Applied Geostatistics with SGeMS: A User's Guide

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  • Edition: CD
  • Format: Hardcover
  • Copyright: 3/23/2009
  • Publisher: Cambridge University Press
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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

Forewordp. ix
Prefacep. xi
List of programsp. xiii
List of symbolsp. xv
Introductionp. 1
General overviewp. 5
A quick tour of the graphical user interfacep. 5
A typical geostatistical analysis using SGeMSp. 5
Loading data into an SGeMS projectp. 8
Exploratory data analysis (EDA)p. 10
Variogram modelingp. 10
Creating a gridp. 12
Running a geostatistics algorithmp. 13
Displaying the resultsp. 14
Post-Processing the results with Pythonp. 19
Saving the resultsp. 21
Automating tasksp. 21
Data file formatsp. 23
Parameter filesp. 24
Defining a 3D ellipsoidp. 26
Geostatistics: a recall of conceptsp. 29
Random variablep. 30
Random functionp. 33
Simulated realizationsp. 34
Estimated mapsp. 37
Conditional distributions and simulationsp. 38
Sequential simulationp. 40
Estimating the local conditional distributionsp. 42
Inference and stationarityp. 44
The variogram, a 2-point statisticsp. 48
The kriging paradigmp. 50
Simple krigingp. 51
Ordinary kriging and other variantsp. 54
Kriging with linear average variablep. 57
Cokrigingp. 59
Indicator krigingp. 61
An introduction to mp statisticsp. 62
Two-point simulation algorithmsp. 65
Sequential Gaussian simulationp. 66
Direct sequential simulationp. 67
Direct error simulationp. 68
Indicator simulationp. 69
Multiple-point simulation algorithmsp. 71
Single normal equation simulation (SNESIM)p. 71
Filter-based algorithm (FILTERSIM)p. 72
The nu/tau expression for combining conditional probabilitiesp. 74
Inverse problemp. 79
Data sets and SGeMS EDA toolsp. 80
The data setsp. 80
The 2D data setp. 80
The 3D data setp. 81
The SGeMS EDA toolsp. 84
Common parametersp. 85
Histogramp. 85
Q-Q plot and P-P plotp. 87
Scatter plotp. 87
Variogram computation and modelingp. 90
Variogram computation in SGeMSp. 92
Selecting the head and tail propertiesp. 92
Computation parametersp. 93
Displaying the computed variogramsp. 98
Variogram modeling in SGeMSp. 98
Common parameter input interfacesp. 101
Algorithm panelp. 101
Selecting a grid and propertyp. 102
Selecting multiple propertiesp. 103
Search neighborhoodp. 104
Variogramp. 104
Krigingp. 105
Line entryp. 105
Non-parametric distributionp. 106
Errors in parametersp. 108
Estimation algorithmsp. 109
KRIGING: univariate krigingp. 109
COKRIGING: kriging with secondary datap. 119
BKRIG: block kriging estimationp. 122
Stochastic simulation algorithmsp. 132
Variogram-based simulationsp. 132
LUSIM: LU simulationp. 133
SGSIM: sequential Gaussian simulationp. 135
COSGSIM: sequential Gaussian CO-simulationp. 139
DSSIM: direct sequential simulationp. 143
SISIM: sequential indicator simulationp. 147
COSISIM: sequential indicator co-simulationp. 153
BSSIM: block sequential simulationp. 157
BESIM: block error simulationp. 163
Multiple-point simulation algorithmsp. 168
SNESIM: single normal equation simulationp. 169
FILTERSIM: filter-based simulationp. 191
Utilitiesp. 215
TRANS: histogram transformationp. 215
TRANSCAT: categorical transformationp. 218
POSTKRIGING: post-processing of kriging estimatesp. 222
POSTSIM: post-processing of realizationsp. 224
NU-TAU MODEL: combining probability fieldsp. 227
BCOVAR: block covariance calculationp. 228
MOVING WINDOW: moving window statisticsp. 234
TIGENERATOR: object-based image generatorp. 237
Object interactionp. 239
Scripting, commands and plug-insp. 245
Commandsp. 245
Command listsp. 246
Execute command filep. 248
Python scriptp. 249
SGeMS Python modulesp. 250
Running Python scriptsp. 250
Plug-insp. 252
Bibliographyp. 254
Indexp. 260
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