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Titlebook: Geostatistics and Petroleum Geology; Michael Edward Hohn Book 1999Latest edition Kluwer Academic Publishers 1999 Fitting.Fundament.Kriging

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Nonlinear estimation: disjunctive and lognormal kriging,riable above a cutoff; drawing a map of estimated local medians rather than estimated local averages; and handling extreme values in markedly nonnormal data all require consideration of local distribution.
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The semivariogram, for measuring spatial autocorrelation of a regionalized variable. As the name implies, a semivariogram is a measure of variance. Although procedures exist for modeling the semivariogram through iterative or least-squares methods, practitioners recommend actual inspection of the observed semivariogr
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Linear estimation,s, to drawing flat surfaces within triangulations, all requiring some criterion to be satisfied. The geologist working by hand interpolates between pairs of points, draws connecting contours, does some smoothing to make the map look ‘real’, and perhaps works in some trends derived from geological ex
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Nonlinear estimation: disjunctive and lognormal kriging,he expected precision of the estimates. However, some applications may call for knowledge of local spatial distributions. Placing confidence intervals around local estimates; stating the probability that a regionalized variable lies below, between, or above some values; computing mean values of a va
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Indicator kriging, data that are more easily treated if converted to a nominal scale. As a simple example of such an indicator variable, the presence or absence of a show of gas may be represented by the two values 0 and 1. Statistics such as success ratio assume a value for economic threshold that may not be stated.
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Conditional simulation,tistical approach to mapping lies in the modeling of spatial covariance that precedes interpolation; semivariogram models derived from this step can make the final estimates sensitive to directional anisotropies present in the data. On the other hand, the smoothing property of kriging can also mean
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https://doi.org/10.1007/978-3-658-26117-7There are several situations where one may want to study and exploit the covariance between two or more regionalized variables.
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Multivariate geostatistics,There are several situations where one may want to study and exploit the covariance between two or more regionalized variables.
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