|  | Chilès, Jean-Paul / Delfiner, Pierre
Geostatistics
Modeling Spatial Uncertainty
Wiley Series in Probability and Statistics
 2. Auflage April 2012
109,- Euro
2012. 726 Seiten, Hardcover
ISBN 978-0-470-18315-1 - John Wiley & Sons
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| Kurzbeschreibung
This second edition of one of the best-selling books on geostatistics provides through updates from two authoritative authors with over twenty years of experience in the field. It 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 geostatistics, including kernal methods, extreme values geostatistics, and modeling in geo-chronologic space. It can be used as a reference book for geostatisticians, physicists, and earth scientists in both industry and academia and as a supplemental text in related couses at the Ph.D level.
Aus dem Inhalt
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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