Kriging Variance and Kriging Procedure


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.


Covariance Kriging Summing 


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