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Kriging Variance and Kriging Procedure

Abstract

In order to derive Kriging Variance, we proceed as follows: we assume that Z′ (x) — the random function is defined on a point support and is second order stationary. It follows that E[Z(x)]=m, and the covariance, defined as E[Z(x+h)Z(x)]−m2=C(h) exists. We know that E[{Z(x+h)−Z(x)}2]=2γ(h). We are interested in the mean Z V (x0)=1/V∫Z(x)dx. The data comprises a set of grade values Z(x i ), in short x i’ i=1 to N. The grades are defined either on point supports, core supports, etc. They could also be mean grades Z Vi (x i ) defined on the supports V i centered on the points x i . It is possible that the N supports could be different from each other. Under the assumption of stationarity, the expectation of these data is m. That is, E(Z i )=m.

Keywords

Ordinary Kriging Point Support Block Versus Kriging Variance Kriged Estimate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Capital Publishing Company 2009

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