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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…mehr
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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.
Produktdetails
- Produktdetails
- Wiley Series in Probability and Statistics
- Verlag: Wiley & Sons
- 2. Aufl.
- Seitenzahl: 736
- Erscheinungstermin: 26. März 2012
- Englisch
- Abmessung: 241mm x 164mm x 50mm
- Gewicht: 1178g
- ISBN-13: 9780470183151
- ISBN-10: 0470183152
- Artikelnr.: 33608514
- Wiley Series in Probability and Statistics
- Verlag: Wiley & Sons
- 2. Aufl.
- Seitenzahl: 736
- Erscheinungstermin: 26. März 2012
- Englisch
- Abmessung: 241mm x 164mm x 50mm
- Gewicht: 1178g
- ISBN-13: 9780470183151
- ISBN-10: 0470183152
- Artikelnr.: 33608514
Jean-Paul Chilès is Deputy Director of the Center of Geosciences and Geoengi neering at MINES ParisTech, France. Pierre Delfiner is Principal of PetroDecisions, a consulting firm based in Paris, France.
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
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
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
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