Application of the improved Probabilistic Fuzzy Logic Inference Engine Model to evaluate Mineralization Prospectivity - Taking the Gejiu region of Yunnan, China as an example

Abstract

Abstract A probabilistic fuzzy logic inference engine simulated by the Monte Carlo method is used to determine the mineralization prospectivity of Sn deposits in the Gejiu region, and the uncertainty of the mineralization prospectivity is evaluated. The elemental analysis of the fuzzy logic inference engine is improved, and the Hilbert-Huang transformation (HHT) multi-scale model is integrated. The copula function is proposed to solve the problem of correlation between elements. The probabilistic fuzzy logic inference engine simulated by the Monte Carlo method can provide more information than the traditional method. Its most significant advantage is that it can describe the potential and uncertainty of data and models, which are caused by random error and fuzziness. This information can be used in the subsequent risk assessment of the exploration targets. It can also determine the largest source of uncertainty in the final mineralization prospectivity map, thereby reducing the uncertainty.