Model-based Geostatistics

Diggle, Peter J.; Ribeiro, Paulo Justiniano

doi:10.1007/978-0-387-48536-2

Cited by 980 publications

(538 citation statements)

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“…Geostatistics is used for estimation and prediction of a spatially continuous phenomenon, using data obtained at a limited number of spatial locations (Diggle & Ribeiro, 2007). The geostatistical study of lineaments has been used widely in many researches (Katsuaki & Yuichi 2006;Chiles, 1998;Koike & Komorida, 2001).…”

Section: Geostatisticsmentioning

confidence: 99%

Análisis de fracturas geológicas extraídas por teledetección en imágenes Landsat TM, ejemplo de la zona de Imilchil-Tounfite (Alto Atlas central, Marruecos)

et al. 2016

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The use of remote sensing, in this research, can be summarized in mapping and statistical studies of lineaments on the satellites images of the Jurassic outcrops in the Imilchil-Tounfite area, Central High Atlas of Morocco. This is to apply various manual techniques for extracting lineaments from Landsat TM image. Analytical techniques used in this work are: the principal component analysis (PCA) applied to selective bands of the visible and infrared, which allows creating new images with better visual interpretation. Directional filters N0°, N45°, N90°, and N135° with a 5×5 matrix were used to enhance lineaments in the corresponding perpendicular directions, and therefore to obtain a good discrimination of those structures. Preliminary results highlight a dominant geological fracturing trending ENE/WSW with 52% of the total lineaments, a second fracture trending is WNW/ESE at 23%, a third fracture series trending NE/SW with 20% and finally, a minor series of fractures trending NW/SE with 5% of the total lineaments. Distribution and statistical relationship, between fractures and the affected surface on the one hand and the fracture length on the other hand, shows a network of well-structured fractures. The final lineament map constitutes a contribution to complete the geology and assisting the mining and hydrogeological prospection, in the Imilchil-Tounfite area.

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Section: Geostatisticsmentioning

confidence: 99%

Análisis de fracturas geológicas extraídas por teledetección en imágenes Landsat TM, ejemplo de la zona de Imilchil-Tounfite (Alto Atlas central, Marruecos)

et al. 2016

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“…Gaussian process regression is also known as kriging in the geostatistical literature or as multivariate-Gauss model for the log conductivity, with model-based covariance function and ML covariance parameters [Matern, 1986;Handcock and Stein, 1993;Diggle and Ribeiro, 2007;Marchant and Lark, 2007;Nowak et al, 2010]. A detailed description of GPR can be found in Rasmussen and Williams [2006] and Elsheikh et al [2012.…”

Section: Gaussian Process Regressionmentioning

confidence: 99%

Nested sampling algorithm for subsurface flow model selection, uncertainty quantification, and nonlinear calibration

2013

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[1] Calibration of subsurface flow models is an essential step for managing ground water aquifers, designing of contaminant remediation plans, and maximizing recovery from hydrocarbon reservoirs. We investigate an efficient sampling algorithm known as nested sampling (NS), which can simultaneously sample the posterior distribution for uncertainty quantification, and estimate the Bayesian evidence for model selection. Model selection statistics, such as the Bayesian evidence, are needed to choose or assign different weights to different models of different levels of complexities. In this work, we report the first successful application of nested sampling for calibration of several nonlinear subsurface flow problems. The estimated Bayesian evidence by the NS algorithm is used to weight different parameterizations of the subsurface flow models (prior model selection). The results of the numerical evaluation implicitly enforced Occam's razor where simpler models with fewer number of parameters are favored over complex models. The proper level of model complexity was automatically determined based on the information content of the calibration data and the data mismatch of the calibrated model.

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“…A simpler, approximate procedure consists of fitting a geostatistical linear Gaussian model to empirical-logit-transformed prevalences. Table 1 summarizes the common functionalities required for prevalence mapping that are available in PrevMap and the existing packages geoR (Diggle and Ribeiro 2007;Ribeiro and Diggle 2001), geoRglm (Christensen and Ribeiro 2002), geostatsp (Brown 2015), geoBayes and spBayes (Finley, Banerjee, and Carlin 2007;Finley, Banerjee, and Gelfand 2015). Overall, PrevMap provides the most extensive functionality.…”

Section: Introductionmentioning

confidence: 99%

PrevMap: An R Package for Prevalence Mapping

Giorgi¹,

Diggle²

2017

J. Stat. Soft.

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In this paper we introduce a new R package, PrevMap, for the analysis of spatially referenced prevalence data, including both classical maximum likelihood and Bayesian approaches to parameter estimation and plug-in or Bayesian prediction. More specifically, the new package implements fitting of geostatistical models for binomial data, based on two distinct approaches. The first approach uses a generalized linear mixed model with logistic link function, binomial error distribution and a Gaussian spatial process as a stochastic component in the linear predictor. A simpler, but approximate, alternative approach consists of fitting a linear Gaussian model to empirical-logit-transformed data. The package also includes implementations of convolution-based low-rank approximations to the Gaussian spatial process to enable computationally efficient analysis of large spatial datasets. We illustrate the use of the package through the analysis of Loa loa prevalence data from Cameroon and Nigeria. We illustrate the use of the low rank approximation using a simulated geostatistical dataset.

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Model-based Geostatistics

Cited by 980 publications

References 0 publications

Análisis de fracturas geológicas extraídas por teledetección en imágenes Landsat TM, ejemplo de la zona de Imilchil-Tounfite (Alto Atlas central, Marruecos)

Análisis de fracturas geológicas extraídas por teledetección en imágenes Landsat TM, ejemplo de la zona de Imilchil-Tounfite (Alto Atlas central, Marruecos)

Nested sampling algorithm for subsurface flow model selection, uncertainty quantification, and nonlinear calibration

PrevMap: An R Package for Prevalence Mapping

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