Applied Geostatistics with SGeMS A User's Guide

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. This prac...

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Bibliographic Details
Main Authors: Remy, Nicolas, Boucher, Alexandre, Wu, Jianbing
Format: eBook Book
Language:English
Published: Cambridge, UK Cambridge University Press 2009
New York
Edition:1
Subjects:
Civil Engineering
Civil Engineering & Construction Materials
Computer simulation
General Engineering & Project Administration
General References
Geological modeling
Geological modeling > Computer simulation
Geological modeling > Statistical methods
Geology
Geology > Computer simulation
Geology > Statistical methods
Geology > Statistical methods > Software
Geology, Exploration & Drilling
Investments
Oil & Gas Engineering
SGeMS (Computer software)
Software
Statistical methods
Transportation Engineering
United States
ISBN:9780521514149, 9781107403246, 0521514142, 1107403243
Online Access:Get full text
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Table of Contents:
  • Title Page List of Programs List of Symbols Preface Table of Contents 1. Introduction 2. General Overview 3. Geostatistics: A Recall of Concepts 4. Data Sets and SGeMS EDA Tools 5. Variogram Computation and Modeling 6. Common Parameter Input Interfaces 7. Estimation Algorithms 8. Stochastic Simulation Algorithms 9. Utilities 10. Scripting, Commands and Plug-ins Bibliography Index Contents of CD-ROM
  • 10.2 Python script -- 10.2.1 SGeMS Python modules -- 10.2.2 Running Python scripts -- 10.3 Plug-ins -- Bibliography -- Index
  • 4.2.3 Q-Q plot and P-P plot -- 4.2.4 Scatter plot -- 5 Variogram computation and modeling -- 5.1 Variogram computation in SGeMS -- 5.1.1 Selecting the head and tail properties -- 5.1.2 Computation parameters -- 5.1.3 Displaying the computed variograms -- 5.2 Variogram modeling in SGeMS -- 6 Common parameter input interfaces -- 6.1 Algorithm panel -- 6.2 Selecting a grid and property -- 6.3 Selecting multiple properties -- 6.4 Search neighborhood -- 6.5 Variogram -- 6.6 Kriging -- 6.7 Line entry -- 6.8 Non-parametric distribution -- 6.9 Errors in parameters -- 7 Estimation algorithms -- 7.1 KRIGING: univariate kriging -- 7.2 INDICATOR KRIGING -- 7.3 COKRIGING: kriging with secondary data -- 7.4 BKRIG: block kriging estimation -- 8 Stochastic simulation algorithms -- 8.1 Variogram-based simulations -- 8.1.1 LUSIM: LU simulation -- 8.1.2 SGSIM: sequential Gaussian simulation -- 8.1.3 COSGSIM: sequential Gaussian co-simulation -- 8.1.4 DSSIM: direct sequential simulation -- 8.1.5 SISIM: sequential indicator simulation -- 8.1.6 COSISIM: sequential indicator co-simulation -- 8.1.7 BSSIM: block sequential simulation -- 8.1.8 BESIM: block error simulation -- 8.2 Multiple-point simulation algorithms -- 8.2.1 SNESIM: single normal equation simulation -- 8.2.2 FILTERSIM: filter-based simulation -- 9 Utilities -- 9.1 TRANS: histogram transformation -- 9.2 TRANSCAT: categorical transformation -- 9.3 POSTKRIGING: post-processing of kriging estimates -- 9.4 POSTSIM: post-processing of realizations -- 9.5 NU-TAU MODEL: combining probability fields -- 9.6 BCOVAR: block covariance calculation -- 9.7 IMAGE PROCESSING -- 9.8 MOVING WINDOW: moving window statistics -- 9.9 TIGENERATOR: object-based image generator -- 9.9.1 Object interaction -- 10 Scripting, commands and plug-ins -- 10.1 Commands -- 10.1.1 Command lists -- 10.1.2 Execute command file
  • Cover -- APPLIED GEOSTATISTICS WITH SGeMS -- Title -- Copyright -- Contents -- Foreword -- Preface -- List of Programs -- List of symbols -- 1 Introduction -- 2 General overview -- 2.1 A quick tour of the graphical user interface -- 2.2 A typical geostatistical analysis using SGeMS -- 2.2.1 Loading data into an SGeMS project -- 2.2.2 Exploratory data analysis (EDA) -- 2.2.3 Variogram modeling -- 2.2.4 Creating a grid -- 2.2.5 Running a geostatistics algorithm -- 2.2.6 Displaying the results -- 2.2.7 Post-processing the results with Python -- 2.2.8 Saving the results -- 2.2.9 Automating tasks -- 2.3 Data file formats -- 2.4 Parameter files -- 2.5 Defining a 3D ellipsoid -- 3 Geostatistics: a recall of concepts -- 3.1 Random variable -- 3.2 Random function -- 3.2.1 Simulated realizations -- 3.2.2 Estimated maps -- 3.3 Conditional distributions and simulations -- 3.3.1 Sequential simulation -- 3.3.2 Estimating the local conditional distributions -- 3.4 Inference and stationarity -- 3.5 The variogram, a 2-point statistics -- 3.6 The kriging paradigm -- 3.6.1 Simple kriging -- 3.6.2 Ordinary kriging and other variants -- 3.6.3 Kriging with linear average variable -- 3.6.4 Cokriging -- 3.6.5 Indicator kriging -- 3.7 An introduction to mp statistics -- 3.8 Two-point simulation algorithms -- 3.8.1 Sequential Gaussian simulation -- 3.8.2 Direct sequential simulation -- 3.8.3 Direct error simulation -- 3.8.4 Indicator simulation -- 3.9 Multiple-point simulation algorithms -- 3.9.1 Single normal equation simulation (SNESIM) -- 3.9.2 Filter-based algorithm (FILTERSIM) -- 3.10 The nu/tau expression for combining conditional probabilities -- 3.11 Inverse problem -- 4 Data sets and SGeMS EDA tools -- 4.1 The data sets -- 4.1.1 The 2D data set -- 4.1.2 The 3D data set -- 4.2 The SGeMS EDA tools -- 4.2.1 Common parameters -- 4.2.2 Histogram