Geostatistics: Modeling Spatial Uncertainty / Edition 2

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Overview

This second edition of the classic text incldues data and methods in a way that is approachable to practitioners and those new to the field.This update edition removes information and data that have lost relevance with time, while maintaining timeless, core methods and integrating them with new developments to the field. The authors employ an applied focus on new aspects of the field of geostatistics including kernal methods, extreme values geostatistics, and modeling in geo-chronologic space. Along with providing updated information, the authors acknowledge the scientific and statistical challenges that stem from these new models, and stress the growth and promise that geostatistics can provide to these unanswered questions.

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Editorial Reviews

From the Publisher
“All who aspire to geostatistical competence should have this book to hand.”  (European Journal of Soil Science, 1 April 2013)

“In summary, a worthwhile investment.”  (Zentralblatt MATH, 1 May 2013)

“Summarizing, Chile’s and Delfiner’s book certainly deserves  recommendation to anyone interested in geostatistics, either as a geostatician or as a researcher in modeling spatial uncertainty.”  (Computers & Geosciences, 1 February 2013)

Journal of Geodsy
This is a book that the geostatistical community has been waiting for long since" "I can recommend anyone interested in geostatistics to get the volume...I am convinced that it will be the volume to cite in the next decade or more.
Booknews
Deals with statistical models used to describe natural variables distributed in space or in time and space, integrating statistical data with physical equations and geological concepts while stressing the importance of an objective description based on empirical evidence. This approach facilitates realistic modeling that accounts for the complexity of natural phenomena and helps solve economic and development problems in mining, oil exploration, environmental engineering, and other situations involving spatial uncertainty. Of interest to geostatisticians, physicists, mining engineers, and earth science professionals. Annotation c. by Book News, Inc., Portland, Or.
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Product Details

Meet the Author

Jean-Paul Chilès is Deputy Director of the Center of Geosciences and Geoengißfineering at MINES ParisTech, France.

Pierre Delfiner is Principal of PetroDecisions, a consulting firm based in Paris, France.

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Table of Contents

Preface to the Second Edition ix

Preface to the First Edition xiii

Abbreviations xv

Introduction 1

Types of Problems Considered, 2

Description or Interpretation?, 8

1. Preliminaries 11

1.1 Random Functions, 11

1.2 On the Objectivity of Probabilistic Statements, 22

1.3 Transitive Theory, 24

2. Structural Analysis 28

2.1 General Principles, 28

2.2 Variogram Cloud and Sample Variogram, 33

2.3 Mathematical Properties of the Variogram, 59

2.4 Regularization and Nugget Effect, 78

2.5 Variogram Models, 84

2.6 Fitting a Variogram Model, 109

2.7 Variography in the Presence of a Drift, 122

2.8 Simple Applications of the Variogram, 130

2.9 Complements: Theory of Variogram Estimation and Fluctuation, 138

3. Kriging 147

3.1 Introduction, 147

3.2 Notations and Assumptions, 149

3.3 Kriging with a Known Mean, 150

3.4 Kriging with an Unknown Mean, 161

3.5 Estimation of a Spatial Average, 196

3.6 Selection of a Kriging Neighborhood, 204

3.7 Measurement Errors and Outliers, 216

3.8 Case Study: The Channel Tunnel, 225

3.9 Kriging Under Inequality Constraints, 232

4. Intrinsic Model of Order k 238

4.1 Introduction, 238

4.2 A Second Look at the Model of Universal Kriging, 240

4.3 Allowable Linear Combinations of Order k, 245

4.4 Intrinsic Random Functions of Order k, 252

4.5 Generalized Covariance Functions, 257

4.6 Estimation in the IRF Model, 269

4.7 Generalized Variogram, 281

4.8 Automatic Structure Identification, 286

4.9 Stochastic Differential Equations, 294

5. Multivariate Methods 299

5.1 Introduction, 299

5.2 Notations and Assumptions, 300

5.3 Simple Cokriging, 302

5.4 Universal Cokriging, 305

5.5 Derivative Information, 320

5.6 Multivariate Random Functions, 330

5.7 Shortcuts, 360

5.8 SpaceTime Models, 370

6. Nonlinear Methods 386

6.1 Introduction, 386

6.2 Global Point Distribution, 387

6.3 Local Point Distribution: Simple Methods, 392

6.4 Local Estimation by Disjunctive Kriging, 401

6.5 Selectivity and Support Effect, 433

6.6 Multi-Gaussian Change-of-Support Model, 445

6.7 Affine Correction, 448

6.8 Discrete Gaussian Model, 449

6.9 Non-Gaussian Isofactorial Change-of-Support Models, 466

6.10 Applications and Discussion, 469

6.11 Change of Support by the Maximum (C. Lantue´ joul), 470

7. Conditional Simulations 478

7.1 Introduction and Definitions, 478

7.2 Direct Conditional Simulation of a Continuous Variable, 489

7.3 Conditioning by Kriging, 495

7.4 Turning Bands, 502

7.5 Nonconditional Simulation of a Continuous Variable, 508

7.6 Simulation of a Categorical Variable, 546

7.7 Object-Based Simulations: Boolean Models, 574

7.8 Beyond Standard Conditioning, 590

7.9 Additional Topics, 606

7.10 Case Studies, 615

Appendix 629

References 642

Index 689

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