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E-raamat: Geostatistics: Modeling Spatial Uncertainty

(Ecole des Mines de Paris- Paris, France), (TOTAL Exploration Production)
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Praise for the First Edition

". . . a readable, comprehensive volume that . . . belongs on the desk, close at hand, of any serious researcher or practitioner." Mathematical Geosciences

The state of the art in geostatistics

Geostatistical models and techniques such as kriging and stochastic multi-realizations exploit spatial correlations to evaluate natural resources, help optimize their development, and address environmental issues related to air and water quality, soil pollution, and forestry. Geostatistics: Modeling Spatial Uncertainty, Second Edition presents a comprehensive, up-to-date reference on the topic, now featuring the latest developments in the field.

The authors explain both the theory and applications of geostatistics through a unified treatment that emphasizes methodology. Key topics that are the foundation of geostatistics are explored in-depth, including stationary and nonstationary models; linear and nonlinear methods; change of support; multivariate approaches; and conditional simulations. The Second Edition highlights the growing number of applications of geostatistical methods and discusses three key areas of growth in the field:





New results and methods, including kriging very large datasets; kriging with outliers; non-separable space-time covariances; multipoint simulations; pluri-gaussian simulations; gradual deformation; and extreme value geostatistics Newly formed connections between geostatistics and other approaches such as radial basis functions, Gaussian Markov random fields, and data assimilation New perspectives on topics such as collocated cokriging, kriging with an external drift, discrete Gaussian change-of-support models, and simulation algorithms

Geostatistics, Second Edition is an excellent book for courses on the topic at the graduate level. It also serves as an invaluable reference for earth scientists, mining and petroleum engineers, geophysicists, and environmental statisticians who collect and analyze data in their everyday work.

Arvustused

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, Chiles and Delfiners 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)

Preface to the Second Edition ix
Preface to the First Edition xiii
Abbreviations xv
Introduction 1(10)
Types of Problems Considered
2(6)
Description or Interpretation?
8(3)
1 Preliminaries
11(17)
1.1 Random Functions
11(11)
1.2 On the Objectivity of Probabilistic Statements
22(2)
1.3 Transitive Theory
24(4)
2 Structural Analysis
28(119)
2.1 General Principles
28(5)
2.2 Variogram Cloud and Sample Variogram
33(26)
2.3 Mathematical Properties of the Variogram
59(19)
2.4 Regularization and Nugget Effect
78(6)
2.5 Variogram Models
84(25)
2.6 Fitting a Variogram Model
109(13)
2.7 Variography in the Presence of a Drift
122(8)
2.8 Simple Applications of the Variogram
130(8)
2.9 Complements: Theory of Variogram Estimation and Fluctuation
138(9)
3 Kriging
147(91)
3.1 Introduction
147(2)
3.2 Notations and Assumptions
149(1)
3.3 Kriging with a Known Mean
150(11)
3.4 Kriging with an Unknown Mean
161(35)
3.5 Estimation of a Spatial Average
196(8)
3.6 Selection of a Kriging Neighborhood
204(12)
3.7 Measurement Errors and Outliers
216(9)
3.8 Case Study: The Channel Tunnel
225(7)
3.9 Kriging Under Inequality Constraints
232(6)
4 Intrinsic Model of Order k
238(61)
4.1 Introduction
238(2)
4.2 A Second Look at the Model of Universal Kriging
240(5)
4.3 Allowable Linear Combinations of Order k
245(7)
4.4 Intrinsic Random Functions of Order k
252(5)
4.5 Generalized Covariance Functions
257(12)
4.6 Estimation in the IRF Model
269(12)
4.7 Generalized Variogram
281(5)
4.8 Automatic Structure Identification
286(8)
4.9 Stochastic Differential Equations
294(5)
5 Multivariate Methods
299(87)
5.1 Introduction
299(1)
5.2 Notations and Assumptions
300(2)
5.3 Simple Cokriging
302(3)
5.4 Universal Cokriging
305(15)
5.5 Derivative Information
320(10)
5.6 Multivariate Random Functions
330(30)
5.7 Shortcuts
360(10)
5.8 Space-Time Models
370(16)
6 Nonlinear Methods
386(92)
6.1 Introduction
386(1)
6.2 Global Point Distribution
387(5)
6.3 Local Point Distribution: Simple Methods
392(9)
6.4 Local Estimation by Disjunctive Kriging
401(32)
6.5 Selectivity and Support Effect
433(12)
6.6 Multi-Gaussian Change-of-Support Model
445(3)
6.7 Affine Correction
448(1)
6.8 Discrete Gaussian Model
449(17)
6.9 Non-Gaussian Isofactorial Change-of-Support Models
466(3)
6.10 Applications and Discussion
469(1)
6.11 Change of Support by the Maximum (C. Lantuejoul)
470(8)
7 Conditional Simulations
478(151)
7.1 Introduction and Definitions
478(11)
7.2 Direct Conditional Simulation of a Continuous Variable
489(6)
7.3 Conditioning by Kriging
495(7)
7.4 Turning Bands
502(6)
7.5 Nonconditional Simulation of a Continuous Variable
508(38)
7.6 Simulation of a Categorical Variable
546(28)
7.7 Object-Based Simulations: Boolean Models
574(16)
7.8 Beyond Standard Conditioning
590(16)
7.9 Additional Topics
606(9)
7.10 Case Studies
615(14)
Appendix 629(13)
References 642(47)
Index 689
Jean-Paul Chilès is Deputy Director of the Center of Geosciences and Geoengineering at MINES ParisTech, France.

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