As an interpolation model with strict mathematical form, the Kriging algorithm of geostatistics has the problem of negative weight when solving the weight value. As an important model of optimization probability, the minimum cross entropy algorithm is often used in insurance finance, power communication and other fields, but there is little research on weight optimization of spatial interpolation model. In this paper, the weight of negative weight points calculated by Kriging interpolation is used as a priori weight, and the optimization weight and corresponding optimization geological parameters (used to describe the values of elevation, mineral value, geothermal and other attributes) are obtained through the minimum cross entropy algorithm. By comparing the weight distribution and geological parameters before and after optimization based on the minimum cross entropy algorithm, the comparison results show that the Kriging model based on the minimum cross entropy algorithm eliminates the negative weight phenomenon and improves the estimation accuracy.
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