Multiple-point Geostatistics: Stochastic Modeling with Training Images
This book provides a comprehensive introduction to multiple-point geostatistics, where spatial continuity is described using training images. Multiple-point geostatistics aims at bridging the gap between physical modelling/realism and spatio-temporal stochastic modelling. The book provides an overview of this new field in three parts. Part I presents a conceptual comparison between traditional random function theory and stochastic modelling based on training images, where random function theory is not always used. Part II covers in detail various algorithms and methodologies starting from basic building blocks in statistical science and computer science. Concepts such as non-stationary and multi-variate modeling, consistency between data and model, the construction of training images and inverse modelling are treated. Part III covers three example application areas, namely, reservoir modelling, mineral resources modelling and climate model downscaling. This book will be an invaluable reference for students, researchers and practitioners of all areas of the Earth Sciences where forecasting based on spatio-temporal data is performed.
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algorithm algorithmic building blocks applications approach auxiliary variable Caers categorical variables Chapter complex Computers conditioning data considered control map convolution corresponding covariance data events database defined direct sampling distance distribution domain downscaling ensemble Kalman filter Equation estimation example facies Figure filter formulation Gaussian geological model geostatistical modeling global grid nodes Hausdorff distance hence histogram hydraulic conductivity inverse problems John Wiley kriging with training Markov Markov random fields Mathematical Geology Mathematical Geosciences methods modeling with training MPS realizations multi-Gaussian Multiple-point Geostatistics multivariate neighborhood nonstationary obtained parameterization parameters patch patterns posterior prior probability properties proportions random fields random function theory relationship rely represented reservoir models resulting scatterplots Section semivariogram sequential simulation similar simulation grid SNESIM spatial specific stationary stochastic stochastic simulation structures template texture synthesis tion training image transformation tree trend types uncertainty univariate universal kriging values variogram Walker Lake Wiley & Sons