Spatially weighted singularity mapping in conjunction with random forest algorithm for mineral prospectivity modeling
Publish Year: 1402
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_IJMGE-57-4_012
تاریخ نمایه سازی: 11 دی 1402
Abstract:
Geochemical exploration data play a vital role in mineral prospectivity modelling (MPM) for discovering unknown mineral deposits. In this study, the improved spatially weighted singularity mapping (SWSM) method is used to improve the practice of identifying geochemical anomalies related to copper mineralization in the Sarduiyeh district, Iran. Then, the random forest algorithm (RF) and geometric average function (GA) are used to integrate the resulting geochemical predictor map with other predictor maps. As demonstrated by the high area under the curve (AUC) values, this approach can effectively delineate prospective areas with RF and GA. However, compared to the GA approach (AUC=۰.۷۸), the RF technique (AUC = ۰.۹۸) offers superior prediction capabilities due to its enhanced ability to capture spatial correlations between predictive maps and known mineral deposits. The proposed procedure, a hybrid of the improved SWSM and RF has outstanding predictive capabilities for identifying prospective areas. A case in point is the new, highly prospective areas identified in this study, which present priority targets for future exploration in the Sarduiyeh district.
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Authors
Saeid Ghasemzadeh
Department of Mining Engineering, Amirkabir University of Technology, Tehran, Iran;
Abbas Maghsoudi
Department of Mining and Metallurgy, Amirkabir University of Technology, Tehran, Iran.
Mahyar Yousefi
Faculty of Engineering, Malayer University, Malayer, Iran.
Oliver Kreuzer
Corporate Geoscience Group, Rockingham Beach, James Cook University, Australia.
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