Applied Geostatistics with SGeMS: A User's Guide

by Nicolas Remy , Alexandre Boucher , Jianbing Wu

• ISBN13: 9780521514149
• ISBN10: 0521514142
• Edition: CD
• Format: Hardcover
• Copyright: 2009-03-23
• Publisher: Cambridge University Press

Summary

The Stanford Geostatistical Modeling Software (SGeMS) is an open-source computer package for solving problems involving spatially related variables. It provides geostatistics practitioners with a user-friendly interface, an interactive 3-D visualization and a wide selection of algorithms. With over 12000 downloads in less than 2 years, SGeMS is used in many research groups and companies.

Table of Contents

Foreword	p. ix
Preface	p. xi
List of programs	p. xiii
List of symbols	p. xv
Introduction	p. 1
General overview	p. 5
A quick tour of the graphical user interface	p. 5
A typical geostatistical analysis using SGeMS	p. 5
Loading data into an SGeMS project	p. 8
Exploratory data analysis (EDA)	p. 10
Variogram modeling	p. 10
Creating a grid	p. 12
Running a geostatistics algorithm	p. 13
Displaying the results	p. 14
Post-Processing the results with Python	p. 19
Saving the results	p. 21
Automating tasks	p. 21
Data file formats	p. 23
Parameter files	p. 24
Defining a 3D ellipsoid	p. 26
Geostatistics: a recall of concepts	p. 29
Random variable	p. 30
Random function	p. 33
Simulated realizations	p. 34
Estimated maps	p. 37
Conditional distributions and simulations	p. 38
Sequential simulation	p. 40
Estimating the local conditional distributions	p. 42
Inference and stationarity	p. 44
The variogram, a 2-point statistics	p. 48
The kriging paradigm	p. 50
Simple kriging	p. 51
Ordinary kriging and other variants	p. 54
Kriging with linear average variable	p. 57
Cokriging	p. 59
Indicator kriging	p. 61
An introduction to mp statistics	p. 62
Two-point simulation algorithms	p. 65
Sequential Gaussian simulation	p. 66
Direct sequential simulation	p. 67
Direct error simulation	p. 68
Indicator simulation	p. 69
Multiple-point simulation algorithms	p. 71
Single normal equation simulation (SNESIM)	p. 71
Filter-based algorithm (FILTERSIM)	p. 72
The nu/tau expression for combining conditional probabilities	p. 74
Inverse problem	p. 79
Data sets and SGeMS EDA tools	p. 80
The data sets	p. 80
The 2D data set	p. 80
The 3D data set	p. 81
The SGeMS EDA tools	p. 84
Common parameters	p. 85
Histogram	p. 85
Q-Q plot and P-P plot	p. 87
Scatter plot	p. 87
Variogram computation and modeling	p. 90
Variogram computation in SGeMS	p. 92
Selecting the head and tail properties	p. 92
Computation parameters	p. 93
Displaying the computed variograms	p. 98
Variogram modeling in SGeMS	p. 98
Common parameter input interfaces	p. 101
Algorithm panel	p. 101
Selecting a grid and property	p. 102
Selecting multiple properties	p. 103
Search neighborhood	p. 104
Variogram	p. 104
Kriging	p. 105
Line entry	p. 105
Non-parametric distribution	p. 106
Errors in parameters	p. 108
Estimation algorithms	p. 109
KRIGING: univariate kriging	p. 109
INDICATOR KRIGING	p. 113
COKRIGING: kriging with secondary data	p. 119
BKRIG: block kriging estimation	p. 122
Stochastic simulation algorithms	p. 132
Variogram-based simulations	p. 132
LUSIM: LU simulation	p. 133
SGSIM: sequential Gaussian simulation	p. 135
COSGSIM: sequential Gaussian CO-simulation	p. 139
DSSIM: direct sequential simulation	p. 143
SISIM: sequential indicator simulation	p. 147
COSISIM: sequential indicator co-simulation	p. 153
BSSIM: block sequential simulation	p. 157
BESIM: block error simulation	p. 163
Multiple-point simulation algorithms	p. 168
SNESIM: single normal equation simulation	p. 169
FILTERSIM: filter-based simulation	p. 191
Utilities	p. 215
TRANS: histogram transformation	p. 215
TRANSCAT: categorical transformation	p. 218
POSTKRIGING: post-processing of kriging estimates	p. 222
POSTSIM: post-processing of realizations	p. 224
NU-TAU MODEL: combining probability fields	p. 227
BCOVAR: block covariance calculation	p. 228
IMAGE PROCESSING	p. 233
MOVING WINDOW: moving window statistics	p. 234
TIGENERATOR: object-based image generator	p. 237
Object interaction	p. 239
Scripting, commands and plug-ins	p. 245
Commands	p. 245
Command lists	p. 246
Execute command file	p. 248
Python script	p. 249
SGeMS Python modules	p. 250
Running Python scripts	p. 250
Plug-ins	p. 252
Bibliography	p. 254
Index	p. 260
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