Introduction to Geostatistics: Applications in Hydrogeology
Engineers and applied geophysicists routinely encounter interpolation and estimation problems when analysing data from field observations. Introduction to Geostatistics presents practical techniques for the estimation of spatial functions from sparse data. The author's unique approach is a synthesis of classic and geostatistical methods with a focus on the most practical linear minimum-variance estimation methods, and includes suggestions on how to test and extend the applicability of such methods. The author includes many useful methods (often not covered in other geostatistics books) such as estimating variogram parameters, evaluating the need for a variable mean, parameter estimation and model testing in complex cases (e.g. anisotropy, variable mean, and multiple variables), and using information from deterministic mathematical models. Well illustrated with exercises and worked examples taken from hydrogeology, Introduction to Geostatistics assumes no background in statistics and is suitable for graduate-level courses in earth sciences, hydrology, and environmental engineering, and also for self-study.
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anisotropic anisotropy applications approach aquifer assume authorized increment average best estimate boundary box plot calculated Chapter cokriging compute Consider correlation covariance function defined depends described deterministic domain drift coefficients ensemble Equation error of estimation estimation methods example expected value experimental variogram exponential flow Gaussian geostatistics given head data histogram hydraulic head interpolation interquartile range interval intrinsic function intrinsic model isotropic known kriging system Lagrange multipliers linear estimation linear unbiased estimation linear variogram log-conductivity log-transmissivity mathematical matrix mean square error mean square estimation measurements node normal distribution nugget effect observations obtain ordinary kriging orthonormal residuals parameter estimation practice predictions probability density function probability distribution problem random field random variable realizations represents Sample function scale selected separation distance simple solution solve spatial function spatial variability square estimation error stationary function statistical stochastic structure tion transmissivity data unbiasedness vector