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Determination of Predictive Variables in Mineral Prospectivity Mapping Using Supervised and Unsupervised Methods

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Abstract

Machine learning methods have recently been used widely for mineral prospectivity mapping. However, few studies have focused on the determination of variables for mineral prospectivity prediction using such methods. Here, we present a comparative study using supervised and unsupervised methods to determine predictive variables (PVs). First, based on a mineral deposit model, 12 variables were created including information about granite, fault and strata, and information from geochemical and geophysical surveys. Second, recursive feature elimination (RFE) and sparse principal components analysis (SPCA) were used to determine the PVs for mineral prospectivity prediction. Third, the weights-of-evidence and Random Forest methods were used to integrate the PVs to generate a probability map of mineral prospectivity. Finally, the receiver operating characteristic curve was used to evaluate the performance of the PVs for indicating mineral prospectivity. The variable strata buffer, granite buffer, stratigraphic entropy, derivative norm of magnetic data, and fault buffer were selected as PVs by SPCA, whereas the derivative norm of magnetic data, fault buffer, geochemical anomalies, and strata number were selected as PVs by the RFE method. The results demonstrate that PV determination is a necessary step for mineral prospectivity mapping because it can improve the performance of mineral prospectivity prediction.

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Acknowledgments

This work was supported by the National Key R&D Program of China (2017YFC0601500, 2017YFC0601504), National Natural Science Foundation of China (41902305, 41942039), Natural Science Foundation of Hubei Province (2019CFB231), and the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (CUG190617).

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Wang, C., Chen, J. & Ouyang, Y. Determination of Predictive Variables in Mineral Prospectivity Mapping Using Supervised and Unsupervised Methods. Nat Resour Res 31, 2081–2102 (2022). https://doi.org/10.1007/s11053-021-09982-7

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