Improved Distance Power Inverse Ratio Method Based on Spatial Data Mining Technique



Through deep analysis on the principles and theories of Distance Power Inverse Ratio Method (DPIRM) and study on methods of geostatistics, this paper proposes a new interpolation to solve inherent problems of traditional DPIRM. This new method uses the regionalized variable theory to mining geological data to get the parameters of spatial anisotropy spheroid, uses cross-validation methodology with artificial intelligence approach, such like genetic algorithm (GA), to get the optimal power exponent of DPIRM. Compared with methods of geostatistics, the new DPIRM method can avoid human interference in fitting the spatial variogram. Compared with methods of traditional DPIRM method, the new DPIRM can avoid the problems of isotropous and power exponent decided without objective evidences. After practical application in a large-scale copper open-pit, the result proves that IDPIRM with spatial data mining technology can improve interpolation precision effectively, and also ensures the reliability of results.


Distance Power Inverse Ratio Method, Data mining, Regionalized variable, Anisotropy spheroid, Power exponent spatial interpolation, GA


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