{"title":"Mineral prospectivity mapping susceptibility evaluation based on interpretable ensemble learning","authors":"Zhengbo Yu , Binbin Li , Xingjie Wang","doi":"10.1016/j.oregeorev.2024.106248","DOIUrl":null,"url":null,"abstract":"<div><div>In the present study, an interpretable ensemble learning-based method for mineral prediction mapping is proposed to address the limitations of traditional mineralization prediction modeling. A stacking ensemble learning model was constructed, employing random forest (RF), extreme gradient boosting (XGBoost), and AdaBoost as primary learners, and logistic regression as the secondary learner. The model’s interpretability was analyzed using local interpretable model-agnostic explanations (LIME) and shapley additive explanations (SHAP) algorithms. The lead–zinc deposits in the Changba mining area of Gansu Province served as a case study. By integrating geological and geochemical data, and selecting 18 evaluation factors, the effectiveness and interpretability of the ensemble learning model in mineralization prediction were validated. The results demonstrate that the lead–zinc prospecting map generated using the stacking model effectively correlates geological and geochemical data with known lead–zinc deposit locations, significantly enhancing the accuracy of identifying potential lead–zinc prospecting areas.</div></div>","PeriodicalId":19644,"journal":{"name":"Ore Geology Reviews","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ore Geology Reviews","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0169136824003810","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
In the present study, an interpretable ensemble learning-based method for mineral prediction mapping is proposed to address the limitations of traditional mineralization prediction modeling. A stacking ensemble learning model was constructed, employing random forest (RF), extreme gradient boosting (XGBoost), and AdaBoost as primary learners, and logistic regression as the secondary learner. The model’s interpretability was analyzed using local interpretable model-agnostic explanations (LIME) and shapley additive explanations (SHAP) algorithms. The lead–zinc deposits in the Changba mining area of Gansu Province served as a case study. By integrating geological and geochemical data, and selecting 18 evaluation factors, the effectiveness and interpretability of the ensemble learning model in mineralization prediction were validated. The results demonstrate that the lead–zinc prospecting map generated using the stacking model effectively correlates geological and geochemical data with known lead–zinc deposit locations, significantly enhancing the accuracy of identifying potential lead–zinc prospecting areas.
期刊介绍:
Ore Geology Reviews aims to familiarize all earth scientists with recent advances in a number of interconnected disciplines related to the study of, and search for, ore deposits. The reviews range from brief to longer contributions, but the journal preferentially publishes manuscripts that fill the niche between the commonly shorter journal articles and the comprehensive book coverages, and thus has a special appeal to many authors and readers.