biophysical characteristics and the minimum level of noise such as background soil influences and atmospheric effects. However, accurate quantification of signal and noise in a vegetation index remains a challenge, because it requires a large number of field measurements or laboratory experiments. In this study, we applied a geostatistical method to estimate signal-to-noise ratio (S/N) for spectral vegetation indices. Based on the sample semivariogram of vegetation index images, we used the standardized noise to quantify the noise component of vegetation indices. In a case study in the grasslands and shrublands of the western United States, we demonstrated the geostatistical method for evaluating S/N for a series of soil-adjusted vegetation indices derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The soil-adjusted vegetation indices were found to have higher S/N values than the traditional normalized difference vegetation index (NDVI) and simple ratio (SR) in the sparsely vegetated areas. This study shows that the proposed geostatistical analysis can constitute an efficient technique for estimating signal and noise components in vegetation indices.
Geostatistical estimation of signal-to-noise ratios for spectral vegetation indices
NASA Astrophysics Data System (ADS)
Ji, Lei; Zhang, Li; Rover, Jennifer; Wylie, Bruce K.; Chen, Xuexia
2014-10-01
In the past 40 years, many spectral vegetation indices have been developed to quantify vegetation biophysical parameters. An ideal vegetation index should contain the maximum level of signal related to specific biophysical characteristics and the minimum level of noise such as background soil influences and atmospheric effects. However, accurate quantification of signal and noise in a vegetation index remains a challenge, because it requires a large number of field measurements or laboratory experiments. In this study, we applied a geostatistical method to estimate signal-to-noise ratio (S/N) for spectral vegetation indices. Based on the sample semivariogram of vegetation index images, we used the standardized noise to quantify the noise component of vegetation indices. In a case study in the grasslands and shrublands of the western United States, we demonstrated the geostatistical method for evaluating S/N for a series of soil-adjusted vegetation indices derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The soil-adjusted vegetation indices were found to have higher S/N values than the traditional normalized difference vegetation index (NDVI) and simple ratio (SR) in the sparsely vegetated areas. This study shows that the proposed geostatistical analysis can constitute an efficient technique for estimating signal and noise components in vegetation indices.
ERIC Educational Resources Information Center
Yoo, Hyung Chol; Burrola, Kimberly S.; Steger, Michael F.
2010-01-01
This investigation is a preliminary report on a new measure of internalization of the model minority myth. In 3 studies, there was evidence for the validation of the 15-item Internalization of the Model Minority Myth Measure (IM-4), with 2 subscales. The Model Minority Myth of Achievement Orientation referred to the myth of Asian Americans'…
Liu, Geng; Niu, Junjie; Zhang, Chao; Guo, Guanlin
2015-12-01
Data distribution is usually skewed severely by the presence of hot spots in contaminated sites. This causes difficulties for accurate geostatistical data transformation. Three types of typical normal distribution transformation methods termed the normal score, Johnson, and Box-Cox transformations were applied to compare the effects of spatial interpolation with normal distribution transformation data of benzo(b)fluoranthene in a large-scale coking plant-contaminated site in north China. Three normal transformation methods decreased the skewness and kurtosis of the benzo(b)fluoranthene, and all the transformed data passed the Kolmogorov-Smirnov test threshold. Cross validation showed that Johnson ordinary kriging has a minimum root-mean-square error of 1.17 and a mean error of 0.19, which was more accurate than the other two models. The area with fewer sampling points and that with high levels of contamination showed the largest prediction standard errors based on the Johnson ordinary kriging prediction map. We introduce an ideal normal transformation method prior to geostatistical estimation for severely skewed data, which enhances the reliability of risk estimation and improves the accuracy for determination of remediation boundaries. PMID:26300353
Lasmar, O; Zanetti, R; dos Santos, A; Fernandes, B V
2012-08-01
One of the fundamental steps in pest sampling is the assessment of the population distribution in the field. Several studies have investigated the distribution and appropriate sampling methods for leaf-cutting ants; however, more reliable methods are still required, such as those that use geostatistics. The objective of this study was to determine the spatial distribution and infestation rate of leaf-cutting ant nests in eucalyptus plantations by using geostatistics. The study was carried out in 2008 in two eucalyptus stands in Paraopeba, Minas Gerais, Brazil. All of the nests in the studied area were located and used for the generation of GIS maps, and the spatial pattern of distribution was determined considering the number and size of nests. Each analysis and map was made using the R statistics program and the geoR package. The nest spatial distribution in a savanna area of Minas Gerais was clustered to a certain extent. The models generated allowed the production of kriging maps of areas infested with leaf-cutting ants, where chemical intervention would be necessary, reducing the control costs, impact on humans, and the environment.
NASA Astrophysics Data System (ADS)
Namysłowska-Wilczyńska, Barbara
2016-04-01
This paper presents selected results of research connected with the development of a (3D) geostatistical hydrogeochemical model of the Klodzko Drainage Basin, dedicated to the spatial and time variation in the selected quality parameters of underground water in the Klodzko 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: ammonium ion [gNH4+/m3], NO3- (nitrate ion) [gNO3/m3], PO4-3 (phosphate ion) [gPO4-3/m3], total organic carbon C (TOC) [gC/m3], pH redox potential and temperature C [degrees], 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 and time variation in the quality parameters was analyzed on the basis of archival data (period 1977÷1999) for 22 (pump and siphon) wells with a depth ranging from 9.5 to 38.0 m b.g.l., later data obtained (November 2011) from tests of water taken from 14 existing wells. The wells were built in the years 1954÷1998. The water abstraction depth (difference between the terrain elevation and the dynamic water table level) is ranged from 276÷286 m a.s.l., with an average of 282.05 m a.s.l. Dynamic water table level is contained between 6.22 m÷16.44 m b.g.l., with a mean value of 9.64 m b.g.l. The latest data (January 2012) acquired from 3 new piezometers, with a depth of 9÷10m, which were made in other locations in the relevant area. Thematic databases, containing original data on coordinates X, Y (latitude, longitude) and Z (terrain elevation and time - years) and on regionalized variables, i.e. the underground water quality parameters in the Klodzko water intake area determined for different analytical configurations (22 wells, 14 wells, 14 wells + 3 piezometers), were created. Both archival data (acquired in the years 1977÷1999) and the latest data (collected in 2011÷2012) were analyzed
Puchezh-Katunki Impact Crater: Preliminary Model of Hydrothermal Circulation System
NASA Astrophysics Data System (ADS)
Masaitis, V. L.; Naumov, M. V.
1993-07-01
Research (using the results of deep drilling) of hydrothermal alteration in the Puchezh-Katunki Crater [1,2] enables us to construct a preliminary model of a hot-water circulation system in this giant astrobleme. Unlike previous reconstructions [3] that consider a circulation system originated in connection with the mass of impact melt in an ideal astrobleme, we examine this process in the impact crater as a whole. Considerable hydrothermal alteration of rocks is restricted by central uplift of the Puchezh-Katunki Crater. The preimpact temperature of the uplift's crystalline rocks (those that occur at 5-6 km in depth before impact) could be more than 100 degrees C. The principal factors that caused the creation of the circulation system are (1) the thermal field of the massif of brecciated and heated rocks and (2) crater lake formation. Shock- and friction-enhanced heating coupled with the influence of injecting impact melt masses generated an ellipsoidal-shaped thermal anomaly for 5-6 km in depth and near 600 km in volume. The heated massif was characterized by temperature values after equilibration from 500 degrees-600 degrees C in the center to 100 degrees-200 degrees C at the edges. The porosity of rocks decreased at depth and outward from the center as well. Hydrothermal convection took place when water from a ring trough lake infiltrated the lens of hot and porous impact breccia and basement rocks, reaching the surface at the uplift's margins and in the bottom of the central pit. A meteoric origin of circulated water is corroborated by isotopic values of fracture-filling calcite (delta ^18O = 21-24 per mil SMOW; delta ^13C = -20-3 per mil PDB) and anhydrite (delta ^18O = 8-10 per mil SMOW). There is no reliable information about addition of any juvenile substance in the circulation system. These facts support the subsurface origin of hydrothermal circulation. The united regressive hydrothermal process may be subdivided into three successive stages (Fig. 1): 1
Crowell, Susan Ritger; Amin, Shantu G.; Anderson, Kim A.; Krishnegowda, Gowdahalli; Sharma, Arun K.; Soelberg, Jolen J.; Williams, David E.; Corley, Richard A.
2011-12-15
Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous environmental contaminants generated as byproducts of natural and anthropogenic combustion processes. Despite significant public health concern, physiologically based pharmacokinetic (PBPK) modeling efforts for PAHs have so far been limited to naphthalene, plus simpler PK models for pyrene, nitropyrene, and benzo[a]pyrene (B[a]P). The dearth of published models is due in part to the high lipophilicity, low volatility, and myriad metabolic pathways for PAHs, all of which present analytical and experimental challenges. Our research efforts have focused upon experimental approaches and initial development of PBPK models for the prototypic PAH, B[a]P, and the more potent, albeit less studied transplacental carcinogen, dibenzo[def,p]chrysene (DBC). For both compounds, model compartments included arterial and venous blood, flow limited lung, liver, richly perfused and poorly perfused tissues, diffusion limited fat, and a two compartment theoretical gut (for oral exposures). Hepatic and pulmonary metabolism was described for both compounds, as were fractional binding in blood and fecal clearance. Partition coefficients for parent PAH along with their diol and tetraol metabolites were estimated using published algorithms and verified experimentally for the hydroxylated metabolites. The preliminary PBPK models were able to describe many, but not all, of the available data sets, comprising multiple routes of exposure (oral, intravenous) and nominal doses spanning several orders of magnitude. Supported by Award Number P42 ES016465 from the National Institute of Environmental Health Sciences. -- Highlights: Black-Right-Pointing-Pointer We present PBPK models for benzo[a]pyrene (B[a]P) and dibenzo[def,p]chrysene (DBC). Black-Right-Pointing-Pointer B[a]P model accurately predicts data from multiple sources over a wide dose range. Black-Right-Pointing-Pointer DBC model was based on the B[a]P model as less chemical specific
Jones, G.F.
1984-01-01
A preliminary model is presented for heat transport within a tongue-and-reservoir liquid diode for passive solar heating. The diode consists of a rectangular vertical slot (tongue) extending from the bottom of a rectangular-shaped reservoir at the reservoir's front face. Water is used as the working fluid in the tongue and reservoir. Solar radiation is incident on the front face of the tongue, which also loses heat to the outside, while radiation and convection transport heat from the back of the reservoir to the building. Convection transports heat when the tongue is irradiated; however, when convection ceases and the temperature of the tongue cools below that of the reservoir (from exposure to the outside temperature), the reservoir stratifies, and the primary heat loss mechanism is conduction through the tongue and its fluid. The result is a passive solar component that may outperform most others. Flow in the tongue is treated as boundary layer flow; the integral forms of the governing equations are combined to form a single equation governing the local boundary layer thickness. The results are shown to depend upon the Grashof, Prandtl, and heat-loss Biot numbers. Results from this model agree well with those from our flow visualization experiments. A model is also proposed for diode heat transport processes during cool-down. In this model, and empirical coefficient accounts for the weak convective mixing that occurs in the reservoir during this phase. Preliminary results indicate the coefficient to be spatially dependent but independent of time and reservoir temperature. More experiments are planned to further validate both of the models described above.
NASA Astrophysics Data System (ADS)
Laloy, Eric; Linde, Niklas; Jacques, Diederik; Mariethoz, Grégoire
2016-04-01
The sequential geostatistical resampling (SGR) algorithm is a Markov chain Monte Carlo (MCMC) scheme for sampling from possibly non-Gaussian, complex spatially-distributed prior models such as geologic facies or categorical fields. In this work, we highlight the limits of standard SGR for posterior inference of high-dimensional categorical fields with realistically complex likelihood landscapes and benchmark a parallel tempering implementation (PT-SGR). Our proposed PT-SGR approach is demonstrated using synthetic (error corrupted) data from steady-state flow and transport experiments in categorical 7575- and 10,000-dimensional 2D conductivity fields. In both case studies, every SGR trial gets trapped in a local optima while PT-SGR maintains an higher diversity in the sampled model states. The advantage of PT-SGR is most apparent in an inverse transport problem where the posterior distribution is made bimodal by construction. PT-SGR then converges towards the appropriate data misfit much faster than SGR and partly recovers the two modes. In contrast, for the same computational resources SGR does not fit the data to the appropriate error level and hardly produces a locally optimal solution that looks visually similar to one of the two reference modes. Although PT-SGR clearly surpasses SGR in performance, our results also indicate that using a small number (16-24) of temperatures (and thus parallel cores) may not permit complete sampling of the posterior distribution by PT-SGR within a reasonable computational time (less than 1-2 weeks).
Barabás, N; Goovaerts, P; Adriaens, P
2001-08-15
Contaminated sediment management is an urgent environmental and regulatory issue worldwide. Because remediation is expensive, sound quantitative assessments of uncertainty aboutthe spatial distribution of contaminants are critical, butthey are hampered bythe physical complexity of sediment environments. This paper describes the use of geostatistical modeling approaches to quantify uncertainty of 2,3,7,8-tetrachlorodibenzo-p-dioxin concentrations in Passaic River (New Jersey) sediments and to incorporate this information in decision-making processes, such as delineation of contaminated areas and additional sampling needs. First, coordinate transformation and analysis of three-dimensional semivariograms were used to describe and modelthe directional variability accounting forthe meandering course of the river. Then, indicator kriging was employed to provide models of local uncertainty at unsampled locations without requiring a prior transform (e.g. log-normal) of concentrations. Cross-validation results show that the use of probability thresholds leads to more efficient delineation of contaminated areas than a classification based on the exceedence of regulatory thresholds by concentration estimates. Depending on whether additional sampling aims at reducing prediction errors or misclassification rates, the variance of local probability distributions or a measure of the expected closeness to the regulatory threshold can be used to locate candidate locations. PMID:11529567
Park, Jinyong; Balasingham, P; McKenna, Sean Andrew; Pinnaduwa H.S.W. Kulatilake
2004-09-01
Sandia National Laboratories, under contract to Nuclear Waste Management Organization of Japan (NUMO), is performing research on regional classification of given sites in Japan with respect to potential volcanic disruption using multivariate statistics and geo-statistical interpolation techniques. This report provides results obtained for hierarchical probabilistic regionalization of volcanism for the Sengan region in Japan by applying multivariate statistical techniques and geostatistical interpolation techniques on the geologic data provided by NUMO. A workshop report produced in September 2003 by Sandia National Laboratories (Arnold et al., 2003) on volcanism lists a set of most important geologic variables as well as some secondary information related to volcanism. Geologic data extracted for the Sengan region in Japan from the data provided by NUMO revealed that data are not available at the same locations for all the important geologic variables. In other words, the geologic variable vectors were found to be incomplete spatially. However, it is necessary to have complete geologic variable vectors to perform multivariate statistical analyses. As a first step towards constructing complete geologic variable vectors, the Universal Transverse Mercator (UTM) zone 54 projected coordinate system and a 1 km square regular grid system were selected. The data available for each geologic variable on a geographic coordinate system were transferred to the aforementioned grid system. Also the recorded data on volcanic activity for Sengan region were produced on the same grid system. Each geologic variable map was compared with the recorded volcanic activity map to determine the geologic variables that are most important for volcanism. In the regionalized classification procedure, this step is known as the variable selection step. The following variables were determined as most important for volcanism: geothermal gradient, groundwater temperature, heat discharge, groundwater
Vuotto, Stefanie C.; Procidano, Mary E.; Annunziato, Rachel A.
2015-01-01
The current study presents preliminary correlational data used to develop a model depicting the psychosocial pathways that lead to the health behaviors of survivors of childhood and young-adult cancer. Data collected from a sample of 18- to 30-year-old cancer survivors (n = 125) was used to examine the relations among interpersonal support and nonsupport, personal agency, avoidance, depressive symptoms and self-efficacy as they related to health behaviors. The outcome measures examined included tobacco and alcohol use, diet, exercise, sunscreen use, medication compliance and follow-up/screening practices. Correlational analyses revealed a number of significant associations among variables. Results are used to inform the development of a health behavior model. Implications for health promotion and survivorship programming are discussed, as well as directions for future research. PMID:27417357
Taha, Haider
1998-06-15
Previous atmospheric modeling efforts that concentrated on the Los Angeles Basin suggested beneficial and significant air quality impacts from cool cities strategies. This paper discusses an extension of similar modeling efforts to three regions, Atlanta GA, Dallas - Ft. Worth TX, and Nashville TN, that experience smog and air quality problems. According to the older ozone air quality standard (120 ppb), these regions were classified as serious, moderate, and marginal, respectively, but may be out of compliance with respect to the newer, 80-ppb/8-hours standard. Results from this exploratory modeling work suggest a range of possible impacts on meteorological and air quality conditions. For example, peak ozone concentrations during each region's respective episode could be decreased by 1-6 ppb (conservative and optimistic scenarios, respectively) in Nashville, 5-15 ppb in Dallas - Fort Worth, and 5-12 ppb in Atlanta following implementation of cool cities. The reductions are generally smaller than those obtained from simulating the Los Angeles Basin but are still significant. In all regions, the simulations suggest, the net, domain-wide effects of cool cities are reductions in ozone mass and improvements in air quality. In Atlanta, Nashville, and Dallas, urban areas benefiting from reduced smog reach up to 8460, 7350, and 12870 km{sup 2} in area, respectively. Results presented in this paper should be taken as exploratory and preliminary. These will most likely change during a more comprehensive modeling study to be started soon with the support of the US Environmental Protection Agency. The main purpose of the present project was to obtain the initial data (emission inventories) for these regions, simulate meteorological conditions, and perform preliminary sensitivity analysis. In the future, additional regions will be simulated to assess the potential of cool cities in improving urban air quality.
Taha, Haider
2001-01-01
In this preliminary and relatively short modeling effort, an initial assessment is made for the potential air quality implications of climate change in California. The focus is mainly on the effects of changes in temperature and related meteorological and emission factors on ozone formation. Photochemical modeling is performed for two areas in the state: the Los Angeles Basin and the Sacramento Valley.
McCann, M.W. Jr.; Boissonnade, A.C.
1988-05-01
As part of an ongoing program, Lawrence Livermore National Laboratory (LLNL) is directing the Natural Phenomena Hazards Modeling Project (NPHMP) on behalf of the Department of Energy (DOE). A major part of this effort is the development of probabilistic definitions of natural phenomena hazards; seismic, wind, and flood. In this report the first phase of the evaluation of flood hazards at DOE sites is described. Unlike seismic and wind events, floods may not present a significant threat to the operations of all DOE sites. For example, at some sites physical circumstances may exist that effectively preclude the occurrence of flooding. As a result, consideration of flood hazards may not be required as part of the site design basis. In this case it is not necessary to perform a detailed flood hazard study at all DOE sites, such as those conducted for other natural phenomena hazards, seismic and wind. The scope of the preliminary flood hazard analysis is restricted to evaluating the flood hazards that may exist in proximity to a site. The analysis does involve an assessment of the potential encroachment of flooding on-site at individual facility locations. However, the preliminary flood hazard assessment does not consider localized flooding at a site due to precipitation (i.e., local run-off, storm sewer capacity, roof drainage). These issues are reserved for consideration by the DOE site manager. 11 refs., 84 figs., 61 tabs.
Bhattacharya, Basabdatta S; Patterson, Cameron; Galluppi, Francesco; Durrant, Simon J; Furber, Steve
2014-01-01
We present a preliminary study of a thalamo-cortico-thalamic (TCT) implementation on SpiNNaker (Spiking Neural Network architecture), a brain inspired hardware platform designed to incorporate the inherent biological properties of parallelism, fault tolerance and energy efficiency. These attributes make SpiNNaker an ideal platform for simulating biologically plausible computational models. Our focus in this work is to design a TCT framework that can be simulated on SpiNNaker to mimic dynamical behavior similar to Electroencephalogram (EEG) time and power-spectra signatures in sleep-wake transition. The scale of the model is minimized for simplicity in this proof-of-concept study; thus the total number of spiking neurons is ≈1000 and represents a "mini-column" of the thalamocortical tissue. All data on model structure, synaptic layout and parameters is inspired from previous studies and abstracted at a level that is appropriate to the aims of the current study as well as computationally suitable for model simulation on a small 4-chip SpiNNaker system. The initial results from selective deletion of synaptic connectivity parameters in the model show similarity with EEG power spectra characteristics of sleep and wakefulness. These observations provide a positive perspective and a basis for future implementation of a very large scale biologically plausible model of thalamo-cortico-thalamic interactivity-the essential brain circuit that regulates the biological sleep-wake cycle and associated EEG rhythms.
Duarte, F; Calvo, M V; Borges, A; Scatoni, I B
2015-08-01
The oriental fruit moth, Grapholita molesta (Busck), is the most serious pest in peach, and several insecticide applications are required to reduce crop damage to acceptable levels. Geostatistics and Geographic Information Systems (GIS) are employed to measure the range of spatial correlation of G. molesta in order to define the optimum sampling distance for performing spatial analysis and to determine the current distribution of the pest in peach orchards of southern Uruguay. From 2007 to 2010, 135 pheromone traps per season were installed and georeferenced in peach orchards distributed over 50,000 ha. Male adult captures were recorded weekly from September to April. Structural analysis of the captures was performed, yielding 14 semivariograms for the accumulated captures analyzed by generation and growing season. Two sets of maps were constructed to describe the pest distribution. Nine significant models were obtained in the 14 evaluated periods. The range estimated for the correlation was from 908 to 6884 m. Three hot spots of high population level and some areas with comparatively low populations were constant over the 3-year period, while there is a greater variation in the size of the population in different generations and years in other areas.
NASA Astrophysics Data System (ADS)
Jahjah, Munzer; Ulivieri, Carlo
2004-02-01
Understanding the dynamics of land cover change has increasingly been recognized as one of the key research imperatives in global environmental change research. Scientists have developed and applied various methods in order to find and propose solutions for many environmental world problems. From 1986-1995 changes in Kenya coastal zone landcover, derived from the post-classification TM images, were significant with arid areas growing from 3% to 10%, woody areas decreased from 4% to 2%, herbaceous areas decreased from 25% to 20%, developed land increased from 2% to 3%. In order to generate the change probability map as a continuous surface using geostatistical method-ArcGIS, we used as an input the Generalized Linear Model (GLM) probability result. The results reveal the efficiency of the Probability-of-Change map (POC), especially if reference data are lacking, in indicating the possibility of having a change and its type in a determined area, taking advantage of the layer transparency of the GIS systems. Thus, the derived information supplies a good tool for the interpretation of the magnitude of the land cover changes and guides the final user directly to the areas of changes to understand and derive the possible interactions of human or natural processes.
Meerschman, Eef; Cockx, Liesbet; Islam, Mohammad Monirul; Meeuws, Fun; Van Meirvenne, Marc
2011-06-01
Previous research showed a regional Cu enrichment of 6 mg kg(-1) in the top soil of the Ypres war zone (Belgium), caused by corrosion of WWI shell fragments. Further research was required since in addition to Cu, also As, Pb, and Zn were used during the manufacturing of ammunition. Therefore, an additional data collection was conducted in which the initial Cu data set was tripled to 731 data points and extended to eight heavy metals (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) which permitted (1) to evaluate the environmental impact of the heavy metals at a regional scale and (2) to assess their regional spatial occurrence by performing an optimized geostatistical modeling. The results showed no pollution at a regional scale, but sometimes locally concentrations exceeded the soil sanitation threshold, especially for Cu, Pb, and Zn. The spatial patterns of Ni and Cr were related to variations in soil texture whereas the occurrences of Cu and Pb were clearly linked to WWI activities. This difference in spatial behavior was confirmed by an analysis of coregionalization.
GVIZ BETA VERSION. A 3D Geostatistical Mapping Tool
Weiss, W.W.; Stevenson, C.; Patel, K.; Wang, J.
1997-03-25
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.
Timescape: a simple space-time interpolation geostatistical Algorithm
NASA Astrophysics Data System (ADS)
Ciolfi, Marco; Chiocchini, Francesca; Gravichkova, Olga; Pisanelli, Andrea; Portarena, Silvia; Scartazza, Andrea; Brugnoli, Enrico; Lauteri, Marco
2016-04-01
Environmental sciences include both time and space variability in their datasets. Some established tools exist for both spatial interpolation and time series analysis alone, but mixing space and time variability calls for compromise: Researchers are often forced to choose which is the main source of variation, neglecting the other. We propose a simple algorithm, which can be used in many fields of Earth and environmental sciences when both time and space variability must be considered on equal grounds. The algorithm has already been implemented in Java language and the software is currently available at https://sourceforge.net/projects/timescapeglobal/ (it is published under GNU-GPL v3.0 Free Software License). The published version of the software, Timescape Global, is focused on continent- to Earth-wide spatial domains, using global longitude-latitude coordinates for samples localization. The companion Timescape Local software is currently under development ad will be published with an open license as well; it will use projected coordinates for a local to regional space scale. The basic idea of the Timescape Algorithm consists in converting time into a sort of third spatial dimension, with the addition of some causal constraints, which drive the interpolation including or excluding observations according to some user-defined rules. The algorithm is applicable, as a matter of principle, to anything that can be represented with a continuous variable (a scalar field, technically speaking). The input dataset should contain position, time and observed value of all samples. Ancillary data can be included in the interpolation as well. After the time-space conversion, Timescape follows basically the old-fashioned IDW (Inverse Distance Weighted) interpolation Algorithm, although users have a wide choice of customization options that, at least partially, overcome some of the known issues of IDW. The three-dimensional model produced by the Timescape Algorithm can be
Preliminary Evaluation of the Community Multiscale Air Quality (CMAQ) Model Version 5.1
The AMAD will perform two annual CMAQ model simulations, one with the current publically available version of the CMAQ model (v5.0.2) and the other with the beta version of the new model (v5.1). The results of each model simulation will then be compared to observations and the pe...
Staines, Graham L; Blankertz, Laura; Magura, Stephen; Bali, Priti; Madison, Elizabeth M; Spinelli, Michael; Horowitz, Emily; Guarino, Honoria; Grandy, Audrey; Fong, Chunki; Gomez, Augustin; Dimun, Amy; Friedman, Ellen
2004-01-01
This article presents initial efficacy data for an innovative vocational rehabilitation model designed for methadone-maintained patients--the Customized Employment Supports (CES) model. In this model, a CES counselor works intensively with a small caseload of patients in order to overcome the vocational as well as nonvocational barriers that hinder their employment, with the goal of attaining rapid placement in competitive employment. The CES model was implemented at two Manhattan methadone treatment programs as part of a randomized clinical trial comparing the model's employment outcomes with those of standard vocational counseling. The study tested the hypothesis that patients in the experimental group will have better employment outcomes than those in the comparison group. The data were collected from May 2001 through September 2003. The sample consisted of the first 121 patients who had completed their 6-month follow-up interviews. The preliminary results supported the hypothesis for two indices of paid employment, i.e., the CES group was more likely to obtain both competitive employment and informal paid employment. The clinical trial is continuing.
Optimization-based multiple-point geostatistics: A sparse way
NASA Astrophysics Data System (ADS)
Kalantari, Sadegh; Abdollahifard, Mohammad Javad
2016-10-01
In multiple-point simulation the image should be synthesized consistent with the given training image and hard conditioning data. Existing sequential simulation methods usually lead to error accumulation which is hardly manageable in future steps. Optimization-based methods are capable of handling inconsistencies by iteratively refining the simulation grid. In this paper, the multiple-point stochastic simulation problem is formulated in an optimization-based framework using a sparse model. Sparse model allows each patch to be constructed as a superposition of a few atoms of a dictionary formed using training patterns, leading to a significant increase in the variability of the patches. To control the creativity of the model, a local histogram matching method is proposed. Furthermore, effective solutions are proposed for different issues arisen in multiple-point simulation. In order to handle hard conditioning data a weighted matching pursuit method is developed in this paper. Moreover, a simple and efficient thresholding method is developed which allows working with categorical variables. The experiments show that the proposed method produces acceptable realizations in terms of pattern reproduction, increases the variability of the realizations, and properly handles numerous conditioning data.
Tillman, Fred D; Garner, Bradley D.; Truini, Margot
2013-01-01
Preliminary numerical models were developed to simulate groundwater flow in the basin-fill alluvium in Detrital, Hualapai, and Sacramento Valleys in northwestern Arizona. The purpose of this exercise was to gather and evaluate available information and data, to test natural‑recharge concepts, and to indicate directions for improving future regional groundwater models of the study area. Both steady-state and transient models were developed with a single layer incorporating vertically averaged hydraulic properties over the model layer. Boundary conditions for the models were constant-head cells along the northern and western edges of the study area, corresponding to the location of the Colorado River, and no-flow boundaries along the bedrock ridges that bound the rest of the study area, except for specified flow where Truxton Wash enters the southern end of Hualapai Valley. Steady-state conditions were simulated for the pre-1935 period, before the construction of Hoover Dam in the northwestern part of the model area. Two recharge scenarios were investigated using the steady-state model—one in which natural aquifer recharge occurs directly in places where water is available from precipitation, and another in which natural aquifer recharge from precipitation occurs in the basin-fill alluvium that drains areas of available water. A transient model with 31 stress periods was constructed to simulate groundwater flow for the period 1935–2010. The transient model incorporates changing Colorado River, Lake Mead, and Lake Mohave water levels and includes time-varying groundwater withdrawals and aquifer recharge. Both the steady-state and transient models were calibrated to available water-level observations in basin-fill alluvium, and simulations approximate observed water-level trends throughout most of the study area.
NASA Astrophysics Data System (ADS)
Aoki, K.; Mito, Y.; Yamamoto, T.; Shirasagi, S.
2007-12-01
The evaluation of the rock mass mechanical properties by the seismic reflection method and TBM driving is proposed for TBM tunnelling. The relationship between the reflection number derived from the three-dimensional seismic reflection method and the rock strength index ( RSI) derived from TBM driving data is examined, and the methodology of conversion from the reflection number to the RSI is proposed. Furthermore a geostatistical prediction methodology to provide a three-dimensional geotechnical profile ahead of the tunnel face is proposed. The performance of this prediction method is verified by actual field data.
New advances in methodology for statistical tests useful in geostatistical studies
Borgman, L.E.
1988-05-01
Methodology for statistical procedures to perform tests of hypothesis pertaining to various aspects of geostatistical investigations has been slow in developing. The correlated nature of the data precludes most classical tests and makes the design of new tests difficult. Recent studies have led to modifications of the classical t test which allow for the intercorrelation. In addition, results for certain nonparametric tests have been obtained. The conclusions of these studies provide a variety of new tools for the geostatistician in deciding questions on significant differences and magnitudes.
NASA Astrophysics Data System (ADS)
Choi, E.; Kelbert, A.; Peckham, S. D.
2014-12-01
We demonstrate that code coupling can be an efficient and flexible method for modeling complicated two-way interactions between tectonic and surface processes with SNAC-CHILD coupling as an example. SNAC is a deep earth process model (a geodynamic/tectonics model), built upon a scientific software framework called StGermain and also compatible with a model coupling framework called Pyre. CHILD is a popular surface process model (a landscape evolution model), interfaced to the CSDMS (Community Surface Dynamics Modeling System) modeling framework. We first present proof-of-concept but non-trivial results from a simplistic coupling scheme. We then report progress towards augmenting SNAC with a Basic Model Interface (BMI), a framework-agnostic standard interface developed by CSDMS that uses the CSDMS Standard Names as controlled vocabulary for model communication across domains. Newly interfaced to BMI, SNAC will be easily coupled with CHILD as well as other BMI-compatible models. In broader context, this work will test BMI as a general and easy-to-implement mechanism for sharing models between modeling frameworks and is a part of the NSF-funded EarthCube Building Blocks project, "Earth System Bridge: Spanning Scientific Communities with Interoperable Modeling Frameworks."
Characterizing regional soil mineral composition using spectroscopyand geostatistics
Mulder, V.L.; de Bruin, S.; Weyermann, J.; Kokaly, Raymond F.; Schaepman, M.E.
2013-01-01
This work aims at improving the mapping of major mineral variability at regional scale using scale-dependent spatial variability observed in remote sensing data. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and statistical methods were combined with laboratory-based mineral characterization of field samples to create maps of the distributions of clay, mica and carbonate minerals and their abundances. The Material Identification and Characterization Algorithm (MICA) was used to identify the spectrally-dominant minerals in field samples; these results were combined with ASTER data using multinomial logistic regression to map mineral distributions. X-ray diffraction (XRD)was used to quantify mineral composition in field samples. XRD results were combined with ASTER data using multiple linear regression to map mineral abundances. We testedwhether smoothing of the ASTER data to match the scale of variability of the target sample would improve model correlations. Smoothing was donewith Fixed Rank Kriging (FRK) to represent the mediumand long-range spatial variability in the ASTER data. Stronger correlations resulted using the smoothed data compared to results obtained with the original data. Highest model accuracies came from using both medium and long-range scaled ASTER data as input to the statistical models. High correlation coefficients were obtained for the abundances of calcite and mica (R2 = 0.71 and 0.70, respectively). Moderately-high correlation coefficients were found for smectite and kaolinite (R2 = 0.57 and 0.45, respectively). Maps of mineral distributions, obtained by relating ASTER data to MICA analysis of field samples, were found to characterize major soil mineral variability (overall accuracies for mica, smectite and kaolinite were 76%, 89% and 86% respectively). The results of this study suggest that the distributions of minerals and their abundances derived using FRK-smoothed ASTER data more closely match the spatial
Percutaneous Irreversible Electroporation Lung Ablation: Preliminary Results in a Porcine Model
Deodhar, Ajita; Monette, Sebastien; Single, Gordon W.; Hamilton, William C.; Thornton, Raymond H.; Sofocleous, Constantinos T.; Maybody, Majid; Solomon, Stephen B.
2011-12-15
Objective: Irreversible electroporation (IRE) uses direct electrical pulses to create permanent 'pores' in cell membranes to cause cell death. In contrast to conventional modalities, IRE has a nonthermal mechanism of action. Our objective was to study the histopathological and imaging features of IRE in normal swine lung. Materials and Methods: Eleven female swine were studied for hyperacute (8 h), acute (24 h), subacute (96 h), and chronic (3 week) effects of IRE ablation in lung. Paired unipolar IRE applicators were placed under computed tomography (CT) guidance. Some applicators were deliberately positioned near bronchovascular structures. IRE pulse delivery was synchronized with the cardiac rhythm only when ablation was performed within 2 cm of the heart. Contrast-enhanced CT scan was performed immediately before and after IRE and at 1 and 3 weeks after IRE ablation. Representative tissue was stained with hematoxylin and eosin for histopathology. Results: Twenty-five ablations were created: ten hyperacute, four acute, and three subacute ablations showed alveolar edema and necrosis with necrosis of bronchial, bronchiolar, and vascular epithelium. Bronchovascular architecture was maintained. Chronic ablations showed bronchiolitis obliterans and alveolar interstitial fibrosis. Immediate post-procedure CT images showed linear or patchy density along the applicator tract. At 1 week, there was consolidation that resolved partially or completely by 3 weeks. Pneumothorax requiring chest tube developed in two animals; no significant cardiac arrhythmias were noted. Conclusion: Our preliminary porcine study demonstrates the nonthermal and extracellular matrix sparing mechanism of action of IRE. IRE is a potential alternative to thermal ablative modalities.
NASA Astrophysics Data System (ADS)
Skalbeck, J. D.; Couch, J. N.; Roy, D. M.
2003-12-01
Increased concerns recently about the quantity and quality of groundwater resources in Wisconsin have brought about the need for better understanding of the subsurface geologic lithology and structure that controls groundwater flow. Concern centers on excessive drawdown in the deep sandstone aquifer system throughout southeastern Wisconsin and its relation to increasing levels of total dissolved solids and radium in some municipal supply wells. It is possible that preferential flow paths influence the circulation of groundwater to these wells and their vulnerability to contamination. The largest feature that could channel vertical and horizontal flow at the regional scale is the northeast trending Waukesha fault zone that intersects the main pumping center at Waukesha. The normal vertical displacement along the Waukesha Fault produces strong gravity and aeromagnetic anomalies coincident with the northeast trend of the fault. Previous gravity modeling studies associated with the fault are generally unconstrained yielding results with wide variation in fault geometry and vertical fault offset. Reasonable model fits were obtained with fault dip ranging from 10o to 85o with vertical offset ranging from 500 to 700 m. This study provides well-constrained results from coupled modeling of gravity and aeromagnetic (potential fields) data and incorporation of lithologic depths from the lithology database used to construct a regional numerical model for southeastern Wisconsin. We also utilized depth information from the only well (USGS Test Well, Zion, IL) that penetrates the entire Cambrian-Ordovician aquifer system to reach the Precambrian basement on the down-thrown (southeast) block of the fault. Preliminary modeling of gravity and aeromagnetic data along two profiles suggests vertical offset of Precambrian basement from 700 to 800 m along a normal fault with a 60o southeast dip. Based on thickness of the Cambrian Mount Simon sandstone, preliminary results also suggest
NASA Astrophysics Data System (ADS)
Elliott, A. J.; Oskin, M. E.; Duan, B.; Liu, J.; Oglesby, D. D.
2009-12-01
Faults are theoretically expected to localize over time onto single, throughgoing strands. Although geologic observations commonly support this theory, examples abound in which parallel active fault strands coexist. One such location is the ~200 km long Aksay restraining double-bend of the Altyn Tagh fault (ATF), within which two fault strands with right bends run subparallel and form a 10 km left stepover in the trace of the active fault. We combine geomorphic analysis, paleoseismologic investigation, and numerical rupture modeling to assess how dynamic interactions of faults may counteract localization. Based on preliminary investigation and prior work we propose three possible explanations for the persistence of multiple active parallel strands: the loading or unloading of each strand by residual stresses from ruptures that terminate in the bend, strain partitioning, or temporal tradeoff or evolution in strand activity. Our preliminary numerical models address the first hypothesis, simulating dynamics of the fault system over multiple earthquake cycles. Model results show that individual ruptures are generally unable to pass through the bend, but through residual stress loading on one strand and unclamping by earthquakes on the other strand, ruptures may occasionally propagate through the bend. This mutual promotion could explain the persistence of activity on both strands through time. Our initial field observations lend support to both the first and second hypotheses, and do not as yet rule out the third. We reinvestigated slip rate sites on both sides of the bend, where previous studies have determined that a dominant strand accommodates nearly the full ATF slip rate (~1 cm/yr, as reported previously by Xu et al., 2005 and Wang et al., 2005, but inconsistent with higher rates found by Meriaux et al., 2005). In addition, we found that a low level of activity persists on the adjacent subordinate strands. Near Old Aksay, the subordinate SATF displays geomorphic
Preliminary regime diagram on a sphere with a simplified general circulation model
NASA Technical Reports Server (NTRS)
Pitcher, E. J.; Geisler, J. E.; Malone, R. C.
1981-01-01
Numerical model studies useful design considerations and which can be accumulated to form the body of basic knowledge necessary for application of the atmospheric general circulation experiment (AGCE) data to understanding of atmospheric problems are reported. The most efficient way to obtain a computer model suitable for this objective is to modify an existing general circulation model (GCM) of the atmosphere rather than to develop such a model from first principles. The GCM and its modification is outlined.
Preliminary simulation of a M6.5 earthquake on the Seattle Fault using 3D finite-difference modeling
Stephenson, William J.; Frankel, Arthur D.
2000-01-01
A three-dimensional finite-difference simulation of a moderate-sized (M 6.5) thrust-faulting earthquake on the Seattle fault demonstrates the effects of the Seattle Basin on strong ground motion in the Puget lowland. The model area includes the cities of Seattle, Bremerton and Bellevue. We use a recently developed detailed 3D-velocity model of the Seattle Basin in these simulations. The model extended to 20-km depth and assumed rupture on a finite fault with random slip distribution. Preliminary results from simulations of frequencies 0.5 Hz and lower suggest amplification can occur at the surface of the Seattle Basin by the trapping of energy in the Quaternary sediments. Surface waves generated within the basin appear to contribute to amplification throughout the modeled region. Several factors apparently contribute to large ground motions in downtown Seattle: (1) radiation pattern and directivity from the rupture; (2) amplification and energy trapping within the Quaternary sediments; and (3) basin geometry and variation in depth of both Quaternary and Tertiary sediments
A Probabilistic Model for the Distribution of Authorships: A Preliminary Report.
ERIC Educational Resources Information Center
Ajiferuke, Isola
The purpose of this study was to develop a model for the distribution of authorships--based on the initial hypothesis that the distribution of authorships follows a shifted Waring distribution--and to test the derived model and some other discrete probability models for goodness-of-fit against empirical data. Bibliographic data from 15 abstracting…
NASA Astrophysics Data System (ADS)
Brozzo, Gianpiero; Doveri, Marco; Lelli, Matteo; Scozzari, Andrea
2010-05-01
Computer-based decision support systems are getting a growing interest for water managing authorities and water distribution companies. This work discusses a preliminary experience in the application of computational intelligence in a hydrological modeling framework, regarding the study area of the alluvial aquifer of the Magra River (Italy). Two sites in the studied area, corresponding to two distinct groups of wells (Battifollo and Fornola) are managed by the local drinkable water distribution company (ACAM Acque), which serves the area of La Spezia, on the Ligurian coast. Battifollo has 9 wells with a total extraction rate of about 240 liters per second, while Fornola has 44 wells with an extraction rate of about 900 liters per second. Objective of this work is to make use of time series coming from long-term monitoring activities in order to assess the trend of the groundwater level with respect to a set of environmental and exploitation parameters; this is accomplished by the experimentation of a suitable model, eligible to be used as a predictor. This activity moves on from the modeling of the system behavior, based on a set of Input/Output data, in order to characterize it without necessarily a prior knowledge of any deterministic mechanism (system identification). In this context, data series collected by continuous hydrological monitoring instrumentation installed in the studied sites, together with meteorological and water extraction data, have been analyzed in order to assess the applicability and performance of a predictive model of the groundwater level. A mixed approach (both data driven and process-based) has been experimented on the whole dataset relating to the last ten years of continuous monitoring activity. The system identification approach presented here is based on the integration of an adaptive technique based on Artificial Neural Networks (ANNs) and a blind deterministic identification approach. According to this concept, the behavior of
Geo-Statistical Approach to Estimating Asteroid Exploration Parameters
NASA Technical Reports Server (NTRS)
Lincoln, William; Smith, Jeffrey H.; Weisbin, Charles
2011-01-01
NASA's vision for space exploration calls for a human visit to a near earth asteroid (NEA). Potential human operations at an asteroid include exploring a number of sites and analyzing and collecting multiple surface samples at each site. In this paper two approaches to formulation and scheduling of human exploration activities are compared given uncertain information regarding the asteroid prior to visit. In the first approach a probability model was applied to determine best estimates of mission duration and exploration activities consistent with exploration goals and existing prior data about the expected aggregate terrain information. These estimates were compared to a second approach or baseline plan where activities were constrained to fit within an assumed mission duration. The results compare the number of sites visited, number of samples analyzed per site, and the probability of achieving mission goals related to surface characterization for both cases.
NASA Astrophysics Data System (ADS)
Asay-Davis, Xylar; Martin, Daniel
2016-04-01
The second Ice Shelf-Ocean MIP (ISOMIP+) and the first Marine Ice Sheet-Ocean MIP (MISOMIP1) prescribe a set of idealized experiments for ocean models with ice-shelf cavities and coupled ice sheet-ocean models, respectively. ISOMIP+ and MISOMIP1 were designed together with the third Marine Ice Sheet MIP (MISMIP+) with three main goals, namely that the MIPs should provide: a controlled forum for researchers to compare their model results with those from other models during model development. a path for testing components in the process of developing coupled ice sheet-ocean models. a basic setup from which a large variety of parameter and process studies can usefully be performed. The experimental design for the three MIPs is currently under review in Geoscientific Model Development (Asay-Davis et al. 2015, doi:10.5194/gmdd-8-9859-2015). We present preliminary results from ISOMIP+ and MISOMIP1 experiments using several ocean-only and coupled ice sheet-ocean models. Among ocean models, we show that differences in model behavior are significant enough that similar results can only be achieved by tuning model parameters (e.g. boundary-layer transfer coefficients, drag coefficients, vertical mixing parameterizations) for each models. This tuning is constrained by a desired mean melt rate in quasi-steady state under specified forcing conditions, akin to how models would be tuned based on observations for non-idealized simulations. We also present a number of parameter studies based the MIP experiments. Again, using several models, we show that melt rates respond sub-linearly to both changes in the square root of the drag coefficient and the heat-transfer coefficient, and that melting is relatively insensitive to horizontal-mixing coefficients (perhaps because the resolution is sufficient to permit eddies) but more sensitive to vertical-mixing coefficients. We show that the choice of the equation of state (linear or nonlinear) does not have a significant impact as long as
Ward, D.B.; Bryan, C.R.; Siegel, M.D.
1994-12-31
An experiment involving migration of fluid and tracers (Li, Br, Ni) through a 6-m-high x 3-m-dia caisson Wedron 510 sand, is being carried out for Yucca Mountain Site Characterization Project. Sand`s surface chemistry of the sand was studied and a preliminary surface-complexation model of Ni adsorption formulated for transport calculations. XPS and leaching suggest that surface of the quartz sand is partially covered by thin layers of Fe-oxyhydroxide and Ca-Mg carbonate and by flakes of kaolinite. Ni adsorption by the sand is strongly pH-dependent, showing no adsorption at pH 5 and near-total adsorption at pH 7. Location of adsorption edge is independent of ionic strength and dissolved Ni concentration; it is shifted to slightly lower pH with higher pCO2 and to slightly higher pH by competition with Li. Diminished adsorption at alkiline pH with higher pCO2 implies formation of dissolved Ni-carbonato complexes. Ni adsorption edges for goethite and quartz, two components of the sand were also measured. Ni adsorption on pure quartz is only moderately pH-dependent and differs in shape and location from that of the sand, whereas Ni adsorption by goethite is strongly pH-dependent. A triple-layer surface-complexation model developed for goethite provides a good fit to the Ni-adsorption curve of the sand. Based on this model, the apparent surface area of the Fe-oxyhydroxide coating is estimated to be 560 m{sup 2}/g, compatible with its occurrence as amorphous Fe-oxyhydroxide. Potentiometric titrations on sand also differ from pure quartz and suggest that effective surface area of sand may be much greater than that measured by N{sub 2}-BET gas adsorption. Attempts to model the adsorption of bulk sand in terms of properties of pure end member components suggest that much of the sand surface is inert. Although the exact Ni adsorption mechanisms remain ambiguous, this preliminary adsorption model provides an initial set of parameters that can be used in transport calculations.
Molton, Ivan R.; Jensen, Mark P.; Nielson, Warren; Cardenas, Diana; Ehde, Dawn M.
2008-01-01
Chronic pain commonly accompanies long-term disabilities such as spinal cord injury (SCI). Research suggests that patient motivation to engage in adaptive pain coping strategies, such as exercise/stretching and task persistence, is an important factor in determining the impact that this pain will have on quality of life. One recently proposed model (the “Motivational Model of Pain Self-Management”) suggests that motivation to manage pain is influenced by two primary variables: beliefs about the importance of engaging in pain self-management (i.e., “perceived importance”) and beliefs about one's own ability to engage in these behaviors (i.e., “self-efficacy”). The purpose of this study was to provide a preliminary test of this model in a sample of 130 adults with SCI who completed a return by mail survey. Measures included a numerical rating scale of pain intensity and the revised version of the Multidimensional Pain Readiness to Change Questionnaire. Mediation analyses were performed using multiple regression. Results suggested that the effects of perceived importance and self-efficacy on exercise behavior were mediated by readiness to engage in exercise, consistent with the proposed model. However, the model could not be established for the outcome of task persistence. Perspective: This study tests a model describing motivation to engage in pain management behaviors (i.e., “readiness to change”) in adults with spinal cord injury. This model could potentially aid clinicians in their conceptualization of the factors that affect patient motivation to manage pain. PMID:18359668
Powroźnik, Beata; Słoczyńska, Karolina; Marciniec, Krzysztof; Zajdel, Paweł; Pękala, Elżbieta
2016-01-01
Purpose: Determination of the mutagenic potential of new biologically active compounds is of great concern for preliminary toxicity testing and drug development. Methods: The mutagenic and antimutagenic effects of some quinoline- and isoquinolinesulfonamide analogs of aripiprazole (1-8), which display potent antidepressant, anxiolytic, and antipsychotic properties, were evaluated using the Vibrio harveyi assay and OSIRIS Property Explorer software. Additionally, the Ames test was used as the reference. Results: In silico prediction showed that compounds 5 (N-(3-(4-(2,3- dichlorophenyl)piperazin-1-yl)propyl)quinoline-7-sulfonamide) and 6 (N-(4-(4-(2,3- Dichlorophenyl)piperazin-1-yl)butyl)quinoline-7-sulfonamide) trigger a mutagenic structural alert. However, this was not confirmed by in vitro assays, as none of the tested compounds displayed mutagenic activity against all tested strains of bacteria. Moreover, compounds 1-8 displayed a protective effect against the mutagenicity induced by a direct acting mutagen NQNO. The most beneficial antimutagenic properties showed compound 5 which exhibited strong antimutagenic properties in all tested V. harveyi strains. High antimutagenic potency of this compound was confirmed in the Ames TA100 assay system. Conclusion: Newly synthesized azinesulfonamide analogs of aripiprazole may be considered as genotoxically safe as they do not display mutagenic activity on the tester strains. Moreover, the tested compounds demonstrated significant antimutagenic properties that can be valuable for prevention of the NQNO genotoxicity. Additionally, it appears that the Vibrio harveyi assay can be applied for primary mutagenicity and antimutagenicity assessment of chemical substances, thus, representing a useful alternative tool for compounds safety evaluation. PMID:27766221
Preliminary Study of 2D Fracture Upscaling of Geothermal Rock Using IFS Fractal Model
NASA Astrophysics Data System (ADS)
Tobing, Prana F. L.; Feranie, Selly; Latief, Fourier D. E.
2016-08-01
Fractured rock plays important role in reservoir production. In larger scale, fractures are more likely to be heterogeneous and considered to be fractal in its nature. One of the characteristics of fractal structure is the scale independence. An investigation of fractal properties on natural fractured rock is therefore needed for modelling larger fracture. We have investigated the possibilities of fractal upscaling method to produce a larger geothermal fracture model based on smaller fracture data. We generate Iterated Function System (IFS) fractal model using parameters e.g. scale factor, angle between branch, initial line direction, and branch thickness. All the model parameters are obtained from smaller fracture data. We generate higher iteration model to be compared with larger geothermal fracture. The similarity between the IFS fractal model and natural fracture is measured by 2D box counting fractal dimension (D). The fractal dimension of first to fourth generation fractal model is (1.86 ± 0.02). The fractal dimension of the reference geothermal site is (1.86 ± 0.04). Besides of D, we found significant similarity of fracture parameters there are intensity and density between fracture model and natural fracture. Based on these result, we conclude that fractal upscaling using IFS fractal model is potential to model larger scale of 2D fracture.
Geostatistical independent simulation of spatially correlated soil variables
NASA Astrophysics Data System (ADS)
Boluwade, Alaba; Madramootoo, Chandra A.
2015-12-01
The selection of best management practices to reduce soil and water pollution often requires estimation of soil properties. It is important to find an efficient and robust technique to simulate spatially correlated soils parameters. Co-kriging and co-simulation are techniques that can be used. These methods are limited in terms of computer simulation due to the problem of solving large co-kriging systems and difficulties in fitting a valid model of coregionalization. The order of complexity increases as the number of covariables increases. This paper presents a technique for the conditional simulation of a non-Gaussian vector random field on point support scale. The technique is termed Independent Component Analysis (ICA). The basic principle underlining ICA is the determination of a linear representation of non-Gaussian data so that the components are considered statistically independent. With such representation, it would be easy and more computationally efficient to develop direct variograms for the components. The process is presented in two stages. The first stage involves the ICA decomposition. The second stage involves sequential Gaussian simulation of the generated components (which are derived from the first stage). This technique was applied for spatially correlated extractable cations such as magnesium (Mg) and iron (Fe) in a Canadian watershed. This paper has a strong application in stochastic quantification of uncertainties of soil attributes in soil remediation and soil rehabilitation.
Geostatistical Sampling Methods for Efficient Uncertainty Analysis in Flow and Transport Problems
NASA Astrophysics Data System (ADS)
Liodakis, Stylianos; Kyriakidis, Phaedon; Gaganis, Petros
2015-04-01
In hydrogeological applications involving flow and transport of in heterogeneous porous media the spatial distribution of hydraulic conductivity is often parameterized in terms of a lognormal random field based on a histogram and variogram model inferred from data and/or synthesized from relevant knowledge. Realizations of simulated conductivity fields are then generated using geostatistical simulation involving simple random (SR) sampling and are subsequently used as inputs to physically-based simulators of flow and transport in a Monte Carlo framework for evaluating the uncertainty in the spatial distribution of solute concentration due to the uncertainty in the spatial distribution of hydraulic con- ductivity [1]. Realistic uncertainty analysis, however, calls for a large number of simulated concentration fields; hence, can become expensive in terms of both time and computer re- sources. A more efficient alternative to SR sampling is Latin hypercube (LH) sampling, a special case of stratified random sampling, which yields a more representative distribution of simulated attribute values with fewer realizations [2]. Here, term representative implies realizations spanning efficiently the range of possible conductivity values corresponding to the lognormal random field. In this work we investigate the efficiency of alternative methods to classical LH sampling within the context of simulation of flow and transport in a heterogeneous porous medium. More precisely, we consider the stratified likelihood (SL) sampling method of [3], in which attribute realizations are generated using the polar simulation method by exploring the geometrical properties of the multivariate Gaussian distribution function. In addition, we propose a more efficient version of the above method, here termed minimum energy (ME) sampling, whereby a set of N representative conductivity realizations at M locations is constructed by: (i) generating a representative set of N points distributed on the
Bezak, Eva
2015-01-01
The major differences between the physics models in Geant4-DNA and RITRACKS Monte Carlo packages are investigated. Proton and electron ionisation interactions and electron excitation interactions in water are investigated in the current work. While these packages use similar semiempirical physics models for inelastic cross-sections, the implementation of these models is demonstrated to be significantly different. This is demonstrated in a simple Monte Carlo simulation designed to identify differences in interaction cross-sections. PMID:26124856
Lv, Ming-ming; Fan, Xin-dong; Su, Li-xin
2013-10-15
Objective: A chronic arteriovenous malformation (AVM) model using the swine retia mirabilia (RMB) was developed and compared with the human extracranial AVM (EAVM) both in hemodynamics and pathology, to see if this brain AVM model can be used as an EAVM model. Methods: We created an arteriovenous fistula between the common carotid artery and the external jugular vein in eight animals by using end-to-end anastomosis. All animals were sacrificed 1 month after surgery, and the bilateral retia were obtained at autopsy and performed hematoxylin and eosin staining and immunohistochemistry. Pre- and postsurgical hemodynamic evaluations also were conducted. Then, the blood flow and histological changes of the animal model were compared with human EAVM. Results: The angiography after operation showed that the blood flow, like human EAVM, flowed from the feeding artery, via the nidus, drained to the draining vein. Microscopic examination showed dilated lumina and disrupted internal elastic lamina in both RMB of model and nidus of human EAVM, but the thickness of vessel wall had significant difference. Immunohistochemical reactivity for smooth muscle actin, angiopoietin 1, and angiopoietin 2 were similar in chronic model nidus microvessels and human EAVM, whereas vascular endothelial growth factor was significant difference between human EAVM and RMB of model. Conclusions: The AVM model described here is similar to human EAVM in hemodynamics and immunohistochemical features, but there are still some differences in anatomy and pathogenetic mechanism. Further study is needed to evaluate the applicability and efficacy of this model.
Preliminary gravity inversion model of basins east of Yucca Flat, Nevada Test Site, Nevada.
Geoffrey A. Phelps; Carter W. Roberts, and Barry C. Moring
2006-03-17
The Yucca Flat eastern extension study area, a 14 kilometer by 45 kilometer region contiguous to Yucca Flat on the west and Frenchman Flat on the south, is being studied to expand the boundary of the Yucca Flat hydrogeologic model. The isostatic residual gravity anomaly was inverted to create a model of the depth of the geologic basins within the study area. Such basins typically are floored by dense pre-Tertiary basement rocks and filled with less-dense Tertiary volcanic and sedimentary rocks and Quaternary alluvium, a necessary condition for the use of gravity modeling to predict the depth to the pre-Tertiary basement rocks within the basins. Three models were created: a preferred model to represent the best estimate of depth to pre-Tertiary basement rocks in the study area, and two end-member models to demonstrate the possible range of solutions. The preferred model predicts shallow basins, generally less than 1,000m depth, throughout the study area, with only Emigrant Valley reaching a depth of 1,100m. Plutonium valley and West Fork Scarp Canyon have maximum depths of 800m and 1,000m, respectively. The end-member models indicate that the uncertainty in the preferred model is less than 200m for most of the study area.
Preliminary results from a four-working space, double-acting piston, Stirling engine controls model
NASA Technical Reports Server (NTRS)
Daniele, C. J.; Lorenzo, C. F.
1980-01-01
A four working space, double acting piston, Stirling engine simulation is being developed for controls studies. The development method is to construct two simulations, one for detailed fluid behavior, and a second model with simple fluid behaviour but containing the four working space aspects and engine inertias, validate these models separately, then upgrade the four working space model by incorporating the detailed fluid behaviour model for all four working spaces. The single working space (SWS) model contains the detailed fluid dynamics. It has seven control volumes in which continuity, energy, and pressure loss effects are simulated. Comparison of the SWS model with experimental data shows reasonable agreement in net power versus speed characteristics for various mean pressure levels in the working space. The four working space (FWS) model was built to observe the behaviour of the whole engine. The drive dynamics and vehicle inertia effects are simulated. To reduce calculation time, only three volumes are used in each working space and the gas temperature are fixed (no energy equation). Comparison of the FWS model predicted power with experimental data shows reasonable agreement. Since all four working spaces are simulated, the unique capabilities of the model are exercised to look at working fluid supply transients, short circuit transients, and piston ring leakage effects.
NASA Astrophysics Data System (ADS)
Peredo, Oscar; Ortiz, Julián M.; Herrero, José R.
2015-12-01
The Geostatistical Software Library (GSLIB) has been used in the geostatistical community for more than thirty years. It was designed as a bundle of sequential Fortran codes, and today it is still in use by many practitioners and researchers. Despite its widespread use, few attempts have been reported in order to bring this package to the multi-core era. Using all CPU resources, GSLIB algorithms can handle large datasets and grids, where tasks are compute- and memory-intensive applications. In this work, a methodology is presented to accelerate GSLIB applications using code optimization and hybrid parallel processing, specifically for compute-intensive applications. Minimal code modifications are added decreasing as much as possible the elapsed time of execution of the studied routines. If multi-core processing is available, the user can activate OpenMP directives to speed up the execution using all resources of the CPU. If multi-node processing is available, the execution is enhanced using MPI messages between the compute nodes.Four case studies are presented: experimental variogram calculation, kriging estimation, sequential gaussian and indicator simulation. For each application, three scenarios (small, large and extra large) are tested using a desktop environment with 4 CPU-cores and a multi-node server with 128 CPU-nodes. Elapsed times, speedup and efficiency results are shown.
A software tool for geostatistical analysis of thermal response test data: GA-TRT
NASA Astrophysics Data System (ADS)
Focaccia, Sara; Tinti, Francesco; Bruno, Roberto
2013-09-01
In this paper we present a new method (DCE - Drift and Conditional Estimation), coupling Infinite Line Source (ILS) theory with geostatistics, to interpret thermal response test (TRT) data and the relative implementing user-friendly software (GA-TRT). Many methods (analytical and numerical) currently exist to analyze TRT data. The innovation derives from the fact that we use a probabilistic approach, able to overcome, without excessively complicated calculations, many interpretation problems (choice of the guess value of ground volumetric heat capacity, identification of the fluctuations of recorded data, inability to provide a measure of the precision of the estimates obtained) that cannot be solved otherwise. The new procedure is based on a geostatistical drift analysis of temperature records which leads to a precise equivalent ground thermal conductivity (λg) estimation, confirmed by the calculation of its estimation variance. Afterwards, based on λg, a monovariate regression on the original data allows for the identification of the theoretical relationship between ground volumetric heat capacity (cg) and borehole thermal resistance (Rb). By assuming the monovariate Probability Distribution Function (PDF) for each variable, the joint conditional PDF to the cg-Rb relationship is found; finally, the conditional expectation allows for the identification of the correct and optimal couple of the cg-Rb estimated values.
Geostatistical analysis of soil moisture distribution in a part of Solani River catchment
NASA Astrophysics Data System (ADS)
Kumar, Kamal; Arora, M. K.; Hariprasad, K. S.
2016-03-01
The aim of this paper is to estimate soil moisture at spatial level by applying geostatistical techniques on the point observations of soil moisture in parts of Solani River catchment in Haridwar district of India. Undisturbed soil samples were collected at 69 locations with soil core sampler at a depth of 0-10 cm from the soil surface. Out of these, discrete soil moisture observations at 49 locations were used to generate a spatial soil moisture distribution map of the region. Two geostatistical techniques, namely, moving average and kriging, were adopted. Root mean square error (RMSE) between observed and estimated soil moisture at remaining 20 locations was determined to assess the accuracy of the estimated soil moisture. Both techniques resulted in low RMSE at small limiting distance, which increased with the increase in the limiting distance. The root mean square error varied from 7.42 to 9.77 in moving average method, while in case of kriging it varied from 7.33 to 9.99 indicating similar performance of the two techniques.
Redesigning rain gauges network in Johor using geostatistics and simulated annealing
Aziz, Mohd Khairul Bazli Mohd; Yusof, Fadhilah; Daud, Zalina Mohd; Yusop, Zulkifli; Kasno, Mohammad Afif
2015-02-03
Recently, many rainfall network design techniques have been developed, discussed and compared by many researchers. Present day hydrological studies require higher levels of accuracy from collected data. In numerous basins, the rain gauge stations are located without clear scientific understanding. In this study, an attempt is made to redesign rain gauge network for Johor, Malaysia in order to meet the required level of accuracy preset by rainfall data users. The existing network of 84 rain gauges in Johor is optimized and redesigned into a new locations by using rainfall, humidity, solar radiation, temperature and wind speed data collected during the monsoon season (November - February) of 1975 until 2008. This study used the combination of geostatistics method (variance-reduction method) and simulated annealing as the algorithm of optimization during the redesigned proses. The result shows that the new rain gauge location provides minimum value of estimated variance. This shows that the combination of geostatistics method (variance-reduction method) and simulated annealing is successful in the development of the new optimum rain gauge system.
NASA Astrophysics Data System (ADS)
de Carvalho Alves, Marcelo; de Carvalho, Luiz Gonsaga; Vianello, Rubens Leite; Sediyama, Gilberto C.; de Oliveira, Marcelo Silva; de Sá Junior, Arionaldo
2013-07-01
The objective of the present study was to use the simple cokriging methodology to characterize the spatial variability of Penman-Monteith reference evapotranspiration and Thornthwaite potential evapotranspiration methods based on Moderate Resolution Imaging Spetroradiometer (MODIS) global evapotranspiration products and high-resolution surfaces of WordClim temperature and precipitation data. The climatic element data referred to 39 National Institute of Meteorology climatic stations located in Minas Gerais state, Brazil and surrounding states. The use of geostatistics and simple cokriging technique enabled the characterization of the spatial variability of the evapotranspiration providing uncertainty information on the spatial prediction pattern. Evapotranspiration and precipitation surfaces were implemented for the climatic classification in Minas Gerais. Multivariate geostatistical determined improvements of evapotranspiration spatial information. The regions in the south of Minas Gerais derived from the moisture index estimated with the MODIS evapotranspiration (2000-2010), presented divergence of humid conditions when compared to the moisture index derived from the simple kriged and cokriged evapotranspiration (1961-1990), indicating climate change in this region. There was stronger pattern of crossed covariance between evapotranspiration and precipitation rather than temperature, indicating that trends in precipitation could be one of the main external drivers of the evapotranspiration in Minas Gerais state, Brazil.
Optimal design of hydraulic head monitoring networks using space-time geostatistics
NASA Astrophysics Data System (ADS)
Herrera, G. S.; Júnez-Ferreira, H. E.
2013-05-01
This paper presents a new methodology for the optimal design of space-time hydraulic head monitoring networks and its application to the Valle de Querétaro aquifer in Mexico. The selection of the space-time monitoring points is done using a static Kalman filter combined with a sequential optimization method. The Kalman filter requires as input a space-time covariance matrix, which is derived from a geostatistical analysis. A sequential optimization method that selects the space-time point that minimizes a function of the variance, in each step, is used. We demonstrate the methodology applying it to the redesign of the hydraulic head monitoring network of the Valle de Querétaro aquifer with the objective of selecting from a set of monitoring positions and times, those that minimize the spatiotemporal redundancy. The database for the geostatistical space-time analysis corresponds to information of 273 wells located within the aquifer for the period 1970-2007. A total of 1,435 hydraulic head data were used to construct the experimental space-time variogram. The results show that from the existing monitoring program that consists of 418 space-time monitoring points, only 178 are not redundant. The implied reduction of monitoring costs was possible because the proposed method is successful in propagating information in space and time.
Redesigning rain gauges network in Johor using geostatistics and simulated annealing
NASA Astrophysics Data System (ADS)
Aziz, Mohd Khairul Bazli Mohd; Yusof, Fadhilah; Daud, Zalina Mohd; Yusop, Zulkifli; Kasno, Mohammad Afif
2015-02-01
Recently, many rainfall network design techniques have been developed, discussed and compared by many researchers. Present day hydrological studies require higher levels of accuracy from collected data. In numerous basins, the rain gauge stations are located without clear scientific understanding. In this study, an attempt is made to redesign rain gauge network for Johor, Malaysia in order to meet the required level of accuracy preset by rainfall data users. The existing network of 84 rain gauges in Johor is optimized and redesigned into a new locations by using rainfall, humidity, solar radiation, temperature and wind speed data collected during the monsoon season (November - February) of 1975 until 2008. This study used the combination of geostatistics method (variance-reduction method) and simulated annealing as the algorithm of optimization during the redesigned proses. The result shows that the new rain gauge location provides minimum value of estimated variance. This shows that the combination of geostatistics method (variance-reduction method) and simulated annealing is successful in the development of the new optimum rain gauge system.
NASA Astrophysics Data System (ADS)
Pesquer, Lluís; Cortés, Ana; Serral, Ivette; Pons, Xavier
2011-11-01
The main goal of this study is to characterize the effects of lossy image compression procedures on the spatial patterns of remotely sensed images, as well as to test the performance of job distribution tools specifically designed for obtaining geostatistical parameters (variogram) in a High Performance Computing (HPC) environment. To this purpose, radiometrically and geometrically corrected Landsat-5 TM images from April, July, August and September 2006 were compressed using two different methods: Band-Independent Fixed-Rate (BIFR) and three-dimensional Discrete Wavelet Transform (3d-DWT) applied to the JPEG 2000 standard. For both methods, a wide range of compression ratios (2.5:1, 5:1, 10:1, 50:1, 100:1, 200:1 and 400:1, from soft to hard compression) were compared. Variogram analyses conclude that all compression ratios maintain the variogram shapes and that the higher ratios (more than 100:1) reduce variance in the sill parameter of about 5%. Moreover, the parallel solution in a distributed environment demonstrates that HPC offers a suitable scientific test bed for time demanding execution processes, as in geostatistical analyses of remote sensing images.
Geostatistics: a common link between medical geography, mathematical geology, and medical geology
Goovaerts, P.
2015-01-01
Synopsis Since its development in the mining industry, geostatistics has emerged as the primary tool for spatial data analysis in various fields, ranging from earth and atmospheric sciences to agriculture, soil science, remote sensing, and more recently environmental exposure assessment. In the last few years, these tools have been tailored to the field of medical geography or spatial epidemiology, which is concerned with the study of spatial patterns of disease incidence and mortality and the identification of potential ‘causes’ of disease, such as environmental exposure, diet and unhealthy behaviours, economic or socio-demographic factors. On the other hand, medical geology is an emerging interdisciplinary scientific field studying the relationship between natural geological factors and their effects on human and animal health. This paper provides an introduction to the field of medical geology with an overview of geostatistical methods available for the analysis of geological and health data. Key concepts are illustrated using the mapping of groundwater arsenic concentration across eleven Michigan counties and the exploration of its relationship to the incidence of prostate cancer at the township level. PMID:25722963
Preliminary results of Physiological plant growth modelling for human life support in space
NASA Astrophysics Data System (ADS)
Sasidharan L, Swathy; Dussap, Claude-Gilles; Hezard, Pauline
2012-07-01
Human life support is fundamental and crucial in any kind of space explorations. MELiSSA project of European Space Agency aims at developing a closed, artificial ecological life support system involving human, plants and micro organisms. Consuming carbon dioxide and water from the life support system, plants grow in one of the chambers and convert it into food and oxygen along with potable water. The environmental conditions, nutrient availability and its consumption of plants should be studied and necessarily modeled to predict the amount of food, oxygen and water with respect to the environmental changes and limitations. The reliability of a completely closed system mainly depends on the control laws and strategies used. An efficient control can occur, only if the system to control is itself well known, described and ideally if the responses of the system to environmental changes are predictable. In this aspect, the general structure of plant growth model has been designed together with physiological modelling.The physiological model consists of metabolic models of leaves, stem and roots, of which concern specific metabolisms of the associated plant parts. On the basis of the carbon source transport (eg. sucrose) through stem, the metabolic models (leaf and root) can be interconnected to each other and finally coupled to obtain the entire plant model. For the first step, leaf metabolic model network was built using stoichiometric, mass and energy balanced metabolic equations under steady state approach considering all necessary plant pathways for growth and maintenance of leaves. As the experimental data for lettuce plants grown in closed and controlled environmental chambers were available, the leaf metabolic model has been established for lettuce leaves. The constructed metabolic network is analyzed using known stoichiometric metabolic technique called metabolic flux analysis (MFA). Though, the leaf metabolic model alone is not sufficient to achieve the
Modelling the Cooling of Coffee: Insights from a Preliminary Study in Indonesia
ERIC Educational Resources Information Center
Widjaja, Wanty
2010-01-01
This paper discusses an attempt to examine pre-service teachers' mathematical modelling skills. A modelling project investigating relationships between temperature and time in the process of cooling of coffee was chosen. The analysis was based on group written reports of the cooling of coffee project and observation of classroom discussion.…
Preliminary eddy current modelling for the large angle magnetic suspension test fixture
NASA Technical Reports Server (NTRS)
Britcher, Colin
1994-01-01
This report presents some recent developments in the mathematical modeling of the Large Angle Magnetic Suspension Test Fixture (LAMSTF) at NASA Langley Research Center. It is shown that these effects are significant, but may be amenable to analysis, modeling and measurement. A theoretical framework is presented, together with a comparison of computed and experimental data.
Chandrasena, G I; Deletic, A; McCarthy, D T
2013-01-01
Stormwater biofilters are not currently optimised for pathogen removal since the behaviour of these pollutants within the stormwater biofilters is poorly understood. Modelling is a common way of optimising these systems, which also provides a better understanding of the major processes that govern the pathogen removal. This paper provides an overview of a laboratory-scale study that investigated how different design and operational conditions impact pathogen removal in the stormwater biofilters. These data were then used to develop a modelling tool that can be used to optimise the design and operation of the stormwater biofilters. The model uses continuous simulations where adsorption and desorption were dominant during wet weather periods and first order die-off kinetics were significant in dry periods between the wet weather events. Relatively high Nash Sutcliffe Efficiencies (>0.5) indicate that the calibrated model is in good agreement with observed data and the optimised model parameters were comparable with values reported in the literature. The model's sensitivity is highest towards the adsorption process parameter followed by the die-off and desorption rate parameters, which implies that adsorption is the governing process of the model. Vegetation is found to have an impact on the wet weather processes since the adsorption and desorption parameters vary significantly with the different plant configurations. The model is yet to be tested against field data and needs to be improved to represent the effect of some other biofilter design configurations, such as the inclusion of the submerged zone.
ERIC Educational Resources Information Center
Fü rst, Guillaume; Ghisletta, Paolo; Lubart, Todd
2016-01-01
The present work proposes an integrative model of creativity that includes personality traits and cognitive processes. This model hypothesizes that three high-order personality factors predict two main process factors, which in turn predict intensity and achievement of creative activities. The personality factors are: "Plasticity" (high…
Preliminary Modeling, Testing, and Analysis of a Gas Tankless Water Heater: Preprint
Burch, J.; Hoeschele, M.; Springer, D.; Rudd, A.
2008-05-01
Today's gas tankless water heaters offer significant energy savings over conventional gas storage tank water heaters, but savings depends on the draw pattern. A one-node model incorporating heat exchanger mass is used to address this and other issues. Key model parameters are determined from least-squares regression on short-term data, including burner efficiency, thermal capacitance, and thermal loss coefficient. The calibrated model ag