Multiple-point geostatistics : stochastic modeling with training images / Gregoire Mariethoz and Jef Caers.

Includes index.

Part I Concepts :
I.1 Hiking in the Sierra Nevada,
I.2 Spatial estimation based on random function theory,
I.3 Universal kriging with training images,
I.4 Stochastic simulations based on random function theory,
I.5 Stochastic simulation without random function theory,
I.6 Returning to the Sierra Nevada,

Part II Methods :
II.1 Introduction,
II.2 The algorithmic building blocks,
II.3 Multiple-point geostatistics algorithms,
II.4 Markov random fields,
II.5 Nonstationary modeling with training images,
II.6 Multivariate modeling with training images,
II.7 Training image construction,
II.8 Validation and quality control,
II.9 Inverse modeling with training images,
II.10 Parallelization,

Part III Applications :
III.1 Reservoir forecasting the West Coast of Africa (WCA) reservoir,
III.2 Geological resources modeling in mining, Coauthored by Cristian P'erez, Julian M. Ortiz, & Alexandre Boucher,
III.3 Climate modeling application the case of the Murray Darling Basin,
Index.

"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"--

"The topic of this book concerns an area of geostatistics that has commonly been known as multiple-point geostatistics because it uses more than two-point statistics (correlation), traditionally represented by the variogram, to model spatial phenomena"--