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ON THE GEOSTATISTICAL APPROACH TO THE INVERSE PROBLEM. (R825689C037)
Citation:
Kitanidis, P. K. ON THE GEOSTATISTICAL APPROACH TO THE INVERSE PROBLEM. (R825689C037). ADVANCES IN WATER RESOURCES. American Chemical Society, Washington, DC, 19(6):333-342, (1996).
Description:
Abstract
The geostatistical approach to the inverse problem is discussed with emphasis on the importance of structural analysis. Although the geostatistical approach is occasionally misconstrued as mere cokriging, in fact it consists of two steps: estimation of statistical parameters ("structural analysis") followed by estimation of the distributed parameter conditional on the observations ("cokriging" or "weighted least squares"). It is argued that in inverse problems, which are algebraically undetermined, the challenge is not so much to reproduce the data as to select an algorithm with the prospect of giving good estimates where there are no observations. The essence of the geostatistical approach is that instead of adjusting a grid-dependent and potentially large number of block conductivities (or other distributed parameters), a small number of structural parameters are fitted to the data. Once this fitting is accomplished, the estimation of block conductivities ensues in a predetermined fashion without fitting of additional parameters. Also, the methodology is compared with a straightforward maximuma posteriori probability estimation method. It is shown that the fundamental differences between the two approaches are: (a) they use different principles to separate the estimation of covariance parameters from the estimation of the spatial variable; (b) the method for covariance parameter estimation in the geostatistical approach produces statistically unbiased estimates of the parameters that are not strongly dependent on the discretization, while the other method is biased and its bias becomes worse by refining the discretization into zones with different conductivity.
Index Terms: Statistical methods; Inverse problems; Problem solving; Parameter estimation; Least squares approximations; Curve fitting; Algorithms; Geostatistical method; Cokriging; Covariance parameters