Applied Geostatistics with SGeMS: A User's Guide
by Nicolas Remy , Alexandre Boucher , Jianbing WuEdition: CD
Format: Hardcover
Pub. Date: 2009-03-23
Publisher(s): Cambridge University Press
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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 |
| Table of Contents provided by Ingram. All Rights Reserved. |
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