Geostatistics: Modeling Spatial Uncertainty

Geostatistics: Modeling Spatial Uncertainty

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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 GeosciencesThe state of the art in geostatisticsGeostatistical models and techniques such as kriging and stochastic multirealizations 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, uptodate 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 indepth, 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; nonse??parable spacetime covariances; multipoint simulations; plurigaussian simulations; gradual deformation; and extreme value geostatisticsNewly formed connections between geostatistics and other approaches such as radial basis functions, Gaussian Markov random fields, and data assimilationNew perspectives on topics such as collocated cokriging, kriging with an external drift, discrete Gaussian changeofsupport models, and simulation algorithmsGeostatistics, 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.

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