Geostatistics: Modeling Spatial UncertaintyA novel, practical approach to modeling spatial uncertainty. This book deals with statistical models used to describe natural variables distributed in space or in time and space. It takes a practical, unified approach to geostatistics-integrating statistical data with physical equations and geological concepts while stressing the importance of an objective description based on empirical evidence. This unique approach facilitates realistic modeling that accounts for the complexity of natural phenomena and helps solve economic and development problems-in mining, oil exploration, environmental engineering, and other real-world situations involving spatial uncertainty. Up-to-date, comprehensive, and well-written, Geostatistics: Modeling Spatial Uncertainty explains both theory and applications, covers many useful topics, and offers a wealth of new insights for nonstatisticians and seasoned professionals alike. This volume: * Reviews the most up-to-date geostatistical methods and the types of problems they address. * Emphasizes the statistical methodologies employed in spatial estimation. * Presents simulation techniques and digital models of uncertainty. * Features more than 150 figures and many concrete examples throughout the text. * Includes extensive footnoting as well as a thorough bibliography. Geostatistics: Modeling Spatial Uncertainty is the only geostatistical book to address a broad audience in both industry and academia. An invaluable resource for geostatisticians, physicists, mining engineers, and earth science professionals such as petroleum geologists, geophysicists, and hydrogeologists, it is also an excellent supplementary text for graduate-level courses in related subjects. |
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algorithm analysis applications approximation aquifer average bivariate distributions block Boolean boundary conditions calculated Chilès coefficients cokriging computed conditional distribution conditional expectation conditional simulation consider correlation correlogram covariance C(h covariance function covariance model cross-covariance data points defined denote derived differentiable discrete disjunctive kriging domain Dordrecht drift equation ergodic error example facies FIGURE finite flow formula gamma Gaussian RF geological Geostatistics grade grid increments independent indicator indicator function integral interval IRF-k isotropic Journel Kluwer kriging estimator kriging variance lognormal marginal distribution Mathematical Matheron matrix mean measure method multivariate nonconditional simulation nugget effect obtained orthogonal parameters permeability Poisson point process Poisson process polynomial positive definite problem properties random function random set random variables residuals sample points sample variogram scale Section simple kriging solution space spectral spectral method stationary Statistical stochastic tion transformation transmissivity turning bands values variogram h vector weights Z₁ zero