This text fulfills a need for an advanced-level work covering both the theory and application of geostatistics. It covers the most important areas of geostatistical methodology, introducing tools for description, quantitative modeling of spatial continuity, spatial prediction, and assessment of
local uncertainty and stochastic simulation. It also details the theoretical background underlying most GSLIB programs. The tools are applied to an environmental data set, but the book includes a general presentation of algorithms intended for students and practitioners in such diverse fields as
soil science, mining, petroleum, remote sensing, hydrogeology, and the environmental sciences.
1. Introduction
2. Exploratory Data Analysis
3. The Random Function Model
4. Inference and Modeling
5. Local Estimation: Accounting for a Single Attribute
6. Local Estimation: Accounting for Secondary Information
7. Assessment of Local Uncertainty
8. Assessment of
Spatial Uncertainty
9. Summary
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Dr. Pierre Goovaerts is assistant professor in the Department of Civil and Environmental Engineering, the University of Michigan, Ann Arbor. He earned his Ph.D. in agricultural sciences at the Universite Catholique de Louvain in Belgium and he has been postdoctoral fellow in the Department of
Geological and Environmental Sciences at Stanford University.
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