This page accompanies the book entitled Multiple-point Geostatistics: Stochastic Modeling with Training Images, First Edition, by Gregoire Mariethoz and Jef Caers, © 2014 John Wiley & Sons, Ltd. It can be purchased on the website of Wiley or through other channels such as Amazon or Bookdepository.
On these pages you will find additional resources under the form of a library of training images, links to research codes and updated bibliographic references. These training images are the source files of the examples that we have used in the book, and are available to download and use for testing methods and for benchmarking computer codes.
The 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.
Training images used in the book
Below are some of the training images that are employed in the book. They are presented both graphically and in ASCII format. A more extensive training image library can be found on our Github repository.
These images be used freely for research purposes. All files are in GSLIB ASCII format. More details here: http://www.gslib.com/gslib_help/format.html.
Large files are split into several smaller zipped files. To extract them, download all parts to the same folder and then open the .zip file.
Walker Lake exhaustive DEM
100 sample data extracted from the Walker Lake DEM
Rock density in a homogeneous layer of a carbonate reservoir (3D)
Rock density in a heterogeneous deltaic reservoir (3D)
Walker lake exhaustive DEM categorized
Walker lake 100 sample categorized DEM data
Training image, categorized
1D-temporal grid representing 120 years of daily rainfall measures in Sydney, Australia
2D grid of a satellite image of the Sundarbans region, Bengladesh
3D grid representing the hydrofacies in an alluvial aquifer in the Maules Creek valley, Australia.
Red component, green component and blue component of an image.
Channels training image (Strebelle 2002)
Ganges delta, Bangladesh
2D grid of a satellite image of the Sundarbans region, Bengladesh – transformed in a binary variable.
Two simple training images
Lines with arrows
Elementary training image: categorical 3D layers
Elementary training image: continuous 3D layers
3D categorical folds
3D continuous folds
Multivariate training image
Link to file in ASCII format
Herten training image (Bayer et al. 2011)
FLUVSIM object-based model
Elementary training image – 3D checker
Rotation-invariant simulation – 90 degrees tolerance
Rotation-invariant simulation – 20 degrees tolerance
Reconstruction of sea surface temperature over the Pacific Ocean
Incomplete data and 5 realizations
Link to file in ASCII format
Process-based model (FLUMY)
Stone wall training image
West Coast of Africa object-based training images