Geostatistics: Modeling Spatial UncertaintyWiley, 1999 M04 7 - 695 páginas A 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. |
Contenido
Preliminaries | 11 |
Structural Analysis | 29 |
Kriging | 150 |
Derechos de autor | |
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Otras ediciones - Ver todas
Geostatistics: Modeling Spatial Uncertainty Jean-Paul Chilès,Pierre Delfiner Vista previa limitada - 2009 |
Términos y frases comunes
a₁ algorithm bivariate distributions block Boolean C₁₁(h calculated Chilès coefficients cokriging estimator computed conditional distribution conditional expectation conditional simulations consider correlation correlogram covariance C(h covariance function covariance model cross-covariance data points defined denote derived discrete disjunctive kriging domain drift equations equivalent error example F₁ factors Figure finite formula gamma Gaussian RF geostatistical grade grid increments independent indicator indicator function integral interpolation interval IRF-k isofactorial model isotropic kriging estimator kriging variance linear combination lognormal marginal distribution Matheron matrix mean measure method multivariate nugget effect obtained orthogonal parameters permeability Poisson point process Poisson process polynomial positive definite problem random function random set random variables representation residuals sample points sample variogram scale Section seismic selected simple kriging solution space spatial spectral spectral method splines standard stationary tion transformation turning bands valid variance vector weights x₁ Z₁ Z₁(x zero