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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.


Part I - Concepts
3-6Hiking in the Sierra Nevada
7-28Spatial estimation based on random function theory
29-48Universal kriging with training images
49-58Stochastic simulations based on random function theory
59-74Stochastic simulation without random function theory
75-86Returning to the Sierra Nevada
Part II - Methods
91-154The algorithmic building blocks
155-172Multiple-point geostatistics blocks
173-182Markov random fields
183-198Nonstationary modeling with training images
199-220Multivariate modeling with training images
221-238Training image construction
239-258Validation and quality control
259-294Inverse modeling with training images
Part III - Applications
303-328Reservoir forecasting - the West Coast of Africa reservoir
329-344Geological resources modeling in mining
345-360Climate modeling application - the case of the Murray-Darling Basin

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