Titlebook: Geostatistics for Engineers and Earth Scientists; Ricardo A. Olea Book 1999 Kluwer Academic Publishers 1999 Estimator.Kriging.Time series.

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Fabio Corno,Richa Lal,Stefano ColomboCharacteristic of geostatistics and other stochastic methods is the ability to assign confidence intervals to the estimates. The confidence intervals are derived from cumulative distributions of random functions.

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Tax and Management Company Structuring,Simple, ordinary, and universal kriging are not multivariate models in the usual statistical sense of the term. Despite the fact that they employ a random function model comprising an infinite number of random variables, they are all used for the modeling of a single attribute.

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The Semivariogram,Three good reasons may be cited to explain why the semivariogram is important in geostatistics:

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Drift and Residuals,In Chapter 6, we have formulated universal kriging for the modeling of an attribute with a systematic trend. In some instances, however, the characteristic of interest is the trend itself rather than the attribute or, even more commonly, it is the residuals resulting from subtracting the drift from the kriging modeling of the attribute.

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Stochastic Simulation,If one analyzes a regular grid of kriging estimates for a spatial attribute such as that shown in Figure 9.1, one finds that there is an uneven smoothing in the grid of estimates—smoothing that is inversely proportional to the data density. Such distortion can be visualized in several ways:

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Cumulative Distribution Estimators,In the previous chapter we have seen different ways to generate cumulative distributions, including a method based on the kriging estimate, when the assumption of multinormality is accepted.