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.
Geostatistical Modeling of Pore Velocity
Devary, J.L.; Doctor, P.G.
1981-06-01
A significant part of evaluating a geologic formation as a nuclear waste repository involves the modeling of contaminant transport in the surrounding media in the event the repository is breached. The commonly used contaminant transport models are deterministic. However, the spatial variability of hydrologic field parameters introduces uncertainties into contaminant transport predictions. This paper discusses the application of geostatistical techniques to the modeling of spatially varying hydrologic field parameters required as input to contaminant transport analyses. Kriging estimation techniques were applied to Hanford Reservation field data to calculate hydraulic conductivity and the ground-water potential gradients. These quantities were statistically combined to estimate the groundwater pore velocity and to characterize the pore velocity estimation error. Combining geostatistical modeling techniques with product error propagation techniques results in an effective stochastic characterization of groundwater pore velocity, a hydrologic parameter required for contaminant transport analyses.
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. PMID:27566774
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.
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.
Can Geostatistical Models Represent Nature's Variability? An Analysis Using Flume Experiments
NASA Astrophysics Data System (ADS)
Scheidt, C.; Fernandes, A. M.; Paola, C.; Caers, J.
2015-12-01
The lack of understanding in the Earth's geological and physical processes governing sediment deposition render subsurface modeling subject to large uncertainty. Geostatistics is often used to model uncertainty because of its capability to stochastically generate spatially varying realizations of the subsurface. These methods can generate a range of realizations of a given pattern - but how representative are these of the full natural variability? And how can we identify the minimum set of images that represent this natural variability? Here we use this minimum set to define the geostatistical prior model: a set of training images that represent the range of patterns generated by autogenic variability in the sedimentary environment under study. The proper definition of the prior model is essential in capturing the variability of the depositional patterns. This work starts with a set of overhead images from an experimental basin that showed ongoing autogenic variability. We use the images to analyze the essential characteristics of this suite of patterns. In particular, our goal is to define a prior model (a minimal set of selected training images) such that geostatistical algorithms, when applied to this set, can reproduce the full measured variability. A necessary prerequisite is to define a measure of variability. In this study, we measure variability using a dissimilarity distance between the images. The distance indicates whether two snapshots contain similar depositional patterns. To reproduce the variability in the images, we apply an MPS algorithm to the set of selected snapshots of the sedimentary basin that serve as training images. The training images are chosen from among the initial set by using the distance measure to ensure that only dissimilar images are chosen. Preliminary investigations show that MPS can reproduce fairly accurately the natural variability of the experimental depositional system. Furthermore, the selected training images provide
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.
Geostatistical modeling of uncertainty, simulation, and proposed applications in GIScience
NASA Astrophysics Data System (ADS)
Doucette, Peter; Dolloff, John; Lenihan, Michael
2015-05-01
Geostatistical modeling of spatial uncertainty has its roots in the mining, water and oil reservoir exploration communities, and has great potential for broader applications as proposed in this paper. This paper describes the underlying statistical models and their use in both the estimation of quantities of interest and the Monte-Carlo simulation of their uncertainty or errors, including their variance or expected magnitude and their spatial correlations or inter-relationships. These quantities can include 2D or 3D terrain locations, feature vertex locations, or any specified attributes whose statistical properties vary spatially. The simulation of spatial uncertainty or errors is a practical and powerful tool for understanding the effects of error propagation in complex systems. This paper describes various simulation techniques and trades-off their generality with complexity and speed. One technique recently proposed by the authors, Fast Sequential Simulation, has the ability to simulate tens of millions of errors with specifiable variance and spatial correlations in a few seconds on a lap-top computer. This ability allows for the timely evaluation of resultant output errors or the performance of a "down-stream" module or application. It also allows for near-real time evaluation when such a simulation capability is built into the application itself.
Bayesian geostatistical modeling of Malaria Indicator Survey data in Angola.
Gosoniu, Laura; Veta, Andre Mia; Vounatsou, Penelope
2010-01-01
The 2006-2007 Angola Malaria Indicator Survey (AMIS) is the first nationally representative household survey in the country assessing coverage of the key malaria control interventions and measuring malaria-related burden among children under 5 years of age. In this paper, the Angolan MIS data were analyzed to produce the first smooth map of parasitaemia prevalence based on contemporary nationwide empirical data in the country. Bayesian geostatistical models were fitted to assess the effect of interventions after adjusting for environmental, climatic and socio-economic factors. Non-linear relationships between parasitaemia risk and environmental predictors were modeled by categorizing the covariates and by employing two non-parametric approaches, the B-splines and the P-splines. The results of the model validation showed that the categorical model was able to better capture the relationship between parasitaemia prevalence and the environmental factors. Model fit and prediction were handled within a Bayesian framework using Markov chain Monte Carlo (MCMC) simulations. Combining estimates of parasitaemia prevalence with the number of children under we obtained estimates of the number of infected children in the country. The population-adjusted prevalence ranges from in Namibe province to in Malanje province. The odds of parasitaemia in children living in a household with at least ITNs per person was by 41% lower (CI: 14%, 60%) than in those with fewer ITNs. The estimates of the number of parasitaemic children produced in this paper are important for planning and implementing malaria control interventions and for monitoring the impact of prevention and control activities. PMID:20351775
Bayesian Geostatistical Modeling of Malaria Indicator Survey Data in Angola
Gosoniu, Laura; Veta, Andre Mia; Vounatsou, Penelope
2010-01-01
The 2006–2007 Angola Malaria Indicator Survey (AMIS) is the first nationally representative household survey in the country assessing coverage of the key malaria control interventions and measuring malaria-related burden among children under 5 years of age. In this paper, the Angolan MIS data were analyzed to produce the first smooth map of parasitaemia prevalence based on contemporary nationwide empirical data in the country. Bayesian geostatistical models were fitted to assess the effect of interventions after adjusting for environmental, climatic and socio-economic factors. Non-linear relationships between parasitaemia risk and environmental predictors were modeled by categorizing the covariates and by employing two non-parametric approaches, the B-splines and the P-splines. The results of the model validation showed that the categorical model was able to better capture the relationship between parasitaemia prevalence and the environmental factors. Model fit and prediction were handled within a Bayesian framework using Markov chain Monte Carlo (MCMC) simulations. Combining estimates of parasitaemia prevalence with the number of children under we obtained estimates of the number of infected children in the country. The population-adjusted prevalence ranges from in Namibe province to in Malanje province. The odds of parasitaemia in children living in a household with at least ITNs per person was by 41% lower (CI: 14%, 60%) than in those with fewer ITNs. The estimates of the number of parasitaemic children produced in this paper are important for planning and implementing malaria control interventions and for monitoring the impact of prevention and control activities. PMID:20351775
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.
NASA Astrophysics Data System (ADS)
Yan, Hongxiang; Moradkhani, Hamid
2016-08-01
Assimilation of satellite soil moisture and streamflow data into a distributed hydrologic model has received increasing attention over the past few years. This study provides a detailed analysis of the joint and separate assimilation of streamflow and Advanced Scatterometer (ASCAT) surface soil moisture into a distributed Sacramento Soil Moisture Accounting (SAC-SMA) model, with the use of recently developed particle filter-Markov chain Monte Carlo (PF-MCMC) method. Performance is assessed over the Salt River Watershed in Arizona, which is one of the watersheds without anthropogenic effects in Model Parameter Estimation Experiment (MOPEX). A total of five data assimilation (DA) scenarios are designed and the effects of the locations of streamflow gauges and the ASCAT soil moisture on the predictions of soil moisture and streamflow are assessed. In addition, a geostatistical model is introduced to overcome the significantly biased satellite soil moisture and also discontinuity issue. The results indicate that: (1) solely assimilating outlet streamflow can lead to biased soil moisture estimation; (2) when the study area can only be partially covered by the satellite data, the geostatistical approach can estimate the soil moisture for those uncovered grid cells; (3) joint assimilation of streamflow and soil moisture from geostatistical modeling can further improve the surface soil moisture prediction. This study recommends that the geostatistical model is a helpful tool to aid the remote sensing technique and the hydrologic DA study.
Integrated geostatistics for modeling fluid contacts and shales in Prudhoe Bay
Perez, G.; Chopra, A.K.; Severson, C.D.
1997-12-01
Geostatistics techniques are being used increasingly to model reservoir heterogeneity at a wide range of scales. A variety of techniques is now available with differing underlying assumptions, complexity, and applications. This paper introduces a novel method of geostatistics to model dynamic gas-oil contacts and shales in the Prudhoe Bay reservoir. The method integrates reservoir description and surveillance data within the same geostatistical framework. Surveillance logs and shale data are transformed to indicator variables. These variables are used to evaluate vertical and horizontal spatial correlation and cross-correlation of gas and shale at different times and to develop variogram models. Conditional simulation techniques are used to generate multiple three-dimensional (3D) descriptions of gas and shales that provide a measure of uncertainty. These techniques capture the complex 3D distribution of gas-oil contacts through time. The authors compare results of the geostatistical method with conventional techniques as well as with infill wells drilled after the study. Predicted gas-oil contacts and shale distributions are in close agreement with gas-oil contacts observed at infill wells.
A geostatistical methodology to assess the accuracy of unsaturated flow models
Smoot, J.L.; Williams, R.E.
1996-04-01
The Pacific Northwest National Laboratory spatiotemporal movement of water injected into (PNNL) has developed a Hydrologic unsaturated sediments at the Hanford Site in Evaluation Methodology (HEM) to assist the Washington State was used to develop a new U.S. Nuclear Regulatory Commission in method for evaluating mathematical model evaluating the potential that infiltrating meteoric predictions. Measured water content data were water will produce leachate at commercial low- interpolated geostatistically to a 16 x 16 x 36 level radioactive waste disposal sites. Two key grid at several time intervals. Then a issues are raised in the HEM: (1) evaluation of mathematical model was used to predict water mathematical models that predict facility content at the same grid locations at the selected performance, and (2) estimation of the times. Node-by-node comparison of the uncertainty associated with these mathematical mathematical model predictions with the model predictions. The technical objective of geostatistically interpolated values was this research is to adapt geostatistical tools conducted. The method facilitates a complete commonly used for model parameter estimation accounting and categorization of model error at to the problem of estimating the spatial every node. The comparison suggests that distribution of the dependent variable to be model results generally are within measurement calculated by the model. To fulfill this error. The worst model error occurs in silt objective, a database describing the lenses and is in excess of measurement error.
NASA Astrophysics Data System (ADS)
Matiatos, Ioannis; Varouhakis, Emmanouil A.; Papadopoulou, Maria P.
2015-04-01
level and nitrate concentrations were produced and compared with those obtained from groundwater and mass transport numerical models. Preliminary results showed similar efficiency of the spatiotemporal geostatistical method with the numerical models. However data requirements of the former model were significantly less. Advantages and disadvantages of the methods performance were analysed and discussed indicating the characteristics of the different approaches.
NASA Astrophysics Data System (ADS)
Linde, Niklas; Lochbühler, Tobias; Dogan, Mine; Van Dam, Remke L.
2015-12-01
We propose a new framework to compare alternative geostatistical descriptions of a given site. Multiple realizations of each of the considered geostatistical models and their corresponding tomograms (based on inversion of noise-contaminated simulated data) are used as a multivariate training image. The training image is scanned with a direct sampling algorithm to obtain conditional realizations of hydraulic conductivity that are not only in agreement with the geostatistical model, but also honor the spatially varying resolution of the site-specific tomogram. Model comparison is based on the quality of the simulated geophysical data from the ensemble of conditional realizations. The tomogram in this study is obtained by inversion of cross-hole ground-penetrating radar (GPR) first-arrival travel time data acquired at the MAcro-Dispersion Experiment (MADE) site in Mississippi (USA). Various heterogeneity descriptions ranging from multi-Gaussian fields to fields with complex multiple-point statistics inferred from outcrops are considered. Under the assumption that the relationship between porosity and hydraulic conductivity inferred from local measurements is valid, we find that conditioned multi-Gaussian realizations and derivatives thereof can explain the crosshole geophysical data. A training image based on an aquifer analog from Germany was found to be in better agreement with the geophysical data than the one based on the local outcrop, which appears to under-represent high hydraulic conductivity zones. These findings are only based on the information content in a single resolution-limited tomogram and extending the analysis to tracer or higher resolution surface GPR data might lead to different conclusions (e.g., that discrete facies boundaries are necessary). Our framework makes it possible to identify inadequate geostatistical models and petrophysical relationships, effectively narrowing the space of possible heterogeneity representations.
A conceptual sedimentological-geostatistical model of aquifer heterogeneity based on outcrop studies
Davis, J.M.
1994-01-01
Three outcrop studies were conducted in deposits of different depositional environments. At each site, permeability measurements were obtained with an air-minipermeameter developed as part of this study. In addition, the geological units were mapped with either surveying, photographs, or both. Geostatistical analysis of the permeability data was performed to estimate the characteristics of the probability distribution function and the spatial correlation structure. The information obtained from the geological mapping was then compared with the results of the geostatistical analysis for any relationships that may exist. The main field site was located in the Albuquerque Basin of central New Mexico at an outcrop of the Pliocene-Pleistocene Sierra Ladrones Formation. The second study was conducted on the walls of waste pits in alluvial fan deposits at the Nevada Test Site. The third study was conducted on an outcrop of an eolian deposit (miocene) south of Socorro, New Mexico. The results of the three studies were then used to construct a conceptual model relating depositional environment to geostatistical models of heterogeneity. The model presented is largely qualitative but provides a basis for further hypothesis formulation and testing.
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)
de Fouquet, Chantal; Malherbe, Laure; Ung, Anthony
2011-07-01
Deterministic models have become essential tools to forecast and map concentration fields of atmospheric pollutants like ozone. Those models are regularly updated and improved by incorporating recent theoretical developments and using more precise input data. Unavoidable differences with in situ measurements still remain, which need to be better understood. This study investigates those discrepancies in a geostatistical framework by comparing the temporal variability of ozone hourly surface concentrations simulated by a chemistry-transport model, CHIMERE, and measured across France. More than 200 rural and urban background monitoring sites are considered. The relationship between modelled and observed data is complex. Ozone concentrations evolve according to various time scales. CHIMERE correctly accounts for those different scales of variability but is usually unable to reproduce the exact magnitude of each temporal component. Such difficulty cannot be entirely attributed to the difference in spatial support between grid cell averages and punctual observations. As a result of this exploratory analysis, the common multivariate geostatistical model, known as the linear model of coregionalization, is used to describe the temporal variability of ozone hourly concentrations and the relationship between simulated and observed values at each observation point. The fitted parameters of the model can then be interpreted. Their distribution in space provides objective criteria to delimitate the areas where the chemistry-transport model is more or less reliable.
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
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
Building on crossvalidation for increasing the quality of geostatistical modeling
Olea, R.A.
2012-01-01
The random function is a mathematical model commonly used in the assessment of uncertainty associated with a spatially correlated attribute that has been partially sampled. There are multiple algorithms for modeling such random functions, all sharing the requirement of specifying various parameters that have critical influence on the results. The importance of finding ways to compare the methods and setting parameters to obtain results that better model uncertainty has increased as these algorithms have grown in number and complexity. Crossvalidation has been used in spatial statistics, mostly in kriging, for the analysis of mean square errors. An appeal of this approach is its ability to work with the same empirical sample available for running the algorithms. This paper goes beyond checking estimates by formulating a function sensitive to conditional bias. Under ideal conditions, such function turns into a straight line, which can be used as a reference for preparing measures of performance. Applied to kriging, deviations from the ideal line provide sensitivity to the semivariogram lacking in crossvalidation of kriging errors and are more sensitive to conditional bias than analyses of errors. In terms of stochastic simulation, in addition to finding better parameters, the deviations allow comparison of the realizations resulting from the applications of different methods. Examples show improvements of about 30% in the deviations and approximately 10% in the square root of mean square errors between reasonable starting modelling and the solutions according to the new criteria. ?? 2011 US Government.
Joint space-time geostatistical model for air quality surveillance
NASA Astrophysics Data System (ADS)
Russo, A.; Soares, A.; Pereira, M. J.
2009-04-01
Air pollution and peoples' generalized concern about air quality are, nowadays, considered to be a global problem. Although the introduction of rigid air pollution regulations has reduced pollution from industry and power stations, the growing number of cars on the road poses a new pollution problem. Considering the characteristics of the atmospheric circulation and also the residence times of certain pollutants in the atmosphere, a generalized and growing interest on air quality issues led to research intensification and publication of several articles with quite different levels of scientific depth. As most natural phenomena, air quality can be seen as a space-time process, where space-time relationships have usually quite different characteristics and levels of uncertainty. As a result, the simultaneous integration of space and time is not an easy task to perform. This problem is overcome by a variety of methodologies. The use of stochastic models and neural networks to characterize space-time dispersion of air quality is becoming a common practice. The main objective of this work is to produce an air quality model which allows forecasting critical concentration episodes of a certain pollutant by means of a hybrid approach, based on the combined use of neural network models and stochastic simulations. A stochastic simulation of the spatial component with a space-time trend model is proposed to characterize critical situations, taking into account data from the past and a space-time trend from the recent past. To identify near future critical episodes, predicted values from neural networks are used at each monitoring station. In this paper, we describe the design of a hybrid forecasting tool for ambient NO2 concentrations in Lisbon, Portugal.
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
Cay, Tayfun; Uyan, Mevlut
2009-12-01
Groundwater is one of the most important resources used for drinking and utility and irrigation purposes in the city of Konya, Turkey, as in many areas. The purpose of this study is to evaluate spatial and temporal changes in the level of groundwater by using geostatistical methods based on data from 91 groundwater wells during the period 1999 to 2003. Geostatistical methods have been used widely as a convenient tool to make decisions on the management of groundwater levels. To evaluate the spatial and temporal changes in the level of the groundwater, a vector-based geographic information system software package, ArcGIS 9.1 (Environmental Systems Research Institute, Redlands, California), was used for the application of an ordinary kriging method, with cross-validation leading to the estimation of groundwater levels. The average value of variogram (spherical model) for the spatial analysis was approximately 2150 m. Results of ordinary kriging for groundwater level drops were underestimated by 17%. Cross-validation errors were within an acceptable level. The kriging model also helps to detect risk-prone areas for groundwater abstraction. PMID:20099631
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.
NASA Astrophysics Data System (ADS)
Atzberger, C.; Richter, K.
2009-09-01
The robust and accurate retrieval of vegetation biophysical variables using radiative transfer models (RTM) is seriously hampered by the ill-posedness of the inverse problem. With this research we further develop our previously published (object-based) inversion approach [Atzberger (2004)]. The object-based RTM inversion takes advantage of the geostatistical fact that the biophysical characteristics of nearby pixel are generally more similar than those at a larger distance. A two-step inversion based on PROSPECT+SAIL generated look-up-tables is presented that can be easily implemented and adapted to other radiative transfer models. The approach takes into account the spectral signatures of neighboring pixel and optimizes a common value of the average leaf angle (ALA) for all pixel of a given image object, such as an agricultural field. Using a large set of leaf area index (LAI) measurements (n = 58) acquired over six different crops of the Barrax test site, Spain), we demonstrate that the proposed geostatistical regularization yields in most cases more accurate and spatially consistent results compared to the traditional (pixel-based) inversion. Pros and cons of the approach are discussed and possible future extensions presented.
Local Geostatistical Models and Big Data in Hydrological and Ecological Applications
NASA Astrophysics Data System (ADS)
Hristopulos, Dionissios
2015-04-01
The advent of the big data era creates new opportunities for environmental and ecological modelling but also presents significant challenges. The availability of remote sensing images and low-cost wireless sensor networks implies that spatiotemporal environmental data to cover larger spatial domains at higher spatial and temporal resolution for longer time windows. Handling such voluminous data presents several technical and scientific challenges. In particular, the geostatistical methods used to process spatiotemporal data need to overcome the dimensionality curse associated with the need to store and invert large covariance matrices. There are various mathematical approaches for addressing the dimensionality problem, including change of basis, dimensionality reduction, hierarchical schemes, and local approximations. We present a Stochastic Local Interaction (SLI) model that can be used to model local correlations in spatial data. SLI is a random field model suitable for data on discrete supports (i.e., regular lattices or irregular sampling grids). The degree of localization is determined by means of kernel functions and appropriate bandwidths. The strength of the correlations is determined by means of coefficients. In the "plain vanilla" version the parameter set involves scale and rigidity coefficients as well as a characteristic length. The latter determines in connection with the rigidity coefficient the correlation length of the random field. The SLI model is based on statistical field theory and extends previous research on Spartan spatial random fields [2,3] from continuum spaces to explicitly discrete supports. The SLI kernel functions employ adaptive bandwidths learned from the sampling spatial distribution [1]. The SLI precision matrix is expressed explicitly in terms of the model parameter and the kernel function. Hence, covariance matrix inversion is not necessary for parameter inference that is based on leave-one-out cross validation. This property
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.
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
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
Integration of geostatistical techniques and intuitive geology in the 3-D modeling process
Heine, C.J.; Cooper, D.H.
1995-08-01
The development of 3-D geologic models for reservoir description and simulation has traditionally relied on the computer derived interpolation of well data in a geocelluar stratigraphic framework. The quality of the interpolation has been directly dependent on the nature of the interpolation method, and ability of the interpolation scheme to accurately predict the value of geologic attributes away from the well. Typically, interpolation methods employ deterministic or geostatistical algorithms which offer limited capacity for integrating data derived from secondary analyses. These secondary analyses, which might include the results from 3-D seismic inversion, borehole imagery studies, or deductive reasoning, introduce a subjective component into what would otherwise be restricted to a purely mathematical treatment of geologic data. At Saudi ARAMCO an increased emphasis is being placed on the role of the reservoir geologist in the development of 3-D geologic models. Quantitative results, based on numerical computations, are being enhanced with intuitive geology, derived from years of cumulative professional experience and expertise. Techniques such as template modeling and modified conditional simulation, are yielding 3-D geologic models, which not only more accurately reflect the geology of the reservoir, but also preserve geologic detail throughout the simulation process. This incorporation of secondary data sources and qualitative analysis has been successfully demonstrated in a clastic reservoir environment in Central Saudi Arabia, and serves as a prototype for future 3-D geologic model development.
Integration of geostatistical techniques and intuitive geology in the 3-D modeling process
Heine, C.J.; Cooper, D.H. )
1996-01-01
The development of 3-D geologic models for reservoir description and simulation has traditionally relied on the computer derived interpolation of well data in a geocelluar stratigraphic framework. The quality of the interpolation has been directly dependent on the nature of the interpolation method, and ability of the Interpolation scheme to accurately predict the value of geologic attributes away from the well. Typically, interpolation methods employ deterministic or geostatistical algorithms which offer limited capacity for Integrating data derived from secondary analyses. These secondary analyses, which might include the results from 3-D seismic inversion, borehole imagery studies, or deductive reasoning, introduce a subjective component into what would otherwise be restricted to a purely mathematical treatment of geologic data. At Saudi ARAMCO an increased emphases is being placed on the role of the reservoir geologist in the development of 3-D geologic models. Quantitative results, based on numerical computations, are being enhanced with intuitive geology, derived from years of cumulative professional experience and expertise. Techniques such as template modeling and modified conditional simulation, are yielding 3-D geologic models, which not only more accurately reflect the geology of the reservoir, but also preserve geologic detail throughout the simulation process. This incorporation of secondary data sources and qualitative analysis has been successfully demonstrated in a clastic reservoir environment in Central Saudi Arabia, and serves as a prototype for future 3-D geologic model development.
Integration of geostatistical techniques and intuitive geology in the 3-D modeling process
Heine, C.J.; Cooper, D.H.
1996-12-31
The development of 3-D geologic models for reservoir description and simulation has traditionally relied on the computer derived interpolation of well data in a geocelluar stratigraphic framework. The quality of the interpolation has been directly dependent on the nature of the interpolation method, and ability of the Interpolation scheme to accurately predict the value of geologic attributes away from the well. Typically, interpolation methods employ deterministic or geostatistical algorithms which offer limited capacity for Integrating data derived from secondary analyses. These secondary analyses, which might include the results from 3-D seismic inversion, borehole imagery studies, or deductive reasoning, introduce a subjective component into what would otherwise be restricted to a purely mathematical treatment of geologic data. At Saudi ARAMCO an increased emphases is being placed on the role of the reservoir geologist in the development of 3-D geologic models. Quantitative results, based on numerical computations, are being enhanced with intuitive geology, derived from years of cumulative professional experience and expertise. Techniques such as template modeling and modified conditional simulation, are yielding 3-D geologic models, which not only more accurately reflect the geology of the reservoir, but also preserve geologic detail throughout the simulation process. This incorporation of secondary data sources and qualitative analysis has been successfully demonstrated in a clastic reservoir environment in Central Saudi Arabia, and serves as a prototype for future 3-D geologic model development.
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.
Predictive risk mapping of schistosomiasis in Brazil using Bayesian geostatistical models.
Scholte, Ronaldo G C; Gosoniu, Laura; Malone, John B; Chammartin, Frédérique; Utzinger, Jürg; Vounatsou, Penelope
2014-04-01
Schistosomiasis is one of the most common parasitic diseases in tropical and subtropical areas, including Brazil. A national control programme was initiated in Brazil in the mid-1970s and proved successful in terms of morbidity control, as the number of cases with hepato-splenic involvement was reduced significantly. To consolidate control and move towards elimination, there is a need for reliable maps on the spatial distribution of schistosomiasis, so that interventions can target communities at highest risk. The purpose of this study was to map the distribution of Schistosoma mansoni in Brazil. We utilized readily available prevalence data from the national schistosomiasis control programme for the years 2005-2009, derived remotely sensed climatic and environmental data and obtained socioeconomic data from various sources. Data were collated into a geographical information system and Bayesian geostatistical models were developed. Model-based maps identified important risk factors related to the transmission of S. mansoni and confirmed that environmental variables are closely associated with indices of poverty. Our smoothed predictive risk map, including uncertainty, highlights priority areas for intervention, namely the northern parts of North and Southeast regions and the eastern part of Northeast region. Our predictive risk map provides a useful tool for to strengthen existing surveillance-response mechanisms. PMID:24361640
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
NASA Astrophysics Data System (ADS)
Wingle, William L.; Poeter, Eileen P.; McKenna, Sean A.
1999-05-01
UNCERT is a 2D and 3D geostatistics, uncertainty analysis and visualization software package applied to ground water flow and contaminant transport modeling. It is a collection of modules that provides tools for linear regression, univariate statistics, semivariogram analysis, inverse-distance gridding, trend-surface analysis, simple and ordinary kriging and discrete conditional indicator simulation. Graphical user interfaces for MODFLOW and MT3D, ground water flow and contaminant transport models, are provided for streamlined data input and result analysis. Visualization tools are included for displaying data input and output. These include, but are not limited to, 2D and 3D scatter plots, histograms, box and whisker plots, 2D contour maps, surface renderings of 2D gridded data and 3D views of gridded data. By design, UNCERT's graphical user interface and visualization tools facilitate model design and analysis. There are few built in restrictions on data set sizes and each module (with two exceptions) can be run in either graphical or batch mode. UNCERT is in the public domain and is available from the World Wide Web with complete on-line and printable (PDF) documentation. UNCERT is written in ANSI-C with a small amount of FORTRAN77, for UNIX workstations running X-Windows and Motif (or Lesstif). This article discusses the features of each module and demonstrates how they can be used individually and in combination. The tools are applicable to a wide range of fields and are currently used by researchers in the ground water, mining, mathematics, chemistry and geophysics, to name a few disciplines.
NASA Astrophysics Data System (ADS)
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.
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.
Viswanathan, Hari S; Robinson, Bruce A; Gable, Carl W; Carey, James W
2003-01-01
Retardation of certain radionuclides due to sorption to zeolitic minerals is considered one of the major barriers to contaminant transport in the unsaturated zone of Yucca Mountain. However, zeolitically altered areas are lower in permeability than unaltered regions, which raises the possibility that contaminants might bypass the sorptive zeolites. The relationship between hydrologic and chemical properties must be understood to predict the transport of radionuclides through zeolitically altered areas. In this study, we incorporate mineralogical information into an unsaturated zone transport model using geostatistical techniques to correlate zeolitic abundance to hydrologic and chemical properties. Geostatistical methods are used to develop variograms, kriging maps, and conditional simulations of zeolitic abundance. We then investigate, using flow and transport modeling on a heterogeneous field, the relationship between percent zeolitic alteration, permeability changes due to alteration, sorption due to alteration, and their overall effect on radionuclide transport. We compare these geostatistical simulations to a simplified threshold method in which each spatial location in the model is assigned either zeolitic or vitric properties based on the zeolitic abundance at that location. A key conclusion is that retardation due to sorption predicted by using the continuous distribution is larger than the retardation predicted by the threshold method. The reason for larger retardation when using the continuous distribution is a small but significant sorption at locations with low zeolitic abundance. If, for practical reasons, models with homogeneous properties within each layer are used, we recommend setting nonzero K(d)s in the vitric tuffs to mimic the more rigorous continuous distribution simulations. Regions with high zeolitic abundance may not be as effective in retarding radionuclides such as Neptunium since these rocks are lower in permeability and contaminants can
Lee, K.H.
1997-09-01
Numerical and geostatistical analyses show that the artificial smoothing effect of kriging removes high permeability flow paths from hydrogeologic data sets, reducing simulated contaminant transport rates in heterogeneous vadose zone systems. therefore, kriging alone is not recommended for estimating the spatial distribution of soil hydraulic properties for contaminant transport analysis at vadose zone sites. Vadose zone transport if modeled more effectively by combining kriging with stochastic simulation to better represent the high degree of spatial variability usually found in the hydraulic properties of field soils. However, kriging is a viable technique for estimating the initial mass distribution of contaminants in the subsurface.
Geostatistical simulations for radon indoor with a nested model including the housing factor.
Cafaro, C; Giovani, C; Garavaglia, M
2016-01-01
The radon prone areas definition is matter of many researches in radioecology, since radon is considered a leading cause of lung tumours, therefore the authorities ask for support to develop an appropriate sanitary prevention strategy. In this paper, we use geostatistical tools to elaborate a definition accounting for some of the available information about the dwellings. Co-kriging is the proper interpolator used in geostatistics to refine the predictions by using external covariates. In advance, co-kriging is not guaranteed to improve significantly the results obtained by applying the common lognormal kriging. Here, instead, such multivariate approach leads to reduce the cross-validation residual variance to an extent which is deemed as satisfying. Furthermore, with the application of Monte Carlo simulations, the paradigm provides a more conservative radon prone areas definition than the one previously made by lognormal kriging. PMID:26547362
Samimi, B.; Bagherpour, H.; Nioc, A.
1995-08-01
The geological reservoir study of the supergiant Ahwaz field significantly improved the history matching process in many aspects, particularly the development of a geostatistical model which allowed a sound basis for changes and by delivering much needed accurate estimates of grid block vertical permeabilities. The geostatistical reservoir evaluation was facilitated by using the Heresim package and litho-stratigraphic zonations for the entire field. For each of the geological zones, 3-dimensional electrolithofacies and petrophysical property distributions (realizations) were treated which captured the heterogeneities which significantly affected fluid flow. However, as this level of heterogeneity was at a significantly smaller scale than the flow simulation grid blocks, a scaling up effort was needed to derive the effective flow properties of the blocks (porosity, horizontal and vertical permeability, and water saturation). The properties relating to the static reservoir description were accurately derived by using stream tube techniques developed in-house whereas, the relative permeabilities of the grid block were derived by dynamic pseudo relative permeability techniques. The prediction of vertical and lateral communication and water encroachment was facilitated by a close integration of pressure, saturation data, geostatistical modelling and sedimentological studies of the depositional environments and paleocurrents. The nature of reservoir barriers and baffles varied both vertically and laterally in this heterogeneous reservoir. Maps showing differences in pressure between zones after years of production served as a guide to integrating the static geological studies to the dynamic behaviour of each of the 16 reservoir zones. The use of deep wells being drilled to a deeper reservoir provided data to better understand the sweep efficiency and the continuity of barriers and baffles.
NASA Astrophysics Data System (ADS)
Moradkhani, Hamid; Yan, Hongxiang
2016-04-01
Soil moisture simulation and prediction are increasingly used to characterize agricultural droughts but the process suffers from data scarcity and quality. The satellite soil moisture observations could be used to improve model predictions with data assimilation. Remote sensing products, however, are typically discontinuous in spatial-temporal coverages; while simulated soil moisture products are potentially biased due to the errors in forcing data, parameters, and deficiencies of model physics. This study attempts to provide a detailed analysis of the joint and separate assimilation of streamflow and Advanced Scatterometer (ASCAT) surface soil moisture into a fully distributed hydrologic model, with the use of recently developed particle filter-Markov chain Monte Carlo (PF-MCMC) method. A geostatistical model is introduced to overcome the satellite soil moisture discontinuity issue where satellite data does not cover the whole study region or is significantly biased, and the dominant land cover is dense vegetation. The results indicate that joint assimilation of soil moisture and streamflow has minimal effect in improving the streamflow prediction, however, the surface soil moisture field is significantly improved. The combination of DA and geostatistical approach can further improve the surface soil moisture prediction.
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
2015-01-01
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
Andrade, A I A S S; Stigter, T Y
2013-04-01
In this study multivariate and geostatistical methods are jointly applied to model the spatial and temporal distribution of arsenic (As) concentrations in shallow groundwater as a function of physicochemical, hydrogeological and land use parameters, as well as to assess the related uncertainty. The study site is located in the Mondego River alluvial body in Central Portugal, where maize, rice and some vegetable crops dominate. In a first analysis scatter plots are used, followed by the application of principal component analysis to two different data matrices, of 112 and 200 samples, with the aim of detecting associations between As levels and other quantitative parameters. In the following phase explanatory models of As are created through factorial regression based on correspondence analysis, integrating both quantitative and qualitative parameters. Finally, these are combined with indicator-geostatistical techniques to create maps indicating the predicted probability of As concentrations in groundwater exceeding the current global drinking water guideline of 10 μg/l. These maps further allow assessing the uncertainty and representativeness of the monitoring network. A clear effect of the redox state on the presence of As is observed, and together with significant correlations with dissolved oxygen, nitrate, sulfate, iron, manganese and alkalinity, points towards the reductive dissolution of Fe (hydr)oxides as the essential mechanism of As release. The association of high As values with rice crop, known to promote reduced environments due to ponding, further corroborates this hypothesis. An additional source of As from fertilizers cannot be excluded, as the correlation with As is higher where rice is associated with vegetables, normally associated with higher fertilization rates. The best explanatory model of As occurrence integrates the parameters season, crop type, well and water depth, nitrate and Eh, though a model without the last two parameters also gives
Implementation of the Iterative Proportion Fitting Algorithm for Geostatistical Facies Modeling
Li Yupeng Deutsch, Clayton V.
2012-06-15
In geostatistics, most stochastic algorithm for simulation of categorical variables such as facies or rock types require a conditional probability distribution. The multivariate probability distribution of all the grouped locations including the unsampled location permits calculation of the conditional probability directly based on its definition. In this article, the iterative proportion fitting (IPF) algorithm is implemented to infer this multivariate probability. Using the IPF algorithm, the multivariate probability is obtained by iterative modification to an initial estimated multivariate probability using lower order bivariate probabilities as constraints. The imposed bivariate marginal probabilities are inferred from profiles along drill holes or wells. In the IPF process, a sparse matrix is used to calculate the marginal probabilities from the multivariate probability, which makes the iterative fitting more tractable and practical. This algorithm can be extended to higher order marginal probability constraints as used in multiple point statistics. The theoretical framework is developed and illustrated with estimation and simulation example.
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)
Todd, M Jason; Lowrance, R Richard; Goovaerts, Pierre; Vellidis, George; Pringle, Catherine M
2010-10-15
Blackwater streams are found throughout the Coastal Plain of the southeastern United States and are characterized by a series of instream floodplain swamps that play a critical role in determining the water quality of these systems. Within the state of Georgia, many of these streams are listed in violation of the state's dissolved oxygen (DO) standard. Previous work has shown that sediment oxygen demand (SOD) is elevated in instream floodplain swamps and due to these areas of intense oxygen demand, these locations play a major role in determining the oxygen balance of the watershed as a whole. This work also showed SOD rates to be positively correlated with the concentration of total organic carbon. This study builds on previous work by using geostatistics and Sequential Gaussian Simulation to investigate the patchiness and distribution of total organic carbon (TOC) at the reach scale. This was achieved by interpolating TOC observations and simulated SOD rates based on a linear regression. Additionally, this study identifies areas within the stream system prone to high SOD at representative 3rd and 5th order locations. Results show that SOD was spatially correlated with the differences in distribution of TOC at both locations and that these differences in distribution are likely a result of the differing hydrologic regime and watershed position. Mapping of floodplain soils at the watershed scale shows that areas of organic sediment are widespread and become more prevalent in higher order streams. DO dynamics within blackwater systems are a complicated mix of natural and anthropogenic influences, but this paper illustrates the importance of instream swamps in enhancing SOD at the watershed scale. Moreover, our study illustrates the influence of instream swamps on oxygen demand while providing support that many of these systems are naturally low in DO. PMID:20938491
Todd, M. Jason; Lowrance, R. Richard; Goovaerts, Pierre; Vellidis, George; Pringle, Catherine M.
2010-01-01
Blackwater streams are found throughout the Coastal Plain of the southeastern United States and are characterized by a series of instream floodplain swamps that play a critical role in determining the water quality of these systems. Within the state of Georgia, many of these streams are listed in violation of the state’s dissolved oxygen (DO) standard. Previous work has shown that sediment oxygen demand (SOD) is elevated in instream floodplain swamps and due to these areas of intense oxygen demand, these locations play a major role in determining the oxygen balance of the watershed as a whole. This work also showed SOD rates to be positively correlated with the concentration of total organic carbon. This study builds on previous work by using geostatistics and Sequential Gaussian Simulation to investigate the patchiness and distribution of total organic carbon (TOC) at the reach scale. This was achieved by interpolating TOC observations and simulated SOD rates based on a linear regression. Additionally, this study identifies areas within the stream system prone to high SOD at representative 3rd and 5th order locations. Results show that SOD was spatially correlated with the differences in distribution of TOC at both locations and that these differences in distribution are likely a result of the differing hydrologic regime and watershed position. Mapping of floodplain soils at the watershed scale shows that areas of organic sediment are widespread and become more prevalent in higher order streams. DO dynamics within blackwater systems are a complicated mix of natural and anthropogenic influences, but this paper illustrates the importance of instream swamps in enhancing SOD at the watershed scale. Moreover, our study illustrates the influence of instream swamps on oxygen demand while providing support that many of these systems are naturally low in DO. PMID:20938491
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)
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.
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.
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
Dummer, T J B; Yu, Z M; Nauta, L; Murimboh, J D; Parker, L
2015-02-01
Arsenic is a naturally occurring class 1 human carcinogen that is widespread in private drinking water wells throughout the province of Nova Scotia in Canada. In this paper we explore the spatial variation in toenail arsenic concentrations (arsenic body burden) in Nova Scotia. We describe the regional distribution of arsenic concentrations in private well water supplies in the province, and evaluate the geological and environmental features associated with higher levels of arsenic in well water. We develop geostatistical process models to predict high toenail arsenic concentrations and high well water arsenic concentrations, which have utility for studies where no direct measurements of arsenic body burden or arsenic exposure are available. 892 men and women who participated in the Atlantic Partnership for Tomorrow's Health Project provided both drinking water and toenail clipping samples. Information on socio-demographic, lifestyle and health factors was obtained with a set of standardized questionnaires. Anthropometric indices and arsenic concentrations in drinking water and toenails were measured. In addition, data on arsenic concentrations in 10,498 private wells were provided by the Nova Scotia Department of Environment. We utilised stepwise multivariable logistic regression modelling to develop separate statistical models to: a) predict high toenail arsenic concentrations (defined as toenail arsenic levels ≥0.12 μg g(-1)) and b) predict high well water arsenic concentrations (defined as well water arsenic levels ≥5.0 μg L(-1)). We found that the geological and environmental information that predicted well water arsenic concentrations can also be used to accurately predict toenail arsenic concentrations. We conclude that geological and environmental factors contributing to arsenic contamination in well water are the major contributing influences on arsenic body burden among Nova Scotia residents. Further studies are warranted to assess appropriate
NASA Astrophysics Data System (ADS)
Tadeu Pereira, Gener; Ribeiro de Oliveira, Ismênia; De Bortoli Teixeira, Daniel; Arantes Camargo, Livia; Rodrigo Panosso, Alan; Marques, José, Jr.
2015-04-01
Phosphorus is one of the limiting nutrients for sugarcane development in Brazilian soils. The spatial variability of this nutrient is great, defined by the properties that control its adsorption and desorption reactions. Spatial estimates to characterize this variability are based on geostatistical interpolation. Thus, the assessment of the uncertainty of estimates associated with the spatial distribution of available P (Plabile) is decisive to optimize the use of phosphate fertilizers. The purpose of this study was to evaluate the performance of sequential Gaussian simulation (sGs) and ordinary kriging (OK) in the modeling of uncertainty in available P estimates. A sampling grid with 626 points was established in a 200-ha experimental sugarcane field in Tabapuã, São Paulo State, Brazil. The soil was sampled in the crossover points of a regular grid with intervals of 50 m. From the observations, 63 points, approximately 10% of sampled points were randomly selected before the geostatistical modeling of the composition of a data set used in the validation process modeling, while the remaining 563 points were used for the predictions variable in a place not sampled. The sGs generated 200 realizations. From the realizations generated, different measures of estimation and uncertainty were obtained. The standard deviation, calculated point to point, all simulated maps provided the map of deviation, used to assess local uncertainty. The visual analysis of maps of the E-type and KO showed that the spatial patterns produced by both methods were similar, however, it was possible to observe the characteristic smoothing effect of the KO especially in regions with extreme values. The Standardized variograms of selected realizations sGs showed both range and model similar to the variogram of the Observed date of Plabile. The variogram KO showed a distinct structure of the observed data, underestimating the variability over short distances, presenting parabolic behavior near
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.
GY SAMPLING THEORY AND GEOSTATISTICS: ALTERNATE MODELS OF VARIABILITY IN CONTINUOUS MEDIA
In the sampling theory developed by Pierre Gy, sample variability is modeled as the sum of a set of seven discrete error components. The variogram used in geostatisties provides an alternate model in which several of Gy's error components are combined in a continuous mode...
Mapping soil organic carbon stocks by robust geostatistical and boosted regression models
NASA Astrophysics Data System (ADS)
Nussbaum, Madlene; Papritz, Andreas; Baltensweiler, Andri; Walthert, Lorenz
2013-04-01
Carbon (C) sequestration in forests offsets greenhouse gas emissions. Therefore, quantifying C stocks and fluxes in forest ecosystems is of interest for greenhouse gas reporting according to the Kyoto protocol. In Switzerland, the National Forest Inventory offers comprehensive data to quantify the aboveground forest biomass and its change in time. Estimating stocks of soil organic C (SOC) in forests is more difficult because the variables needed to quantify stocks vary strongly in space and precise quantification of some of them is very costly. Based on data from 1'033 plots we modeled SOC stocks of the organic layer and the mineral soil to depths of 30 cm and 100 cm for the Swiss forested area. For the statistical modeling a broad range of covariates were available: Climate data (e. g. precipitation, temperature), two elevation models (resolutions 25 and 2 m) with respective terrain attributes and spectral reflectance data representing vegetation. Furthermore, the main mapping units of an overview soil map and a coarse scale geological map were used to coarsely represent the parent material of the soils. The selection of important covariates for SOC stocks modeling out of a large set was a major challenge for the statistical modeling. We used two approaches to deal with this problem: 1) A robust restricted maximum likelihood method to fit linear regression model with spatially correlated errors. The large number of covariates was first reduced by LASSO (Least Absolute Shrinkage and Selection Operator) and then further narrowed down to a parsimonious set of important covariates by cross-validation of the robustly fitted model. To account for nonlinear dependencies of the response on the covariates interaction terms of the latter were included in model if this improved the fit. 2) A boosted structured regression model with componentwise linear least squares or componentwise smoothing splines as base procedures. The selection of important covariates was done by the
2013-01-01
Background Soil-transmitted helminth infections affect tens of millions of individuals in the People’s Republic of China (P.R. China). There is a need for high-resolution estimates of at-risk areas and number of people infected to enhance spatial targeting of control interventions. However, such information is not yet available for P.R. China. Methods A geo-referenced database compiling surveys pertaining to soil-transmitted helminthiasis, carried out from 2000 onwards in P.R. China, was established. Bayesian geostatistical models relating the observed survey data with potential climatic, environmental and socioeconomic predictors were developed and used to predict at-risk areas at high spatial resolution. Predictors were extracted from remote sensing and other readily accessible open-source databases. Advanced Bayesian variable selection methods were employed to develop a parsimonious model. Results Our results indicate that the prevalence of soil-transmitted helminth infections in P.R. China considerably decreased from 2005 onwards. Yet, some 144 million people were estimated to be infected in 2010. High prevalence (>20%) of the roundworm Ascaris lumbricoides infection was predicted for large areas of Guizhou province, the southern part of Hubei and Sichuan provinces, while the northern part and the south-eastern coastal-line areas of P.R. China had low prevalence (<5%). High infection prevalence (>20%) with hookworm was found in Hainan, the eastern part of Sichuan and the southern part of Yunnan provinces. High infection prevalence (>20%) with the whipworm Trichuris trichiura was found in a few small areas of south P.R. China. Very low prevalence (<0.1%) of hookworm and whipworm infections were predicted for the northern parts of P.R. China. Conclusions We present the first model-based estimates for soil-transmitted helminth infections throughout P.R. China at high spatial resolution. Our prediction maps provide useful information for the spatial targeting of
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
Howes, Rosalind E.; Piel, Frédéric B.; Patil, Anand P.; Nyangiri, Oscar A.; Gething, Peter W.; Dewi, Mewahyu; Hogg, Mariana M.; Battle, Katherine E.; Padilla, Carmencita D.; Baird, J. Kevin; Hay, Simon I.
2012-01-01
Background Primaquine is a key drug for malaria elimination. In addition to being the only drug active against the dormant relapsing forms of Plasmodium vivax, primaquine is the sole effective treatment of infectious P. falciparum gametocytes, and may interrupt transmission and help contain the spread of artemisinin resistance. However, primaquine can trigger haemolysis in patients with a deficiency in glucose-6-phosphate dehydrogenase (G6PDd). Poor information is available about the distribution of individuals at risk of primaquine-induced haemolysis. We present a continuous evidence-based prevalence map of G6PDd and estimates of affected populations, together with a national index of relative haemolytic risk. Methods and Findings Representative community surveys of phenotypic G6PDd prevalence were identified for 1,734 spatially unique sites. These surveys formed the evidence-base for a Bayesian geostatistical model adapted to the gene's X-linked inheritance, which predicted a G6PDd allele frequency map across malaria endemic countries (MECs) and generated population-weighted estimates of affected populations. Highest median prevalence (peaking at 32.5%) was predicted across sub-Saharan Africa and the Arabian Peninsula. Although G6PDd prevalence was generally lower across central and southeast Asia, rarely exceeding 20%, the majority of G6PDd individuals (67.5% median estimate) were from Asian countries. We estimated a G6PDd allele frequency of 8.0% (interquartile range: 7.4–8.8) across MECs, and 5.3% (4.4–6.7) within malaria-eliminating countries. The reliability of the map is contingent on the underlying data informing the model; population heterogeneity can only be represented by the available surveys, and important weaknesses exist in the map across data-sparse regions. Uncertainty metrics are used to quantify some aspects of these limitations in the map. Finally, we assembled a database of G6PDd variant occurrences to inform a national-level index of
Geostatistical modeling of riparian forest microclimate and its implications for sampling
Eskelson, B.N.I.; Anderson, P.D.; Hagar, J.C.; Temesgen, H.
2011-01-01
Predictive models of microclimate under various site conditions in forested headwater stream - riparian areas are poorly developed, and sampling designs for characterizing underlying riparian microclimate gradients are sparse. We used riparian microclimate data collected at eight headwater streams in the Oregon Coast Range to compare ordinary kriging (OK), universal kriging (UK), and kriging with external drift (KED) for point prediction of mean maximum air temperature (Tair). Several topographic and forest structure characteristics were considered as site-specific parameters. Height above stream and distance to stream were the most important covariates in the KED models, which outperformed OK and UK in terms of root mean square error. Sample patterns were optimized based on the kriging variance and the weighted means of shortest distance criterion using the simulated annealing algorithm. The optimized sample patterns outperformed systematic sample patterns in terms of mean kriging variance mainly for small sample sizes. These findings suggest methods for increasing efficiency of microclimate monitoring in riparian areas.
NASA Astrophysics Data System (ADS)
Clark, J. S.; Rizzo, D. M.; Hession, W. C.; Watzin, M. C.; Laible, J. P.
2006-05-01
A two-dimensional hydrodynamic model (River2D) was utilized to evaluate the relationship between geomorphic conditions (as estimated using an existing rapid assessment protocol) and instream habitat quality in small Vermont streams. Six stream reaches ranging in geomorphic condition from good to poor according to the protocols were utilized for this study. We conducted detailed topographic surveys, quantified bed substrate, and measured velocity and discharge values during baseflow conditions. The reach models were calibrated with realistic roughness values based on field observations and pebble counts. After calibration, the weighted usable area (WUA) of habitat was calculated for each stream at three flows (7Q 10, median, and bankfull) using modeled parameters and habitat suitability curves for specific fish species and life stage. Brown trout (Salmo trutta), white sucker (Catostomus commersoni), and common shiner (Notropis cornutus) habitats were predicted using habitat parameters of velocity, depth, and channel substrate type for adult, juvenile, and fry stages. The predictions of reach-averaged WUA show a negative correlation to the geomorphic condition scores, indicating that the often-used rapid protocols, may not directly relate to habitat conditions at the reach spatial scale. However, the areas of high WUA are distributed in a patchy nature throughout the stream. This fluctuation of physical habitat conditions may be more important to classifying habitat than a single reach-averaged WUA score. The spatial distribution of habitat variables is not captured using either the reach-averaged WUA or geomorphic assessment scores to classify streams. Spatial analyses will be used to further evaluate the patchy nature of WUA distributions, and actual data on species distributions in the study streams will be compared to modeled habitat parameters and their spatial patterns.
Ashton, Ruth A.; Kefyalew, Takele; Rand, Alison; Sime, Heven; Assefa, Ashenafi; Mekasha, Addis; Edosa, Wasihun; Tesfaye, Gezahegn; Cano, Jorge; Teka, Hiwot; Reithinger, Richard; Pullan, Rachel L.; Drakeley, Chris J.; Brooker, Simon J.
2015-01-01
Ethiopia has a diverse ecology and geography resulting in spatial and temporal variation in malaria transmission. Evidence-based strategies are thus needed to monitor transmission intensity and target interventions. A purposive selection of dried blood spots collected during cross-sectional school-based surveys in Oromia Regional State, Ethiopia, were tested for presence of antibodies against Plasmodium falciparum and P. vivax antigens. Spatially explicit binomial models of seroprevalence were created for each species using a Bayesian framework, and used to predict seroprevalence at 5 km resolution across Oromia. School seroprevalence showed a wider prevalence range than microscopy for both P. falciparum (0–50% versus 0–12.7%) and P. vivax (0–53.7% versus 0–4.5%), respectively. The P. falciparum model incorporated environmental predictors and spatial random effects, while P. vivax seroprevalence first-order trends were not adequately explained by environmental variables, and a spatial smoothing model was developed. This is the first demonstration of serological indicators being used to detect large-scale heterogeneity in malaria transmission using samples from cross-sectional school-based surveys. The findings support the incorporation of serological indicators into periodic large-scale surveillance such as Malaria Indicator Surveys, and with particular utility for low transmission and elimination settings. PMID:25962770
Welhan, John A.; Farabaugh, Renee L.; Merrick, Melissa J.; Anderson, Steven R.
2007-01-01
The spatial distribution of sediment in the eastern Snake River Plain aquifer was evaluated and modeled to improve the parameterization of hydraulic conductivity (K) for a subregional-scale ground-water flow model being developed by the U.S. Geological Survey. The aquifer is hosted within a layered series of permeable basalts within which intercalated beds of fine-grained sediment constitute local confining units. These sediments have K values as much as six orders of magnitude lower than the most permeable basalt, and previous flow-model calibrations have shown that hydraulic conductivity is sensitive to the proportion of intercalated sediment. Stratigraphic data in the form of sediment thicknesses from 333 boreholes in and around the Idaho National Laboratory were evaluated as grouped subsets of lithologic units (composite units) corresponding to their relative time-stratigraphic position. The results indicate that median sediment abundances of the stratigraphic units below the water table are statistically invariant (stationary) in a spatial sense and provide evidence of stationarity across geologic time, as well. Based on these results, the borehole data were kriged as two-dimensional spatial data sets representing the sediment content of the layers that discretize the ground-water flow model in the uppermost 300 feet of the aquifer. Multiple indicator kriging (mIK) was used to model the geographic distribution of median sediment abundance within each layer by defining the local cumulative frequency distribution (CFD) of sediment via indicator variograms defined at multiple thresholds. The mIK approach is superior to ordinary kriging because it provides a statistically best estimate of sediment abundance (the local median) drawn from the distribution of local borehole data, independent of any assumption of normality. A methodology is proposed for delineating and constraining the assignment of hydraulic conductivity zones for parameter estimation, based on the
NASA Astrophysics Data System (ADS)
Liao, Chujiang
2015-08-01
On different degrees of desertification land, there exists different vegetation communities, and spatial structure differences are obvious among different vegetation communities. This study implemented variogram calculation using typical sample selected from the image, adopting a common global optimization method to fit them into the spherical model. The results showed that the difference is obvious among different vegetation communities for the sill and range, such as, the sill and range are smaller for sample variogram of Artemisia halodendron and Salix flavida community than that of Artemisia halodendron and Caragana microphylla community, and the range for sample variogram of Agriophyllum arenarium community is bigger than that of Artemisia halodendron and Salix flavida community, but smaller than that of Artemisia halodendron and Caragana microphylla community. Incorporating the difference of the spatial structure characterization into the vegetation classification can improve sample separation, thereby increasing the overall classification accuracy.
NASA Astrophysics Data System (ADS)
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.
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
Goovaerts, Pierre; Trinh, Hoa T.; Demond, Avery; Franzblau, Alfred; Garabrant, David; Gillespie, Brenda; Lepkowski, James; Adriaens, Peter
2008-01-01
Deposition of pollutants around point sources of contamination, such as incinerators, can display complex spatial patterns depending on prevailing weather conditions, the local topography and the characteristics of the source. Deterministic dispersion models often fail to capture the complexity observed in the field, resulting in uncertain predictions that might hamper subsequent decision-making, such as delineation of areas targeted for additional sampling or remediation. This paper describes a geostatistical simulation-based methodology that combines the detailed process-based modeling of atmospheric deposition from an incinerator with the probabilistic modeling of residual variability of field samples. The approach is used to delineate areas with high level of dioxin TEQDF-WHO98 (Toxic Equivalents) around an incinerator, accounting for 53 field data and the output of the EPA Industrial Source Complex (ISC3) dispersion model. The dispersion model explains 43.7% of the variance in soil TEQ data, while the regression residuals are spatially correlated with a range of 776 meters. One hundred realizations of soil TEQ values are simulated on a grid with a 50 meter spacing. The benefit of stochastic simulation over spatial interpolation is twofold: 1) maps of simulated point TEQ values can easily be aggregated to the geography that is the most relevant for decision making (e.g. census block, ZIP codes); and 2) the uncertainty at the larger scale is simply modeled by the empirical distribution of block-averaged simulated values. Incorporating the output of the atmospheric deposition model as spatial trend yields a more realistic prediction of the spatial distribution of TEQ value than lognormal kriging using only the field data, in particular in sparsely sampled areas away from the incinerator. The geostatistical model provided guidance for the study design (census block-based population sampling) of the University of Michigan Dioxin Exposure Study (UMDES), focused on
Ioannidis, M.; Kwiecien, M.; Chatzis, I.
1995-12-31
This paper describes a new method for the computer generation of 3-D stochastic realizations of porous media using geostatistical information obtained from high-contrast 2-D images of pore casts. The stochastic method yields model porous media with statistical properties identical to those of their real counterparts. Synthetic media obtained in this manner can form the basis for a number of studies related to the detailed characterization of the porous microstructure and, ultimately, the prediction of important petrophysical and reservoir engineering properties. In this context, direct computer estimation of the formation resistivity factor is examined using a discrete random walk algorithm. The dependence of formation factor on measureable statistical properties of the pore space is also investigated.
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.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.
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.
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
Goovaerts, Pierre; Jacquez, Geoffrey M.
2005-01-01
This paper presents the first application of spatially correlated neutral models to the detection of changes in mortality rates across space and time using the local Moran's I statistic. Sequential Gaussian simulation is used to generate realizations of the spatial distribution of mortality rates under increasingly stringent conditions: 1) reproduction of the sample histogram, 2) reproduction of the pattern of spatial autocorrelation modeled from the data, 3) incorporation of regional background obtained by geostatistical smoothing of observed mortality rates, and 4) incorporation of smooth regional background observed at a prior time interval. The simulated neutral models are then processed using two new spatio-temporal variants of the Morany's I statistic, which allow one to identify significant changes in mortality rates above and beyond past spatial patterns. Last, the results are displayed using an original classification of clusters/outliers tailored to the space-time nature of the data. Using this new methodology the space-time distribution of cervix cancer mortality rates recorded over all US State Economic Areas (SEA) is explored for 9 time periods of 5 years each. Incorporation of spatial autocorrelation leads to fewer significant SEA units than obtained under the traditional assumption of spatial independence, confirming earlier claims that Type I errors may increase when tests using the assumption of independence are applied to spatially correlated data. Integration of regional background into the neutral models yields substantially different spatial clusters and outliers, highlighting local patterns which were blurred when local Moran's I was applied under the null hypothesis of constant risk. PMID:16710441
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
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.
Preliminary Model of Porphyry Copper Deposits
Berger, Byron R.; Ayuso, Robert A.; Wynn, Jeffrey C.; Seal, Robert R., II
2008-01-01
The U.S. Geological Survey (USGS) Mineral Resources Program develops mineral-deposit models for application in USGS mineral-resource assessments and other mineral resource-related activities within the USGS as well as for nongovernmental applications. Periodic updates of models are published in order to incorporate new concepts and findings on the occurrence, nature, and origin of specific mineral deposit types. This update is a preliminary model of porphyry copper deposits that begins an update process of porphyry copper models published in USGS Bulletin 1693 in 1986. This update includes a greater variety of deposit attributes than were included in the 1986 model as well as more information about each attribute. It also includes an expanded discussion of geophysical and remote sensing attributes and tools useful in resource evaluations, a summary of current theoretical concepts of porphyry copper deposit genesis, and a summary of the environmental attributes of unmined and mined deposits.
Akita, Yasuyuki; Baldasano, Jose M; Beelen, Rob; Cirach, Marta; de Hoogh, Kees; Hoek, Gerard; Nieuwenhuijsen, Mark; Serre, Marc L; de Nazelle, Audrey
2014-04-15
In recognition that intraurban exposure gradients may be as large as between-city variations, recent air pollution epidemiologic studies have become increasingly interested in capturing within-city exposure gradients. In addition, because of the rapidly accumulating health data, recent studies also need to handle large study populations distributed over large geographic domains. Even though several modeling approaches have been introduced, a consistent modeling framework capturing within-city exposure variability and applicable to large geographic domains is still missing. To address these needs, we proposed a modeling framework based on the Bayesian Maximum Entropy method that integrates monitoring data and outputs from existing air quality models based on Land Use Regression (LUR) and Chemical Transport Models (CTM). The framework was applied to estimate the yearly average NO2 concentrations over the region of Catalunya in Spain. By jointly accounting for the global scale variability in the concentration from the output of CTM and the intraurban scale variability through LUR model output, the proposed framework outperformed more conventional approaches. PMID:24621302
Satellite servicing mission preliminary cost estimation model
NASA Technical Reports Server (NTRS)
1987-01-01
The cost model presented is a preliminary methodology for determining a rough order-of-magnitude cost for implementing a satellite servicing mission. Mission implementation, in this context, encompassess all activities associated with mission design and planning, including both flight and ground crew training and systems integration (payload processing) of servicing hardward with the Shuttle. A basic assumption made in developing this cost model is that a generic set of servicing hardware was developed and flight tested, is inventoried, and is maintained by NASA. This implies that all hardware physical and functional interfaces are well known and therefore recurring CITE testing is not required. The development of the cost model algorithms and examples of their use are discussed.
Geostatistical prediction of flow-duration curves
NASA Astrophysics Data System (ADS)
Pugliese, A.; Castellarin, A.; Brath, A.
2013-11-01
We present in this study an adaptation of Topological kriging (or Top-kriging), which makes the geostatistical procedure capable of predicting flow-duration curves (FDCs) in ungauged catchments. Previous applications of Top-kriging mainly focused on the prediction of point streamflow indices (e.g. flood quantiles, low-flow indices, etc.). In this study Top-kriging is used to predict FDCs in ungauged sites as a weighted average of standardised empirical FDCs through the traditional linear-weighting scheme of kriging methods. Our study focuses on the prediction of period-of-record FDCs for 18 unregulated catchments located in Central Italy, for which daily streamflow series with length from 5 to 40 yr are available, together with information on climate referring to the same time-span of each daily streamflow sequence. Empirical FDCs are standardised by a reference streamflow value (i.e. mean annual flow, or mean annual precipitation times the catchment drainage area) and the overall deviation of the curves from this reference value is then used for expressing the hydrological similarity between catchments and for deriving the geostatistical weights. We performed an extensive leave-one-out cross-validation to quantify the accuracy of the proposed technique, and to compare it to traditional regionalisation models that were recently developed for the same study region. The cross-validation points out that Top-kriging is a reliable approach for predicting FDCs, which can significantly outperform traditional regional models in ungauged basins.
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.
Silué, Kigbafori D; Raso, Giovanna; Yapi, Ahoua; Vounatsou, Penelope; Tanner, Marcel; N'Goran, Eliézer K; Utzinger, Jürg
2008-01-01
Background There is a renewed political will and financial support to eradicate malaria. Spatially-explicit risk profiling will play an important role in this endeavour. Patterns of Plasmodium falciparum infection prevalence were examined among schoolchildren in a highly malaria-endemic area. Methods A questionnaire was administered and finger prick blood samples collected from 3,962 children, aged six to 16 years, attending 55 schools in a rural part of western Côte d'Ivoire. Information was gathered from the questionnaire on children's socioeconomic status and the use of bed nets for the prevention of malaria. Blood samples were processed with standardized, quality-controlled methods for diagnosis of Plasmodium spp. infections. Environmental data were obtained from satellite images and digitized maps. Bayesian variogram models for spatially-explicit risk modelling of P. falciparum infection prevalence were employed, assuming for stationary and non-stationary spatial processes. Findings The overall prevalence of P. falciparum infection was 64.9%, ranging between 34.0% and 91.9% at the unit of the school. Risk factors for a P. falciparum infection included age, socioeconomic status, not sleeping under a bed net, distance to health care facilities and a number of environmental features (i.e. normalized difference vegetation index, rainfall and distance to rivers). After taking into account spatial correlation only age remained significant. Non-stationary models performed better than stationary models. Conclusion Spatial risk profiling of P. falciparum prevalence data provides a useful tool for targeting malaria control intervention, and hence will play a role in the quest of local elimination and ultimate eradication of the disease. PMID:18570685
NASA Astrophysics Data System (ADS)
Galeana-Pizaña, J. Mauricio; López-Caloca, Alejandra; López-Quiroz, Penélope; Silván-Cárdenas, José Luis; Couturier, Stéphane
2014-08-01
Forest conservation is considered an option for mitigating the effect of greenhouse gases on global climate, hence monitoring forest carbon pools at global and local levels is important. The present study explores the capability of remote-sensing variables (vegetation indices and textures derived from SPOT-5; backscattering coefficient and interferometric coherence of ALOS PALSAR images) for modeling the spatial distribution of above-ground biomass in the Environmental Conservation Zone of Mexico City. Correlation and spatial autocorrelation coefficients were used to select significant explanatory variables in fir and pine forests. The correlation for interferometric coherence in HV polarization was negative, with correlations coefficients r = -0.83 for the fir and r = -0.75 for the pine forests. Regression-kriging showed the least root mean square error among the spatial interpolation methods used, with 37.75 tC/ha for fir forests and 29.15 tC/ha for pine forests. The results showed that a hybrid geospatial method, based on interferometric coherence data and a regression-kriging interpolator, has good potential for estimating above-ground biomass carbon.
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.
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.
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
Goovaerts, Pierre; Jacquez, Geoffrey M
2004-01-01
Background Complete Spatial Randomness (CSR) is the null hypothesis employed by many statistical tests for spatial pattern, such as local cluster or boundary analysis. CSR is however not a relevant null hypothesis for highly complex and organized systems such as those encountered in the environmental and health sciences in which underlying spatial pattern is present. This paper presents a geostatistical approach to filter the noise caused by spatially varying population size and to generate spatially correlated neutral models that account for regional background obtained by geostatistical smoothing of observed mortality rates. These neutral models were used in conjunction with the local Moran statistics to identify spatial clusters and outliers in the geographical distribution of male and female lung cancer in Nassau, Queens, and Suffolk counties, New York, USA. Results We developed a typology of neutral models that progressively relaxes the assumptions of null hypotheses, allowing for the presence of spatial autocorrelation, non-uniform risk, and incorporation of spatially heterogeneous population sizes. Incorporation of spatial autocorrelation led to fewer significant ZIP codes than found in previous studies, confirming earlier claims that CSR can lead to over-identification of the number of significant spatial clusters or outliers. Accounting for population size through geostatistical filtering increased the size of clusters while removing most of the spatial outliers. Integration of regional background into the neutral models yielded substantially different spatial clusters and outliers, leading to the identification of ZIP codes where SMR values significantly depart from their regional background. Conclusion The approach presented in this paper enables researchers to assess geographic relationships using appropriate null hypotheses that account for the background variation extant in real-world systems. In particular, this new methodology allows one to identify
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., Jr.
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.
An improved algorithm of a priori based on geostatistics
NASA Astrophysics Data System (ADS)
Chen, Jiangping; Wang, Rong; Tang, Xuehua
2008-12-01
In data mining one of the classical algorithms is Apriori which has been developed for association rule mining in large transaction database. And it cannot been directly used in spatial association rules mining. The main difference between data mining in relational DB and in spatial DB is that attributes of the neighbors of some object of interest may have an influence on the object and therefore have to be considered as well. The explicit location and extension of spatial objects define implicit relations of spatial neighborhood (such as topological, distance and direction relations) which are used by spatial data mining algorithms. Therefore, new techniques are required for effective and efficient spatial data mining. Geostatistics are statistical methods used to describe spatial relationships among sample data and to apply this analysis to the prediction of spatial and temporal phenomena. They are used to explain spatial patterns and to interpolate values at unsampled locations. This paper put forward an improved algorithm of Apriori about mining association rules with geostatistics. First the spatial autocorrelation of the attributes with location were estimated with the geostatistics methods such as kriging and Spatial Autoregressive Model (SAR). Then a spatial autocorrelation model of the attributes were built. Later an improved algorithm of apriori combined with the spatial autocorrelation model were offered to mine the spatial association rules. Last an experiment of the new algorithm were carried out on the hayfever incidence and climate factors in UK. The result shows that the output rules is matched with the references.
Geostatistical Study of Precipitation on the Island of Crete
NASA Astrophysics Data System (ADS)
Agou, Vasiliki D.; Varouchakis, Emmanouil A.; Hristopulos, Dionissios T.
2015-04-01
Understanding and predicting the spatiotemporal patterns of precipitation in the Mediterranean islands is an important topic of research, which is emphasized by alarming long-term predictions for increased drought conditions [4]. The analysis of records from drought-prone areas around the world has demonstrated that precipitation data are non-Gaussian. Typically, such data are fitted to the gamma distribution function and then transformed into a normalized index, the so-called Standardized Precipitation Index (SPI) [5]. The SPI can be defined for different time scales and has been applied to data from various regions [2]. Precipitation maps can be constructed using the stochastic method of Ordinary Kriging [1]. Such mathematical tools help to better understand the space-time variability and to plan water resources management. We present preliminary results of an ongoing investigation of the space-time precipitation distribution on the island of Crete (Greece). The study spans the time period from 1948 to 2012 and extends over an area of 8 336 km2. The data comprise monthly precipitation measured at 56 stations. Analysis of the data showed that the most severe drought occurred in 1950 followed by 1989, whereas the wettest year was 2002 followed by 1977. A spatial trend was observed with the spatially averaged annual precipitation in the West measured at about 450mm higher than in the East. Analysis of the data also revealed strong correlations between the precipitation in the western and eastern parts of the island. In addition to longitude, elevation (masl) was determined to be an important factor that exhibits strong linear correlation with precipitation. The precipitation data exhibit wet and dry periods with strong variability even during the wet period. Thus, fitting the data to specific probability distribution models has proved challenging. Different time scales, e.g. monthly, biannual, and annual have been investigated. Herein we focus on annual
GEOSTATISTICAL SAMPLING DESIGNS FOR HAZARDOUS WASTE SITES
This chapter discusses field sampling design for environmental sites and hazardous waste sites with respect to random variable sampling theory, Gy's sampling theory, and geostatistical (kriging) sampling theory. The literature often presents these sampling methods as an adversari...
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.
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.
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
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.
Preliminary results of steel containment vessel model test
Matsumoto, T.; Komine, K.; Arai, S.
1997-04-01
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.
A Preliminary Instructional Model for a Computerized Training System.
ERIC Educational Resources Information Center
Kimberlin, D. A.
A preliminary instructional model suitable for lesson preparation for a Computerized Training System is described. Topics discussed include general course architecture, general course structure, and the training decision process. Guidelines for the design of the model are presented and information is provided on the adaptation of the model to…
Recent Advances in Geostatistical Inversion
NASA Astrophysics Data System (ADS)
Kitanidis, P. K.
2011-12-01
Inverse problems are common in hydrologic applications, such as in subsurface imaging which is the identification of parameters characterizing geologic formations from hydrologic and geophysical observations. In such problems, the data do not suffice to constrain the solution to be unique. The geostatistical approach utilizes probability theory and statistical inference to assimilate data and information about structure and to explore the range of possible solutions in a systematic way. This is a progress report on recent advances in terms of formulation and computational methods. The standard implementation of the geostatistical approach to the inverse method is computationally very expensive when there are millions of unknowns and hundreds of thousands of observations, as is the case in fusing data from many sources in hydrogeology. However, depending on the specific problem, alternative formulations and numerical methods can reduce the computational problem quite dramatically. One approach can utilize formulations that involve matrices with a very high degree of sparsity combined with indirect methods of solution and strategies that minimize the number of required forward runs. The potential for this method is illustrated with an application to transient hydraulic tomography. Another approach speeds up matrix-vector multiplications by utilizing hierarchical sparsity in commonly encountered matrices, particularly prior covariance matrices. A couple of examples show how the computational cost scales with the size of the problem (number of observations and unknowns) in conventional and newer methods. Yet another fruitful approach is to rely on a large number of realizations to represent ensembles of solutions and we illustrate this approach in fusing data from two different geophysical methods. In all of these approaches, utilizing parallel processors and mixed CPU/GPU programming can significantly reduce the computational cost and make it possible to solve very
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.
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. PMID:18257009
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
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...
An Inverse Model for TETRAD: Preliminary Results
Shook, George Michael; Renner, Joel Lawrence
2002-09-01
A model-independent parameter estimation model known as PEST has been linked to the reservoir simulator TETRAD. The method of inverse modeling is briefly reviewed, and the link between PEST and TETRAD is discussed. A single example is presented that illustrates the power of parameter estimation from well observations.
V and V Efforts of Auroral Precipitation Models: Preliminary Results
NASA Technical Reports Server (NTRS)
Zheng, Yihua; Kuznetsova, Masha; Rastaetter, Lutz; Hesse, Michael
2011-01-01
Auroral precipitation models have been valuable both in terms of space weather applications and space science research. Yet very limited testing has been performed regarding model performance. A variety of auroral models are available, including empirical models that are parameterized by geomagnetic indices or upstream solar wind conditions, now casting models that are based on satellite observations, or those derived from physics-based, coupled global models. In this presentation, we will show our preliminary results regarding V&V efforts of some of the models.
Extending the Agricultural Extension Model. Preliminary Draft.
ERIC Educational Resources Information Center
Rogers, Everett M.; And Others
The purposes of this report are: to describe the main elements of the U.S. agricultural extension model and its effects on the agricultural revolution; to analyze attempts to extend this model to non-agricultural technology and/or to less developed countries; and to draw general conclusions about the diffusion of technological innovations, with…
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.
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.
Land Use Systems Model. Technical Report. Preliminary.
ERIC Educational Resources Information Center
Austin, W. Burnet
Since the service requirements of a utility depend on the distribution of population and land use in the service area, the planning for future requirements depends on accurate projections of future distributions. This systems approach model organizes land use data as an aid to facility planning. Included as variables are residential, commercial,…
Preliminary report on electromagnetic model studies
Frischknecht, F.C.; Mangan, G.B.
1960-01-01
More than 70 resopnse curves for various models have been obtained using the slingram and turam electromagnetic methods. Results show that for the slingram method, horizontal co-planar coils are usually more sensitive than vertical, co-axial or vertical, co-planar coils. The shape of the anomaly usually is simpler for the vertical coils.
Use of geostatistics for remediation planning to transcend urban political boundaries.
Milillo, Tammy M; Sinha, Gaurav; Gardella, Joseph A
2012-11-01
Soil remediation plans are often dictated by areas of jurisdiction or property lines instead of scientific information. This study exemplifies how geostatistically interpolated surfaces can substantially improve remediation planning. Ordinary kriging, ordinary co-kriging, and inverse distance weighting spatial interpolation methods were compared for analyzing surface and sub-surface soil sample data originally collected by the US EPA and researchers at the University at Buffalo in Hickory Woods, an industrial-residential neighborhood in Buffalo, NY, where both lead and arsenic contamination is present. Past clean-up efforts estimated contamination levels from point samples, but parcel and agency jurisdiction boundaries were used to define remediation sites, rather than geostatistical models estimating the spatial behavior of the contaminants in the soil. Residents were understandably dissatisfied with the arbitrariness of the remediation plan. In this study we show how geostatistical mapping and participatory assessment can make soil remediation scientifically defensible, socially acceptable, and economically feasible. PMID:22771352
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.
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.
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.
Geospatial Interpolation and Mapping of Tropospheric Ozone Pollution Using Geostatistics
Kethireddy, Swatantra R.; Tchounwou, Paul B.; Ahmad, Hafiz A.; Yerramilli, Anjaneyulu; Young, John H.
2014-01-01
Tropospheric ozone (O3) pollution is a major problem worldwide, including in the United States of America (USA), particularly during the summer months. Ozone oxidative capacity and its impact on human health have attracted the attention of the scientific community. In the USA, sparse spatial observations for O3 may not provide a reliable source of data over a geo-environmental region. Geostatistical Analyst in ArcGIS has the capability to interpolate values in unmonitored geo-spaces of interest. In this study of eastern Texas O3 pollution, hourly episodes for spring and summer 2012 were selectively identified. To visualize the O3 distribution, geostatistical techniques were employed in ArcMap. Using ordinary Kriging, geostatistical layers of O3 for all the studied hours were predicted and mapped at a spatial resolution of 1 kilometer. A decent level of prediction accuracy was achieved and was confirmed from cross-validation results. The mean prediction error was close to 0, the root mean-standardized-prediction error was close to 1, and the root mean square and average standard errors were small. O3 pollution map data can be further used in analysis and modeling studies. Kriging results and O3 decadal trends indicate that the populace in Houston-Sugar Land-Baytown, Dallas-Fort Worth-Arlington, Beaumont-Port Arthur, San Antonio, and Longview are repeatedly exposed to high levels of O3-related pollution, and are prone to the corresponding respiratory and cardiovascular health effects. Optimization of the monitoring network proves to be an added advantage for the accurate prediction of exposure levels. PMID:24434594
Assessment of spatial distribution of fallout radionuclides through geostatistics concept.
Mabit, L; Bernard, C
2007-01-01
After introducing geostatistics concept and its utility in environmental science and especially in Fallout Radionuclide (FRN) spatialisation, a case study for cesium-137 ((137)Cs) redistribution at the field scale using geostatistics is presented. On a Canadian agricultural field, geostatistics coupled with a Geographic Information System (GIS) was used to test three different techniques of interpolation [Ordinary Kriging (OK), Inverse Distance Weighting power one (IDW1) and two (IDW2)] to create a (137)Cs map and to establish a radioisotope budget. Following the optimization of variographic parameters, an experimental semivariogram was developed to determine the spatial dependence of (137)Cs. It was adjusted to a spherical isotropic model with a range of 30 m and a very small nugget effect. This (137)Cs semivariogram showed a good autocorrelation (R(2)=0.91) and was well structured ('nugget-to-sill' ratio of 4%). It also revealed that the sampling strategy was adequate to reveal the spatial correlation of (137)Cs. The spatial redistribution of (137)Cs was estimated by Ordinary Kriging and IDW to produce contour maps. A radioisotope budget was established for the 2.16 ha agricultural field under investigation. It was estimated that around 2 x 10(7)Bq of (137)Cs were missing (around 30% of the total initial fallout) and were exported by physical processes (runoff and erosion processes) from the area under investigation. The cross-validation analysis showed that in the case of spatially structured data, OK is a better interpolation method than IDW1 or IDW2 for the assessment of potential radioactive contamination and/or pollution. PMID:17673340
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
Modeling the complete Otto cycle: Preliminary version. [computer programming
NASA Technical Reports Server (NTRS)
Zeleznik, F. J.; Mcbride, B. J.
1977-01-01
A description is given of the equations and the computer program being developed to model the complete Otto cycle. The program incorporates such important features as: (1) heat transfer, (2) finite combustion rates, (3) complete chemical kinetics in the burned gas, (4) exhaust gas recirculation, and (5) manifold vacuum or supercharging. Changes in thermodynamic, kinetic and transport data as well as model parameters can be made without reprogramming. Preliminary calculations indicate that: (1) chemistry and heat transfer significantly affect composition and performance, (2) there seems to be a strong interaction among model parameters, and (3) a number of cycles must be calculated in order to obtain steady-state conditions.
Modeling the connection between development and evolution: Preliminary report
Mjolsness, E.; Reinitz, J.; Garrett, C.D.; Sharp, D.H.
1993-07-29
In this paper we outline a model which incorporates development processes into an evolutionary frame work. The model consists of three sectors describing development, genetics, and the selective environment. The formulation of models governing each sector uses dynamical grammars to describe processes in which state variables evolve in a quantitative fashion, and the number and type of participating biological entities can change. This program has previously been elaborated for development. Its extension to the other sectors of the model is discussed here and forms the basis for further approximations. A specific implementation of these ideas is described for an idealized model of the evolution of a multicellular organism. While this model doe not describe an actual biological system, it illustrates the interplay of development and evolution. Preliminary results of numerical simulations of this idealized model are presented.
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
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.
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
Mathematical modeling of normal pharyngeal bolus transport: a preliminary study.
Chang, M W; Rosendall, B; Finlayson, B A
1998-07-01
Dysphagia (difficulty in swallowing) is a common clinical symptom associated with many diseases, such as stroke, multiple sclerosis, neuromuscular diseases, and cancer. Its complications include choking, aspiration, malnutrition, cachexia, and dehydration. The goal in dysphagia management is to provide adequate nutrition and hydration while minimizing the risk of choking and aspiration. It is important to advance the individual toward oral feeding in a timely manner to enhance the recovery of swallowing function and preserve the quality of life. Current clinical assessments of dysphagia are limited in providing adequate guidelines for oral feeding. Mathematical modeling of the fluid dynamics of pharyngeal bolus transport provides a unique opportunity for studying the physiology and pathophysiology of swallowing. Finite element analysis (FEA) is a special case of computational fluid dynamics (CFD). In CFD, the flow of a fluid in a space is modeled by covering the space with a grid and predicting how the fluid moves from grid point to grid point. FEA is capable of solving problems with complex geometries and free surfaces. A preliminary pharyngeal model has been constructed using FEA. This model incorporates literature-reported, normal, anatomical data with time-dependent pharyngeal/upper esophageal sphincter (UES) wall motion obtained from videofluorography (VFG). This time-dependent wall motion can be implemented as a moving boundary condition in the model. Clinical kinematic data can be digitized from VFG studies to construct and test the mathematical model. The preliminary model demonstrates the feasibility of modeling pharyngeal bolus transport, which, to our knowledge, has not been attempted before. This model also addresses the need and the potential for CFD in understanding the physiology and pathophysiology of the pharyngeal phase of swallowing. Improvements of the model are underway. Combining the model with individualized clinical data should potentially
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
Technology Transfer Automated Retrieval System (TEKTRAN)
This report describes the data and preliminary modeling to develop a case study of model abstraction application at the watershed scale. Model abstraction is defined as the methodology for reducing the complexity of a simulation model while maintaining the validity of the simulation results with res...
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.
Combining Geostatistics with Moran’s I Analysis for Mapping Soil Heavy Metals in Beijing, China
Huo, Xiao-Ni; Li, Hong; Sun, Dan-Feng; Zhou, Lian-Di; Li, Bao-Guo
2012-01-01
Production of high quality interpolation maps of heavy metals is important for risk assessment of environmental pollution. In this paper, the spatial correlation characteristics information obtained from Moran’s I analysis was used to supplement the traditional geostatistics. According to Moran’s I analysis, four characteristics distances were obtained and used as the active lag distance to calculate the semivariance. Validation of the optimality of semivariance demonstrated that using the two distances where the Moran’s I and the standardized Moran’s I, Z(I) reached a maximum as the active lag distance can improve the fitting accuracy of semivariance. Then, spatial interpolation was produced based on the two distances and their nested model. The comparative analysis of estimation accuracy and the measured and predicted pollution status showed that the method combining geostatistics with Moran’s I analysis was better than traditional geostatistics. Thus, Moran’s I analysis is a useful complement for geostatistics to improve the spatial interpolation accuracy of heavy metals. PMID:22690179
The use of geostatistics in the study of floral phenology of Vulpia geniculata (L.) link.
León Ruiz, Eduardo J; García Mozo, Herminia; Domínguez Vilches, Eugenio; Galán, Carmen
2012-01-01
Traditionally phenology studies have been focused on changes through time, but there exist many instances in ecological research where it is necessary to interpolate among spatially stratified samples. The combined use of Geographical Information Systems (GIS) and Geostatistics can be an essential tool for spatial analysis in phenological studies. Geostatistics are a family of statistics that describe correlations through space/time and they can be used for both quantifying spatial correlation and interpolating unsampled points. In the present work, estimations based upon Geostatistics and GIS mapping have enabled the construction of spatial models that reflect phenological evolution of Vulpia geniculata (L.) Link throughout the study area during sampling season. Ten sampling points, scattered throughout the city and low mountains in the "Sierra de Córdoba" were chosen to carry out the weekly phenological monitoring during flowering season. The phenological data were interpolated by applying the traditional geostatitical method of Kriging, which was used to elaborate weekly estimations of V. geniculata phenology in unsampled areas. Finally, the application of Geostatistics and GIS to create phenological maps could be an essential complement in pollen aerobiological studies, given the increased interest in obtaining automatic aerobiological forecasting maps. PMID:22629169
NASA Astrophysics Data System (ADS)
Rühaak, W.; Bär, K.; Sass, I.
2012-04-01
Based on a 3D structural GOCAD model of the German federal state Hessen the subsurface temperature distribution is computed. Since subsurface temperature data for greater depth are typically sparse, two different approaches for estimating the spatial subsurface temperature distribution are tested. One approach is the numerical computation of a 3D purely conductive steady state temperature distribution. This numerical model is based on measured thermal conductivity data for all relevant geological units, together with heat flow measurements and surface temperatures. The model is calibrated using continuous temperature-logs. Here only conductive heat transfer is considered as data for convective heat transport at great depth are currently not available. The other approach is by 3D ordinary Kriging; applying a modified approach where the quality of the temperature measurements is taken into account. A difficult but important part here is to derive good variograms for the horizontal and vertical direction. The variograms give necessary information about the spatial dependence. Both approaches are compared and discussed. Differences are mainly related due to convective processes, which are reflected by the interpolation result, but not by the numerical model. Therefore, a comparison of the two results is a good way to obtain information about flow processes in such great depth. This way an improved understanding of this mid enthalpy geothermal reservoir (1000 - 6000 m) is possible. Future work will be the reduction of the small but - especially for depth up to approximately 1000 m - relevant paleoclimate signal.
GEO-EAS (GEOSTATISTICAL ENVIRONMENTAL ASSESSMENT SOFTWARE) USER'S GUIDE
The report describes how to install and use the Geo-EAS (Geostatistical Environmental Assessment Software) software package on an IBM-PC compatible computer system. A detailed example is provided showing how to use the software to conduct a geostatistical analysis of a data set. ...
The geostatistical characteristics of the Borden aquifer
NASA Astrophysics Data System (ADS)
Woodbury, Allan D.; Sudicky, E. A.
1991-04-01
A complete reexamination of Sudicky's (1986) field experiment for the geostatistical characterization of hydraulic conductivity at the Borden aquifer in Ontario, Canada is performed. The sampled data reveal that a number of outliers (low ln (K) values) are present in the data base. These low values cause difficulties in both variogram estimation and determining population statistics. The analysis shows that assuming either a normal distribution or exponential distribution for log conductivity is appropriate. The classical, Cressie/Hawkins and squared median of the absolute deviations (SMAD) estimators are used to compute experimental variograms. None of these estimators provides completely satisfactory variograms for the Borden data with the exception of the classical estimator with outliers removed from the data set. Theoretical exponential variogram parameters are determined from nonlinear (NL) estimation. Differences are obtained between NL fits and those of Sudicky (1986). For the classical-screened estimated variogram, NL fits produce an ln (K) variance of 0.24, nugget of 0.07, and integral scales of 5.1 m horizontal and 0.21 m vertical along A-A'. For B-B' these values are 0.37, 0.11, 8.3 and 0.34. The fitted parameter set for B-B' data (horizontal and vertical) is statistically different than the parameter set determined for A-A'. We also evaluate a probabilistic form of Dagan's (1982, 1987) equations relating geostatistical parameters to a tracer cloud's spreading moments. The equations are evaluated using the parameter estimates and covariances determined from line A-A' as input, with a velocity equal to 9.0 cm/day. The results are compared with actual values determined from the field test, but evaluated by both Freyberg (1986) and Rajaram and Gelhar (1988). The geostatistical parameters developed from this study produce an excellent fit to both sets of calculated plume moments when combined with Dagan's stochastic theory for predicting the spread of a
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
REGIONAL PARTICULATE MODEL - 1. MODEL DESCRIPTION AND PRELIMINARY RESULTS
The gas-phase chemistry and transport mechanisms of the Regional Acid Deposition Model have been modified to create the Regional Particulate Model, a three-dimensional Eulerian model that simulates the chemistry, transport, and dynamics of sulfuric acid aerosol resulting from pri...
A comprehensive, user-friendly geostatistical software system called GEOPACk has been developed. he purpose of this software is to make available the programs necessary to undertake a geostatistical analysis of spatially correlated data. he programs were written so that they can ...
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.
Geostatistical Interpolation of Particle-Size Curves in Heterogeneous Aquifers
NASA Astrophysics Data System (ADS)
Guadagnini, A.; Menafoglio, A.; Secchi, P.
2013-12-01
We address the problem of predicting the spatial field of particle-size curves (PSCs) from measurements associated with soil samples collected at a discrete set of locations within an aquifer system. Proper estimates of the full PSC are relevant to applications related to groundwater hydrology, soil science and geochemistry and aimed at modeling physical and chemical processes occurring in heterogeneous earth systems. Hence, we focus on providing kriging estimates of the entire PSC at unsampled locations. To this end, we treat particle-size curves as cumulative distribution functions, model their densities as functional compositional data and analyze them by embedding these into the Hilbert space of compositional functions endowed with the Aitchison geometry. On this basis, we develop a new geostatistical methodology for the analysis of spatially dependent functional compositional data. Our functional compositional kriging (FCK) approach allows providing predictions at unsampled location of the entire particle-size curve, together with a quantification of the associated uncertainty, by fully exploiting both the functional form of the data and their compositional nature. This is a key advantage of our approach with respect to traditional methodologies, which treat only a set of selected features (e.g., quantiles) of PSCs. Embedding the full PSC into a geostatistical analysis enables one to provide a complete characterization of the spatial distribution of lithotypes in a reservoir, eventually leading to improved predictions of soil hydraulic attributes through pedotransfer functions as well as of soil geochemical parameters which are relevant in sorption/desorption and cation exchange processes. We test our new method on PSCs sampled along a borehole located within an alluvial aquifer near the city of Tuebingen, Germany. The quality of FCK predictions is assessed through leave-one-out cross-validation. A comparison between hydraulic conductivity estimates obtained
Preliminary evaluation of a lake whitefish (Coregonus clupeaformis) bioenergetics model
Madenjian, Charles P.; Pothoven, Steven A.; Schneeberger, Philip J.; O'Connor, Daniel V.; Brandt, Stephen B.
2005-01-01
We conducted a preliminary evaluation of a lake whitefish (Coregonus clupeaformis) bioenergetics model by applying the model to size-at-age data for lake whitefish from northern Lake Michigan. We then compared estimates of gross growth efficiency (GGE) from our bioenergetis model with previously published estimates of GGE for bloater (C. hoyi) in Lake Michigan and for lake whitefish in Quebec. According to our model, the GGE of Lake Michigan lake whitefish decreased from 0.075 to 0.02 as age increased from 2 to 5 years. In contrast, the GGE of lake whitefish in Quebec inland waters decreased from 0.12 to 0.05 for the same ages. When our swimming-speed submodel was replaced with a submodel that had been used for lake trout (Salvelinus namaycush) in Lake Michigan and an observed predator energy density for Lake Michigan lake whitefish was employed, our model predicted that the GGE of Lake Michigan lake whitefish decreased from 0.12 to 0.04 as age increased from 2 to 5 years.
Preliminary deformation model for National Seismic Hazard map of Indonesia
Meilano, Irwan; Gunawan, Endra; Sarsito, Dina; Prijatna, Kosasih; Abidin, Hasanuddin Z.; Susilo,; Efendi, Joni
2015-04-24
Preliminary deformation model for the Indonesia’s National Seismic Hazard (NSH) map is constructed as the block rotation and strain accumulation function at the elastic half-space. Deformation due to rigid body motion is estimated by rotating six tectonic blocks in Indonesia. The interseismic deformation due to subduction is estimated by assuming coupling on subduction interface while deformation at active fault is calculated by assuming each of the fault‘s segment slips beneath a locking depth or in combination with creeping in a shallower part. This research shows that rigid body motion dominates the deformation pattern with magnitude more than 15 mm/year, except in the narrow area near subduction zones and active faults where significant deformation reach to 25 mm/year.
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
Avian demography in mosaic landscapes: modeling paradigm and preliminary results
Urban, D.L.; Shugart, H.H. Jr.
1984-01-01
We pursued a mechanistic explanation for the local extinction of some avian species from small and/or isolated patches in a habitat mosaic. A model was developed to simulate demographic processes of natality, dispersal, and mortality as these might occur in a mosaic of discrete habitat patches. These processes were adjusted to reflect life-history attributes that seem to confer sensitivity to habitat fragmentation. We used the model to explore the effects of restricted vagility and reduced natality resulting from biotic interactions for a hypothetical bird species. Restricted vagility resulted in local extinctions in small, isolated patches, but this effect was buffered by an available pool of surplus nonbreeders. Lower natality reduced this suplus so as to increase the frequency of local extinction, and allowed restricted vagility to play a more important role in affecting species distribution. Moderate levels of restricted vagility and reduced natality were synergistic in effect. In general, small patches in isolation tended to lose their populations permanently, large patches rarely suffered extinctions, but small and intermediate patches adjacent to other patches suffered extinctions and were recolonized repeatedly. The basic modeling approach can be expanded to include greater mechanistic detail, but implementation has been limited by a lack of appropriate data. Our preliminary results suggest which data are necessary to pursue a mechanistic, patch-scale understanding of species demography at the landscape scale. 17 references, 5 figures, 3 tables.
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.
Preliminary analysis of GRB060607A within the fireshell model
NASA Astrophysics Data System (ADS)
Bernardini, M. G.; Bianco, C. L.; Caito, L.; Dainotti, M. G.; Guida, R.; Ruffini, R.
2008-10-01
GRB060607A is a very distant (z = 3.082) and energetic event (Eiso~1053 erg). Its main peculiarity is that the peak of the near-infrared afterglow has been observed with the REM robotic telescope, allowing to infer the initial Lorentz gamma factor of the emitting system. We present a preliminary analysis of the spectra and light curves of GRB060607A prompt emission within the fireshell model. We show that the N(E) spectrum of the prompt emission, whose behavior is usually described as ``simple power-law,'' can also be fitted in a satisfactory way by a convolution of thermal spectra as predicted by the model we applied. The theoretical time-integrated spectrum of the prompt emission as well as the light curves in the BAT and XRT energy band are in good agreement with the observations, enforcing the plausibility of our approach. Furthermore, the initial value of Lorentz gamma factor we predict is compatible with the one deduced from the REM observations.
Sand transport on Mars: Preliminary results from models
NASA Technical Reports Server (NTRS)
Greeley, R.; Anderson, F. S.; Blumberg, D.; Lo, E.; Xu, P.; Pollack, J.
1993-01-01
Most studies of active aeolian processes on Mars have focused on dust, i.e., particles approximately 1 micron in diameter that are transported in suspension by wind. The presence of sand dunes on Mars indicates that larger grains (approximately greater than 60 microns, transported primarily in saltation) are also present. Although indirect evidence suggests that some dunes may be active, definitive evidence is lacking. Nonetheless, numerous studies demonstrate that sand is substantially easier to transport by wind than dust, and it is reasonable to infer that sand transportation in saltation occurs under present Martian conditions. In order to assess potential source regions, transportation pathways, and sites of deposition for sand on Mars, an iterative sand transport algorithm was developed that is based on the Mars General Circulation Model of Pollack et al. The results of the dust transport model are then compared with observed surface features, such as dune field locations observed on images, and surficial deposits as inferred from Viking IRTM observations. Preliminary results suggest that the north polar dune fields in the vicinity of 270 degrees W, 70 degrees N originated from weathered polar layered plains centered at 280 degrees W, 85 degrees N, and that Thaumasia Fossae, southern Hellas Planitia, and the area west of Hellespontus Montes are sand depositional sites. Examples of transportation 'corridors' include a westward pathway in the latitudinal band 35 degrees N to 45 degrees N, and a pathway southward from Solis Planum to Thaumasia Fossae, among others.
LES Modeling of Oxy-combustion of Pulverized Coal: Preliminary Study
NASA Astrophysics Data System (ADS)
Warzecha, Piotr; Boguslawski, Andrzej
2009-12-01
The paper presents preliminary results of pulverized coal combustion process modeling using Large Eddy Simulation. First the methodology for the testing of mesh resolution is presented. The combustion process was carried out using equilibrium model with single mixture fraction approach.
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.
Quinn, J.J.
1996-02-01
Geostatistical analysis of hydraulic head data is useful in producing unbiased contour plots of head estimates and relative errors. However, at most sites being characterized, monitoring wells are generally present at different densities, with clusters of wells in some areas and few wells elsewhere. The problem that arises when kriging data at different densities is in achieving adequate resolution of the grid while maintaining computational efficiency and working within software limitations. For the site considered, 113 data points were available over a 14-mi{sup 2} study area, including 57 monitoring wells within an area of concern of 1.5 mi{sup 2}. Variogram analyses of the data indicate a linear model with a negligible nugget effect. The geostatistical package used in the study allows a maximum grid of 100 by 100 cells. Two-dimensional kriging was performed for the entire study area with a 500-ft grid spacing, while the smaller zone was modeled separately with a 100-ft spacing. In this manner, grid cells for the dense area and the sparse area remained small relative to the well separation distances, and the maximum dimensions of the program were not exceeded. The spatial head results for the detailed zone were then nested into the regional output by use of a graphical, object-oriented database that performed the contouring of the geostatistical output. This study benefitted from the two-scale approach and from very fine geostatistical grid spacings relative to typical data separation distances. The combining of the sparse, regional results with those from the finer-resolution area of concern yielded contours that honored the actual data at every measurement location. The method applied in this study can also be used to generate reproducible, unbiased representations of other types of spatial data.
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.
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.
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.
The role of geostatistics in medical geology
NASA Astrophysics Data System (ADS)
Goovaerts, Pierre
2014-05-01
Since its development in the mining industry, geostatistics has emerged as the primary tool for spatial data analysis in various fields, ranging from earth and atmospheric sciences, to agriculture, soil science, remote sensing, and more recently environmental exposure assessment. In the last few years, these tools have been tailored to the field of medical geography or spatial epidemiology, which is concerned with the study of spatial patterns of disease incidence and mortality and the identification of potential 'causes' of disease, such as environmental exposure, diet and unhealthy behaviors, economic or socio-demographic factors. On the other hand, medical geology is an emerging interdisciplinary scientific field studying the relationship between natural geological factors and their effects on human and animal health. This paper provides an introduction to the field of medical geology with an overview of geostatistical methods available for the analysis of geological and health data. Key concepts are illustrated using the mapping of groundwater arsenic concentrations across eleven Michigan counties and the exploration of its relationship to the incidence of prostate cancer at the township level. Arsenic in drinking-water is a major problem and has received much attention because of the large human population exposed and the extremely high concentrations (e.g. 600 to 700 μg/L) recorded in many instances. Few studies have however assessed the risks associated with exposure to low levels of arsenic (say < 50 μg/L) most commonly found in drinking water in the United States. In the Michigan thumb region, arsenopyrite (up to 7% As by weight) has been identified in the bedrock of the Marshall Sandstone aquifer, one of the region's most productive aquifers. Epidemiologic studies have suggested a possible associationbetween exposure to inorganic arsenic and prostate cancer mortality, including a study of populations residing in Utah. The information available for the
Doctor, P.G.; Oberlander, P.L.; Rice, W.A.; Devary, J.L.; Nelson, R.W.; Tucker, P.E.
1982-09-01
The Office of Nuclear Waste Isolation (ONWI) requested Pacific Northwest Laboratory (PNL) to: (1) use geostatistical analyses to evaluate the adequacy of hydrologic data from three salt regions, each of which contains a potential nuclear waste repository site; and (2) demonstrate a methodology that allows quantification of the value of additional data collection. The three regions examined are the Paradox Basin in Utah, the Permian Basin in Texas, and the Mississippi Study Area. Additional and new data became available to ONWI during and following these analyses; therefore, this report must be considered a methodology demonstration here would apply as illustrated had the complete data sets been available. A combination of geostatistical and hydrologic analyses was used for this demonstration. Geostatistical analyses provided an optimal estimate of the potentiometric surface from the available data, a measure of the uncertainty of that estimate, and a means for selecting and evaluating the location of future data. The hydrologic analyses included the calculation of transmissivities, flow paths, travel times, and ground-water flow rates from hypothetical repository sites. Simulation techniques were used to evaluate the effect of optimally located future data on the potentiometric surface, flow lines, travel times, and flow rates. Data availability, quality, quantity, and conformance with model assumptions differed in each of the salt areas. Report highlights for the three locations are given.
Fienen, Michael N.; D'Oria, Marco; Doherty, John E.; Hunt, Randall J.
2013-01-01
The application bgaPEST is a highly parameterized inversion software package implementing the Bayesian Geostatistical Approach in a framework compatible with the parameter estimation suite PEST. Highly parameterized inversion refers to cases in which parameters are distributed in space or time and are correlated with one another. The Bayesian aspect of bgaPEST is related to Bayesian probability theory in which prior information about parameters is formally revised on the basis of the calibration dataset used for the inversion. Conceptually, this approach formalizes the conditionality of estimated parameters on the speciﬁc data and model available. The geostatistical component of the method refers to the way in which prior information about the parameters is used. A geostatistical autocorrelation function is used to enforce structure on the parameters to avoid overﬁtting and unrealistic results. Bayesian Geostatistical Approach is designed to provide the smoothest solution that is consistent with the data. Optionally, users can specify a level of ﬁt or estimate a balance between ﬁt and model complexity informed by the data. Groundwater and surface-water applications are used as examples in this text, but the possible uses of bgaPEST extend to any distributed parameter applications.
Kriging in the Shadows: Geostatistical Interpolation for Remote Sensing
NASA Technical Reports Server (NTRS)
Rossi, Richard E.; Dungan, Jennifer L.; Beck, Louisa R.
1994-01-01
It is often useful to estimate obscured or missing remotely sensed data. Traditional interpolation methods, such as nearest-neighbor or bilinear resampling, do not take full advantage of the spatial information in the image. An alternative method, a geostatistical technique known as indicator kriging, is described and demonstrated using a Landsat Thematic Mapper image in southern Chiapas, Mexico. The image was first classified into pasture and nonpasture land cover. For each pixel that was obscured by cloud or cloud shadow, the probability that it was pasture was assigned by the algorithm. An exponential omnidirectional variogram model was used to characterize the spatial continuity of the image for use in the kriging algorithm. Assuming a cutoff probability level of 50%, the error was shown to be 17% with no obvious spatial bias but with some tendency to categorize nonpasture as pasture (overestimation). While this is a promising result, the method's practical application in other missing data problems for remotely sensed images will depend on the amount and spatial pattern of the unobscured pixels and missing pixels and the success of the spatial continuity model used.
An interactive Bayesian geostatistical inverse protocol for hydraulic tomography
Fienen, Michael N.; Clemo, Tom; Kitanidis, Peter K.
2008-01-01
Hydraulic tomography is a powerful technique for characterizing heterogeneous hydrogeologic parameters. An explicit trade-off between characterization based on measurement misfit and subjective characterization using prior information is presented. We apply a Bayesian geostatistical inverse approach that is well suited to accommodate a flexible model with the level of complexity driven by the data and explicitly considering uncertainty. Prior information is incorporated through the selection of a parameter covariance model characterizing continuity and providing stability. Often, discontinuities in the parameter field, typically caused by geologic contacts between contrasting lithologic units, necessitate subdivision into zones across which there is no correlation among hydraulic parameters. We propose an interactive protocol in which zonation candidates are implied from the data and are evaluated using cross validation and expert knowledge. Uncertainty introduced by limited knowledge of dynamic regional conditions is mitigated by using drawdown rather than native head values. An adjoint state formulation of MODFLOW-2000 is used to calculate sensitivities which are used both for the solution to the inverse problem and to guide protocol decisions. The protocol is tested using synthetic two-dimensional steady state examples in which the wells are located at the edge of the region of interest.
Geostatistical Analysis of Spatio-Temporal Forest Fire Data
NASA Astrophysics Data System (ADS)
Vega Orozco, Carmen D.; Kanevski, Mikhail; Tonini, Marj; Conedera, Marc
2010-05-01
Forest fire is one of the major phenomena causing degradation of environment, landscape, natural ecosystems, human health and economy. One of the main topic in forest fire data studies deals with the detection, analysis and modelling of spatio-temporal patterns of clustering. Spatial patterns of forest fire locations, their sizes and their sequence in time are of great interest for fire prediction and for forest fire management planning and distribution in optimal way necessary resources. Currently, fires can be analyzed and monitored by using different statistical tools, for example, Ripley's k-function, fractals, Allan factor, scan statistics, etc. Some of them are adapted to temporal or spatial data and are either local or global. In the present study the main attention is paid to the application of geostatistical tools - variography and methods for the analysis of monitoring networks (MN) clustering techniques (topological, statistical and fractal measures), in order to detect and to characterize spatio-temporal forest fire patterns. The main studies performed include: a) analysis of forest fires temporal sequences; b) spatial clustering of forest fires; c) geostatistical spatial analysis of burnt areas. Variography was carried out both for temporal and spatial data. Real case study is based on the forest-fire event data from Canton of Ticino (Switzerland) for a period of 1969 to 2008. The results from temporal analysis show the presence of clustering and seasonal periodicities. Comprehensive analysis of the variograms shows an anisotropy in the direction 30° East-North where smooth changes are detected, while on the direction 30° North-West a greater variability was identified. The research was completed with an application of different MN analysis techniques including, analysis of distributions of distances between events, Morisita Index (MI), fractal dimensions (sandbox counting and box counting methods) and functional fractal dimensions, adapted and
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.
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.
Student Matriculation: A Proposal to Study a Preliminary Model.
ERIC Educational Resources Information Center
Farland, Ronnald W.; Berg, Ernest
Developed for the Board of Governors of the California Community Colleges (CCC) as part of a larger study of academic quality, this report presents a preliminary analysis of student matriculation, a guidance process which brings the student into an agreement with the college for the purpose of achieving the student's educational objectives through…
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. PMID:25461089
NASA Astrophysics Data System (ADS)
Troldborg, M.; Nowak, W.; Binning, P. J.; Bjerg, P. L.
2012-12-01
Estimates of mass discharge (mass/time) are increasingly being used when assessing risks of groundwater contamination and designing remedial systems at contaminated sites. Mass discharge estimates are, however, prone to rather large uncertainties as they integrate uncertain spatial distributions of both concentration and groundwater flow velocities. For risk assessments or any other decisions that are being based on mass discharge estimates, it is essential to address these uncertainties. We present a novel Bayesian geostatistical approach for quantifying the uncertainty of the mass discharge across a multilevel control plane. The method decouples the flow and transport simulation and has the advantage of avoiding the heavy computational burden of three-dimensional numerical flow and transport simulation coupled with geostatistical inversion. It may therefore be of practical relevance to practitioners compared to existing methods that are either too simple or computationally demanding. The method is based on conditional geostatistical simulation and accounts for i) heterogeneity of both the flow field and the concentration distribution through Bayesian geostatistics (including the uncertainty in covariance functions), ii) measurement uncertainty, and iii) uncertain source zone geometry and transport parameters. The method generates multiple equally likely realizations of the spatial flow and concentration distribution, which all honour the measured data at the control plane. The flow realizations are generated by analytical co-simulation of the hydraulic conductivity and the hydraulic gradient across the control plane. These realizations are made consistent with measurements of both hydraulic conductivity and head at the site. An analytical macro-dispersive transport solution is employed to simulate the mean concentration distribution across the control plane, and a geostatistical model of the Box-Cox transformed concentration data is used to simulate observed
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.
Separation of potential data as regional and residuals by geostatistical filtering techniques
NASA Astrophysics Data System (ADS)
Rim, H.
2009-12-01
I propose a spatial filtering scheme using factorial kriging, a kind of geostatistical filtering method in order to separate potential data as regional and residual anomalies. Spatial filtering assumes that regional anomalies have longer distance relation and residual anomalies have effected on smaller range. Gravity anomaly was decomposed into two variogram models depending on long and short effective ranges. And best-fitted variogram models produced the separated regional-residual anomalies by means of factorial kriging. This algorithm was examined with synthetic gravity data, and also applied to a real microgravity data to figure out abandoned mineshaft.
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.
A geostatistical approach to mapping site response spectral amplifications
Thompson, E.M.; Baise, L.G.; Kayen, R.E.; Tanaka, Y.; Tanaka, H.
2010-01-01
If quantitative estimates of the seismic properties do not exist at a location of interest then the site response spectral amplifications must be estimated from data collected at other locations. Currently, the most common approach employs correlations of site class with maps of surficial geology. Analogously, correlations of site class with topographic slope can be employed where the surficial geology is unknown. Our goal is to identify and validate a method to estimate site response with greater spatial resolution and accuracy for regions where additional effort is warranted. This method consists of three components: region-specific data collection, a spatial model for interpolating seismic properties, and a theoretical method for computing spectral amplifications from the interpolated seismic properties. We consider three spatial interpolation schemes: correlations with surficial geology, termed the geologic trend (GT), ordinary kriging (OK), and kriging with a trend (KT). We estimate the spectral amplifications from seismic properties using the square root of impedance method, thereby linking the frequency-dependent spectral amplifications to the depth-dependent seismic properties. Thus, the range of periods for which this method is applicable is limited by the depth of exploration. A dense survey of near-surface S-wave slowness (Ss) throughout Kobe, Japan shows that the geostatistical methods give more accurate estimates of Ss than the topographic slope and GT methods, and the OK and KT methods perform equally well. We prefer the KT model because it can be seamlessly integrated with geologic maps that cover larger regions. Empirical spectral amplifications show that the region-specific data achieve more accurate estimates of observed median short-period amplifications than the topographic slope method. ?? 2010 Elsevier B.V.
Unsupervised classification of multivariate geostatistical data: Two algorithms
NASA Astrophysics Data System (ADS)
Romary, Thomas; Ors, Fabien; Rivoirard, Jacques; Deraisme, Jacques
2015-12-01
With the increasing development of remote sensing platforms and the evolution of sampling facilities in mining and oil industry, spatial datasets are becoming increasingly large, inform a growing number of variables and cover wider and wider areas. Therefore, it is often necessary to split the domain of study to account for radically different behaviors of the natural phenomenon over the domain and to simplify the subsequent modeling step. The definition of these areas can be seen as a problem of unsupervised classification, or clustering, where we try to divide the domain into homogeneous domains with respect to the values taken by the variables in hand. The application of classical clustering methods, designed for independent observations, does not ensure the spatial coherence of the resulting classes. Image segmentation methods, based on e.g. Markov random fields, are not adapted to irregularly sampled data. Other existing approaches, based on mixtures of Gaussian random functions estimated via the expectation-maximization algorithm, are limited to reasonable sample sizes and a small number of variables. In this work, we propose two algorithms based on adaptations of classical algorithms to multivariate geostatistical data. Both algorithms are model free and can handle large volumes of multivariate, irregularly spaced data. The first one proceeds by agglomerative hierarchical clustering. The spatial coherence is ensured by a proximity condition imposed for two clusters to merge. This proximity condition relies on a graph organizing the data in the coordinates space. The hierarchical algorithm can then be seen as a graph-partitioning algorithm. Following this interpretation, a spatial version of the spectral clustering algorithm is also proposed. The performances of both algorithms are assessed on toy examples and a mining dataset.
The theoretical basis, physical structure, and preliminary evaluation of the U.S. Environmental Protection Agency's Complex Terrain Dispersion Model (CTDM) are described. CTDM is a point-source plume model designed primarily to estimate windward-side surface concentrations on dis...
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
Geostatistical borehole image-based mapping of karst-carbonate aquifer pores
Michael Sukop; Cunningham, Kevin J.
2016-01-01
Quantification of the character and spatial distribution of porosity in carbonate aquifers is important as input into computer models used in the calculation of intrinsic permeability and for next-generation, high-resolution groundwater flow simulations. Digital, optical, borehole-wall image data from three closely spaced boreholes in the karst-carbonate Biscayne aquifer in southeastern Florida are used in geostatistical experiments to assess the capabilities of various methods to create realistic two-dimensional models of vuggy megaporosity and matrix-porosity distribution in the limestone that composes the aquifer. When the borehole image data alone were used as the model training image, multiple-point geostatistics failed to detect the known spatial autocorrelation of vuggy megaporosity and matrix porosity among the three boreholes, which were only 10 m apart. Variogram analysis and subsequent Gaussian simulation produced results that showed a realistic conceptualization of horizontal continuity of strata dominated by vuggy megaporosity and matrix porosity among the three boreholes.
Geostatistical Borehole Image-Based Mapping of Karst-Carbonate Aquifer Pores.
Sukop, Michael C; Cunningham, Kevin J
2016-03-01
Quantification of the character and spatial distribution of porosity in carbonate aquifers is important as input into computer models used in the calculation of intrinsic permeability and for next-generation, high-resolution groundwater flow simulations. Digital, optical, borehole-wall image data from three closely spaced boreholes in the karst-carbonate Biscayne aquifer in southeastern Florida are used in geostatistical experiments to assess the capabilities of various methods to create realistic two-dimensional models of vuggy megaporosity and matrix-porosity distribution in the limestone that composes the aquifer. When the borehole image data alone were used as the model training image, multiple-point geostatistics failed to detect the known spatial autocorrelation of vuggy megaporosity and matrix porosity among the three boreholes, which were only 10 m apart. Variogram analysis and subsequent Gaussian simulation produced results that showed a realistic conceptualization of horizontal continuity of strata dominated by vuggy megaporosity and matrix porosity among the three boreholes. PMID:26174850
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
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.
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.
A system level model for preliminary design of a space propulsion solid rocket motor
NASA Astrophysics Data System (ADS)
Schumacher, Daniel M.
Preliminary design of space propulsion solid rocket motors entails a combination of components and subsystems. Expert design tools exist to find near optimal performance of subsystems and components. Conversely, there is no system level preliminary design process for space propulsion solid rocket motors that is capable of synthesizing customer requirements into a high utility design for the customer. The preliminary design process for space propulsion solid rocket motors typically builds on existing designs and pursues feasible rather than the most favorable design. Classical optimization is an extremely challenging method when dealing with the complex behavior of an integrated system. The complexity and combinations of system configurations make the number of the design parameters that are traded off unreasonable when manual techniques are used. Existing multi-disciplinary optimization approaches generally address estimating ratios and correlations rather than utilizing mathematical models. The developed system level model utilizes the Genetic Algorithm to perform the necessary population searches to efficiently replace the human iterations required during a typical solid rocket motor preliminary design. This research augments, automates, and increases the fidelity of the existing preliminary design process for space propulsion solid rocket motors. The system level aspect of this preliminary design process, and the ability to synthesize space propulsion solid rocket motor requirements into a near optimal design, is achievable. The process of developing the motor performance estimate and the system level model of a space propulsion solid rocket motor is described in detail. The results of this research indicate that the model is valid for use and able to manage a very large number of variable inputs and constraints towards the pursuit of the best possible design.
Environment modelling in near Earth space: Preliminary LDEF results
NASA Technical Reports Server (NTRS)
Coombs, C. R.; Atkinson, D. R.; Wagner, J. D.; Crowell, L. B.; Allbrooks, M.; Watts, A. J.
1992-01-01
Hypervelocity impacts by space debris cause not only local cratering or penetrations, but also cause large areas of damage in coated, painted or laminated surfaces. Features examined in these analyses display interesting morphological characteristics, commonly exhibiting a concentric ringed appearance. Virtually all features greater than 0.2 mm in diameter possess a spall zone in which all of the paint was removed from the aluminum surface. These spall zones vary in size from approximately 2 - 5 crater diameters. The actual craters in the aluminum substrate vary from central pits without raised rims, to morphologies more typical of craters formed in aluminum under hypervelocity laboratory conditions for the larger features. Most features also possess what is referred to as a 'shock zone' as well. These zones vary in size from approximately 1 - 20 crater diameters. In most cases, only the outer-most layer of paint was affected by this impact related phenomenon. Several impacts possess ridge-like structures encircling the area in which this outer-most paint layer was removed. In many ways, such features resemble the lunar impact basins, but on an extremely reduced scale. Overall, there were no noticeable penetrations, bulges or spallation features on the backside of the tray. On Row 12, approximately 85 degrees from the leading edge (RAM direction), there was approximately one impact per 15 cm(exp 2). On the trailing edge, there was approximately one impact per 72 cm(exp 2). Currently, craters on four aluminum experiment trays from Bay E09, directly on the leading edge are being measured and analyzed. Preliminary results have produced more than 2200 craters on approximately 1500 cm(exp 2) - or approximately 1 impact per 0.7 cm(exp 2).
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.
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 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,…
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.
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
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…
Wave-current interactions: model development and preliminary results
NASA Astrophysics Data System (ADS)
Mayet, Clement; Lyard, Florent; Ardhuin, Fabrice
2013-04-01
The coastal area concentrates many uses that require integrated management based on diagnostic and predictive tools to understand and anticipate the future of pollution from land or sea, and learn more about natural hazards at sea or activity on the coast. The realistic modelling of coastal hydrodynamics needs to take into account various processes which interact, including tides, surges, and sea state (Wolf [2008]). These processes act at different spatial scales. Unstructured-grid models have shown the ability to satisfy these needs, given that a good mesh resolution criterion is used. We worked on adding a sea state forcing in a hydrodynamic circulation model. The sea state model is the unstructured version of WAVEWATCH III c (Tolman [2008]) (which version is developed at IFREMER, Brest (Ardhuin et al. [2010]) ), and the hydrodynamic model is the 2D barotropic module of the unstructured-grid finite element model T-UGOm (Le Bars et al. [2010]). We chose to use the radiation stress approach (Longuet-Higgins and Stewart [1964]) to represent the effect of surface waves (wind waves and swell) in the barotropic model, as previously done by Mastenbroek et al. [1993]and others. We present here some validation of the model against academic cases : a 2D plane beach (Haas and Warner [2009]) and a simple bathymetric step with analytic solution for waves (Ardhuin et al. [2008]). In a second part we present realistic application in the Ushant Sea during extreme event. References Ardhuin, F., N. Rascle, and K. Belibassakis, Explicit wave-averaged primitive equations using a generalized Lagrangian mean, Ocean Modelling, 20 (1), 35-60, doi:10.1016/j.ocemod.2007.07.001, 2008. Ardhuin, F., et al., Semiempirical Dissipation Source Functions for Ocean Waves. Part I: Definition, Calibration, and Validation, J. Phys. Oceanogr., 40 (9), 1917-1941, doi:10.1175/2010JPO4324.1, 2010. Haas, K. A., and J. C. Warner, Comparing a quasi-3D to a full 3D nearshore circulation model: SHORECIRC and
Preliminary numerical analysis of improved gas chromatograph model
NASA Technical Reports Server (NTRS)
Woodrow, P. T.
1973-01-01
A mathematical model for the gas chromatograph was developed which incorporates the heretofore neglected transport mechanisms of intraparticle diffusion and rates of adsorption. Because a closed-form analytical solution to the model does not appear realizable, techniques for the numerical solution of the model equations are being investigated. Criteria were developed for using a finite terminal boundary condition in place of an infinite boundary condition used in analytical solution techniques. The class of weighted residual methods known as orthogonal collocation is presently being investigated and appears promising.
Preliminary results of GODIVA-IV prompt burst modeling
Kimpland, R.
1996-06-01
The dynamic computer model developed to simulate GODIVA-IV prompt bursts adequately predicts the magnitude of power bursts. Also, it demonstrates the characteristic features of prompt bursts in metal assemblies, such as the change in shape of power pulses and the ringing of fuel surfaces at the onset of inertial effects. The model will be used to test more sophisticated reactivity feedback coefficients and neutronic-hydrodynamic coupling schemes. It will also be used for a more detailed analysis of inertial effects.
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.
NASA Astrophysics Data System (ADS)
Tang, Yunwei; Atkinson, Peter M.; Zhang, Jingxiong
2015-03-01
A cross-scale data integration method was developed and tested based on the theory of geostatistics and multiple-point geostatistics (MPG). The goal was to downscale remotely sensed images while retaining spatial structure by integrating images at different spatial resolutions. During the process of downscaling, a rich spatial correlation model in the form of a training image was incorporated to facilitate reproduction of similar local patterns in the simulated images. Area-to-point cokriging (ATPCK) was used as locally varying mean (LVM) (i.e., soft data) to deal with the change of support problem (COSP) for cross-scale integration, which MPG cannot achieve alone. Several pairs of spectral bands of remotely sensed images were tested for integration within different cross-scale case studies. The experiment shows that MPG can restore the spatial structure of the image at a fine spatial resolution given the training image and conditioning data. The super-resolution image can be predicted using the proposed method, which cannot be realised using most data integration methods. The results show that ATPCK-MPG approach can achieve greater accuracy than methods which do not account for the change of support issue.
A preliminary geodetic data model for geographic information systems
NASA Astrophysics Data System (ADS)
Kelly, K. M.
2009-12-01
Our ability to gather and assimilate integrated data collections from multiple disciplines is important for earth system studies. Moreover, geosciences data collection has increased dramatically, with pervasive networks of observational stations on the ground, in the oceans, in the atmosphere and in space. Contemporary geodetic observations from several space and terrestrial technologies contribute to our knowledge of earth system processes and thus are a valuable source of high accuracy information for many global change studies. Assimilation of these geodetic observations and numerical models into models of weather, climate, oceans, hydrology, ice, and solid Earth processes is an important contribution geodesists can make to the earth science community. Clearly, the geodetic observations and models are fundamental to these contributions. ESRI wishes to provide leadership in the geodetic community to collaboratively build an open, freely available content specification that can be used by anyone to structure and manage geodetic data. This Geodetic Data Model will provide important context for all geographic information. The production of a task-specific geodetic data model involves several steps. The goal of the data model is to provide useful data structures and best practices for each step, making it easier for geodesists to organize their data and metadata in a way that will be useful in their data analyses and to their customers. Built on concepts from the successful Arc Marine data model, we introduce common geodetic data types and summarize the main thematic layers of the Geodetic Data Model. These provide a general framework for envisioning the core feature classes required to represent geodetic data in a geographic information system. Like Arc Marine, the framework is generic to allow users to build workflow or product specific geodetic data models tailored to the specific task(s) at hand. This approach allows integration of the data with other existing
The NASA/GISS Mars general circulation model: Preliminary experiments
NASA Technical Reports Server (NTRS)
Allison, Michael; Chandler, M. A.; Delgenio, A. D.; Lacis, A.; Rind, D.; Rossow, W. B.; Travis, L. D.; Zhou, W.
1993-01-01
The NASA/GISS Mars General Circulation Model (GCM) is an adapted version of the GISS Global Climate/Middle Atmosphere Model, specifically developed for the diagnostic validation and objective analysis of measured atmospheric temperatures from the Mars Observer Pressure Modulator Infrared Radiometer (PMIRR) experiment. The GISS Mars GCM has 23 vertical layers extending from the surface to approximately 80 km altitude, representing a vertical resolution of about 0.3 scale heights. The primitive (vertically hydrostatic) equations are solved in finite difference form on the Krakawa B grid, with a horizontal resolution of 8 deg x 10 deg (latitude-longitude). The model includes a diurnal solar cycle, heat transport within a two-layer ground, and a high-order 'slopes-scheme' for the advection of heat in the upper atmosphere. The radiative transfer scheme is based on the correlated k distribution method for the treatment of nongray gaseous absorption thermal emission, and multiple scattering, including options for suspended dust. A special feature of the model of particular importance for Mars is a parameterization of gravity-wave-induced drag incorporating orographic forcing, wind shear, convection, and radiative damping. The implementation of the GISS Mars model includes global maps of topography, roughness, and albedo.
Preliminary results of a three-dimensional radiative transfer model
O`Hirok, W.
1995-09-01
Clouds act as the primary modulator of the Earth`s radiation at the top of the atmosphere, within the atmospheric column, and at the Earth`s surface. They interact with both shortwave and longwave radiation, but it is primarily in the case of shortwave where most of the uncertainty lies because of the difficulties in treating scattered solar radiation. To understand cloud-radiative interactions, radiative transfer models portray clouds as plane-parallel homogeneous entities to ease the computational physics. Unfortunately, clouds are far from being homogeneous, and large differences between measurement and theory point to a stronger need to understand and model cloud macrophysical properties. In an attempt to better comprehend the role of cloud morphology on the 3-dimensional radiation field, a Monte Carlo model has been developed. This model can simulate broadband shortwave radiation fluxes while incorporating all of the major atmospheric constituents. The model is used to investigate the cloud absorption anomaly where cloud absorption measurements exceed theoretical estimates and to examine the efficacy of ERBE measurements and cloud field experiments. 3 figs.
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.
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. PMID:22328661
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
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.
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 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 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...
A preliminary study to Assess Model Uncertainties in Fluid Flows
Marc Oliver Delchini; Jean C. Ragusa
2009-09-01
The goal of this study is to assess the impact of various flow models for a simplified primary coolant loop of a light water nuclear reactor. The various fluid flow models are based on the Euler equations with an additional friction term, gravity term, momentum source, and energy source. The geometric model is purposefully chosen simple and consists of a one-dimensional (1D) loop system in order to focus the study on the validity of various fluid flow approximations. The 1D loop system is represented by a rectangle; the fluid is heated up along one of the vertical legs and cooled down along the opposite leg. A pressurizer and a pump are included in the horizontal legs. The amount of energy transferred and removed from the system is equal in absolute value along the two vertical legs. The various fluid flow approximations are compressible vs. incompressible, and complete momentum equation vs. Darcy’s approximation. The ultimate goal is to compute the fluid flow models’ uncertainties and, if possible, to generate validity ranges for these models when applied to reactor analysis. We also limit this study to single phase flows with low-Mach numbers. As a result, sound waves carry a very small amount of energy in this particular case. A standard finite volume method is used for the spatial discretization of the system.
Preliminary Shuttle Space Suit Shielding Model. Chapter 9
NASA Technical Reports Server (NTRS)
Anderson, Brooke M.; Nealy, J. E.; Qualls, G. D.; Staritz, P. J.; Wilson, J. W.; Kim, M.-H. Y.; Cucinotta, F. A.; Atwell, W.; DeAngelis, G.; Ware, J.; Persans, A. E.
2003-01-01
There are two space suits in current usage within the space program: EMU [2] and Orlan-M Space Suit . The Shuttle space suit components are discussed elsewhere [2,5,6] and serve as a guide to development of the current model. The present model is somewhat simplified in details which are considered to be second order in their effects on exposures. A more systematic approach is ongoing on a part-by-part basis with the most important ones in terms of exposure contributions being addressed first with detailed studies of the relatively thin space suit fabric as the first example . Additional studies to validate the model of the head coverings (bubble, helmet, visors.. .) will be undertaken in the near future. The purpose of this paper is to present the details of the model as it is now and to examine its impact on estimates of astronaut health risks. In this respect, the nonuniform distribution of mass of the space suit provides increased shielding in some directions and some organs. These effects can be most important in terms of health risks and especially critical to evaluation of potential early radiation effects .
Preliminary 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…
Measuring Experiential Avoidance: A Preliminary Test of a Working Model
ERIC Educational Resources Information Center
Hayes, Steven C.; Strosahl, Kirk; Wilson, Kelly G.; Bissett, Richard T.; Pistorello, Jacqueline; Toarmino, Dosheen; Polusny, Melissa A.; Dykstra, Thane A.; Batten, Sonja V.; Bergan, John; Stewart, Sherry H.; Zvolensky, Michael J.; Eifert, Georg H.; Bond, Frank W.; Forsyth, John P.; Karekla, Maria; Mccurry, Susan M.
2004-01-01
The present study describes the development of a short, general measure of experiential avoidance, based on a specific theoretical approach to this process. A theoretically driven iterative exploratory analysis using structural equation modeling on data from a clinical sample yielded a single factor comprising 9 items. A fully confirmatory factor…
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
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.
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.
NASA Astrophysics Data System (ADS)
Lee, J.; Kitanidis, P. K.
2014-07-01
The stochastic geostatistical inversion approach is widely used in subsurface inverse problems to estimate unknown parameter fields and corresponding uncertainty from noisy observations. However, the approach requires a large number of forward model runs to determine the Jacobian or sensitivity matrix, thus the computational and storage costs become prohibitive when the number of unknowns, m, and the number of observations, n increase. To overcome this challenge in large-scale geostatistical inversion, the Principal Component Geostatistical Approach (PCGA) has recently been developed as a "matrix-free" geostatistical inversion strategy that avoids the direct evaluation of the Jacobian matrix through the principal components (low-rank approximation) of the prior covariance and the drift matrix with a finite difference approximation. As a result, the proposed method requires about K runs of the forward problem in each iteration independently of m and n, where K is the number of principal components and can be much less than m and n for large-scale inverse problems. Furthermore, the PCGA is easily adaptable to different forward simulation models and various data types for which the adjoint-state method may not be implemented suitably. In this paper, we apply the PCGA to representative subsurface inverse problems to illustrate its efficiency and scalability. The low-rank approximation of the large-dimensional dense prior covariance matrix is computed through a randomized eigen decomposition. A hydraulic tomography problem in which the number of observations is typically large is investigated first to validate the accuracy of the PCGA compared with the conventional geostatistical approach. Then the method is applied to a large-scale hydraulic tomography with 3 million unknowns and it is shown that underlying subsurface structures are characterized successfully through an inversion that involves an affordable number of forward simulation runs. Lastly, we present a joint
NASA Astrophysics Data System (ADS)
Bourgine, Bernard; Lasseur, Éric; Leynet, Aurélien; Badinier, Guillaume; Ortega, Carole; Issautier, Benoit; Bouchet, Valentin
2015-04-01
In 2012 BRGM launched an extensive program to build the new French Geological Reference platform (RGF). Among the objectives of this program is to provide the public with validated, reliable and 3D-consistent geological data, with estimation of uncertainty. Approx. 100,000 boreholes over the whole French national territory provide a preliminary interpretation in terms of depths of main geological interfaces, but with an unchecked, unknown and often low reliability. The aim of this paper is to present the procedure that has been tested on two areas in France, in order to validate (or not) these boreholes, with the aim of being generalized as much as possible to the nearly 100,000 boreholes waiting for validation. The approach is based on the following steps, and includes the management of uncertainty at different steps: (a) Selection of a loose network of boreholes owning a logging or coring information enabling a reliable interpretation. This first interpretation is based on the correlation of well log data and allows defining 3D sequence stratigraphic framework identifying isochronous surfaces. A litho-stratigraphic interpretation is also performed. Be "A" the collection of all boreholes used for this step (typically 3 % of the total number of holes to be validated) and "B" the other boreholes to validate, (b) Geostatistical analysis of characteristic geological interfaces. The analysis is carried out firstly on the "A" type data (to validate the variogram model), then on the "B" type data and at last on "B" knowing "A". It is based on cross-validation tests and evaluation of the uncertainty associated to each geological interface. In this step, we take into account inequality constraints provided by boreholes that do not intersect all interfaces, as well as the "litho-stratigraphic pile" defining the formations and their relationships (depositing surfaces or erosion). The goal is to identify quickly and semi-automatically potential errors among the data, up to
Preliminary Environmental Flow and Transport Modeling at the INEEL
Magnuson, Swen O; Mccarthy, James Michael; Navratil, James Dale
1999-09-01
The Idaho National Engineering and Environmental Laboratory (INEEL) is located in southeastern Idaho in the USA. The primary mission since the laboratory was founded in 1949 has been nuclear reactor research. Fifty-two reactors have been built and operated on the INEEL. Other principal activities at the laboratory have been reprocessing of spent nuclear fuel. Low-level radioactive waste generated on site and mixed and transuranic waste from the Rocky Flats plutonium processing facility in Colorado has been disposed on the INEEL at the Radioactive Waste Management Complex (RWMC). Waste disposal at the RWMC began in 1952 with shallow land burial in pits and trenches. The INEEL was placed on the National Priorities List (NPL) in 1989. The resulting environmental assessments of the potential negative health impacts of disposed waste at the RWMC have required the use of predictive numerical simulations. A petroleum reservoir simulator called TETRAD was modified for use in simulating environmental flow and transport. Use of this code has allowed the complex subsurface stratigraphy to be simulated, including an extensive region of unsaturated fractured basalt. Dual continual simulation approaches have been used to assess combined aqueous- and gaseous-phase transport of volatile organic compounds as well as dissolved-phase transport of radionuclides. Calibration of the simulator to available monitoring data has increased the confidence in the simulator results to the point where the model sensitivities are being used to direct additional characterization efforts. Eventually, as the model calibration improves and confidence in the model predictions increases, the simulator will be used as a decision tool for selecting remedial alternatives for the wastes buried at the RWMC. An overview of the overall program including a summary of laboratory actinide migration studies will be presented.
Preliminary Environmental Flow and Transport Modeling at the INEEL
J. D. Navratil; J. M. McCarthy; S. O. Magnuson
1999-09-26
The Idaho National Engineering and Environmental Laboratory (INEEL) is located in southeastern Idaho in the USA. The primary mission since the laboratory was founded in 1949 has been nuclear reactor research. Fifty-two reactors have been built and operated on the INEEL. Other principal activities at the laboratory have been reprocessing of spent nuclear fuel. Low-level radioactive waste generated on site and mixed and transuranic waste from the Rocky Flats plutonium processing facility in Colorado has been disposed on the INEEL at the Radioactive Waste Management Complex (RWMC). Waste disposal at the RWMC began in 1952 with shallow land burial in pits and trenches. The INEEL was placed on the National Priorities List (NPL) in 1989. The resulting environmental assessments of the potential negative health impacts of disposed waste at the RWMC have required the use of predictive numerical simulations. A petroleum reservoir simulator called TETRAD was modified for use in simulating environmental flow and transport. Use of this code has allowed the complex subsurface stratigraphy to be simulated, including an extensive region of unsaturated fractured basalt. Dual continual simulation approaches have been used to assess combined aqueous- and gaseous-phase transport of volatile organic compounds as well as dissolved-phase transport of radionuclides. Calibration of the simulator to available monitoring data has increased the confidence in the simulator results to the point where the model sensitivities are being used to direct additional characterization efforts. Eventually, as the model calibration improves and confidence in the model predictions increases, the simulator will be used as a decision tool for selecting remedial alternatives for the wastes buried at the RWMC. An overview of the overall program including a summary of laboratory actinide migration studies will be presented.
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
Preliminary Fracture Model for The SE Geysers Geothermal Reservoir
NASA Astrophysics Data System (ADS)
Furrey, L.; Furrey, L.; Wagoner, J.; Elkibbi, M.; Hutchings, L. J.
2001-12-01
In this study we combine interpretation of steam entry points, seismicity, shear-wave splitting, geology, and rock physics to develop a fracture model for the Southeast Geysers reservoir in an attempt to improve understanding of the permeability and steam flow within the reservoir. The Geysers is a dry steam field located approximately 140 km NNW of San Francisco, in Sonoma and Lake Counties in northern California. We developed this model by utilizing three-dimensional coordinates of wellbores and observations of steam entries encountered during drilling in conjunction with the locations of microearthquakes, the orientations of fractures from shear-wave splitting, geologic interpretation, and the result of rock physics interpretations. We utilize earthVision5.1TM visualization software in analyzing this data. We are interested in analyzing the fault, fractures, or fracture sets that appear to have the major control over fluid flow at reservoir depths. Faults offsetting the reservoir graywacke and felsite are generally identified by indirect methods. Fault detection within the reservoir rocks is difficult because the geology is relatively homogeneous and lacks marker horizons. Most high-angle faults mapped at the surface are truncated above the reservoir by thrust faults, and do not project to zones of high permeability within the reservoir. Thus, we utilize steam entry points along with geological formation topography to assist in the identification of faults at depth.
Preliminary conceptual model for mineral evolution in Yucca Mountain
Duffy, C.J.
1993-12-01
A model is presented for mineral alteration in Yucca Mountain, Nevada, that suggests that the mineral transformations observed there are primarily controlled by the activity of aqueous silica. The rate of these reactions is related to the rate of evolution of the metastable silica polymorphs opal-CT and cristobalite assuming that a{sub SiO{sub 2(aq)}} is fixed at the equilibrium solubility of the most soluble silica polymorph present. The rate equations accurately predict the present depths of disappearance of opal-CT and cristobalite. The rate equations have also been used to predict the extent of future mineral alteration that may result from emplacement of a high-level nuclear waste repository in Yucca Mountain. Relatively small changes in mineralogy are predicted, but these predictions are based on the assumption that emplacement of a repository would not increase the pH of water in Yucca Mountain nor increase its carbonate content. Such changes may significantly increase mineral alteration. Some of the reactions currently occurring in Yucca Mountain consume H{sup +} and CO{sub 3}{sup 2{minus}}. Combining reaction rate models for these reactions with water chemistry data may make it possible to estimate water flux through the basal vitrophyre of the Topopah Spring Member and to help confirm the direction and rate of flow of groundwater in Yucca Mountain.
Preliminary 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.
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.
Dynamic density functional theory of solid tumor growth: Preliminary models.
Chauviere, Arnaud; Hatzikirou, Haralambos; Kevrekidis, Ioannis G; Lowengrub, John S; Cristini, Vittorio
2012-03-01
Cancer is a disease that can be seen as a complex system whose dynamics and growth result from nonlinear processes coupled across wide ranges of spatio-temporal scales. The current mathematical modeling literature addresses issues at various scales but the development of theoretical methodologies capable of bridging gaps across scales needs further study. We present a new theoretical framework based on Dynamic Density Functional Theory (DDFT) extended, for the first time, to the dynamics of living tissues by accounting for cell density correlations, different cell types, phenotypes and cell birth/death processes, in order to provide a biophysically consistent description of processes across the scales. We present an application of this approach to tumor growth. PMID:22489279
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.
Preliminary empirical model of inner boundary of ion plasma sheet
NASA Astrophysics Data System (ADS)
Cao, J. B.; Zhang, D.; Reme, H.; Dandouras, I.; Sauvaud, J. A.; Fu, H. S.; Wei, X. H.
2015-09-01
The penetration of the plasma sheet into the inner magnetosphere is important to both ring current formation and spacecraft charging at geosynchronous orbit. This paper, using hot ion data recorded by HIA of TC-1/DSP, establishes an empirical model of the inner boundary of ion plasma sheet (IBIPS) on the near equatorial plane. All IBIPS are located inside geocentric radial distance of 9 RE. We divided local times (LT) into eight local time bins and found that during quiet times (Kp ⩽ 2-), the IBIPS is closest to the Earth on the pre-midnight side (LT = 1930-2130) and farthest on the dawn side (LT = 0430-0730), which differs from previous spiral models. The geocentric radius of IBIPS in each local time bin can be described by a linear fitting function: Rps = A + Bkp · Kp. The changing rate Bkp of the radius of IBIPS relative to Kp index on the midnight side (LT = 2230-0130) and post-night side (LT = 0130-0430) are the two largest (0.66 and 0.67), indicating that the IBIPS on the night side (LT = 2230-0430) moves fastest when Kp changes. Since the IBIPSs in different local times bins have different changing rates, both the size and shape of IBIPS change when Kp varies. The correlation coefficients between the radius of IBIPS and the instantaneous Kp increase with the increase of ΔT (the time difference between IBIPS crossing time and preceding Kp interval), which suggests that with the increase of ΔT, the radius of IBIPS is more and more controlled by instantaneous Kp, and the influence of preceding Kp becomes weaker. The response time of IBIPS to Kp is between 80 and 95 min. When ΔT > 95 min, the correlation coefficient basically keeps unchanged and only has a weak increase, suggesting that the IBIPS is mainly determined by the convection electric field represented by instantaneous Kp.
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
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. PMID:25635771
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. PMID:26473728