Data-Driven Mineral Prospectivity Mapping Based on Known Deposits Using Association Rules

Recently, machine learning methods have been utilized to mine correlations between geological variables and mineral deposits because of their significance in mineral prospectivity mapping (MPM). However, the characteristics of known mineral deposits are often overlooked in supervised approaches to MPM because only spatial coordinates of known mineral deposits are used as positive training samples. We propose an interpretable method using association rules to predict mineral prospectivity in the Pangxidong district by incorporating characteristics associated with mineral deposits into MPM. Specifically, association rules are a type of data-driven equivalent of ore-controlling factors in knowledge-based exploration and it warrants a broader consideration in modern data-centric exploration. The detailed procedures are as follows: (1) two strong association rules related to mineral deposits were extracted using the Apriori algorithm based on the known Ag–Au and Pb–Zn deposits in Pangxidong; (2) the weights of the variables in the data filtered by the strong association rules were defined using entropy weight method (EWM); and (3) the probability of finding undiscovered mineral deposits was calculated. The Apriori algorithm delineated 57.3% and 52.6% of the known Ag–Au deposits and Pb–Zn deposits within 3.91% and 1.48% of the study area, respectively. In addition, after the EWM, high-probability areas of Ag–Au deposits and Pb–Zn deposits cover 1.05% and 0.43% of the study area, respectively. Therefore, the proposed method is effective and efficient in MPM and it has the potential to be applied more broadly.