Description
Book SynopsisSolved Problems in Geostatistics brings together exercises and projects that demonstrate key principles and build strong bridges between theory and practice. Each chapter focuses on a comprehensive topic with examples and problems for a technologically evolving audience.
Trade Review?The book is more than its title; it really is a treatise on how to model data by two experienced and competent analysts.?(
Biometrics , September 2009)
Table of ContentsPreface and Acknowledgments.
1. Introduction.
1.1 Plan of this Book.
1.2 The Premise of Geostatistics.
1.3 Nomenclature.
2. Getting Comfortable with Probabilities.
2.1 Parametric Probability Distributions.
2.2 Variance of Linear Combinations.
2.3 Standardization and Probability Intervals.
3. Obtaining Representative Distributions.
3.1 Basic Declustering.
3.2 Debiasing with Bivariate Glaussian Distribution.
3.3 Comparison of Declustering Methods.
4. Monte Carlo Simulation.
4.1 Impact of the Central Limit Theorem.
4.2 Bootstrap and Spatial Bootstrap.
4.3 Transfer of Uncertainty.
5. Variograms and Volume Variance.
5.1 Geometric Anisotropy.
5.2 Variogram Calculation.
5.3 Variogram Modeling and Volume Variance.
6. Kriging.
6.1 Stationary Kriging.
6.2 Nonstationary Kriging.
6.3 Screening Effect of Kriging.
7. Gaussian Simulation.
7.1 Bivariate Gaussian Distribution.
7.2 Conditioning by Kriging.
7.3 Gaussian Simulation.
8. Indicators.
8.1 Variogram of Objects.
8.2 Indicator Variograms and the Gaussian Distribution.
8.3 Indicator Simulation for Categorical Data.
9. Multiple Variables.
9.1 Linear Model of Coregionalization.
9.2 Gaussian Cosimulation.
9.3 Multiscale Cokriging.
10. Special Topics.
10.1 Decision Making in the Presence of Uncertainty.
10.2 Trend Model Construction.
10.3 Multiple Point Statistics.
11. Closing Remarks.
Bibliography.
Index.
