首页|Explainable artificial intelligence models for mineral prospectivity mapping

Explainable artificial intelligence models for mineral prospectivity mapping

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Mineral prospectivity mapping(MPM)is designed to reduce the exploration search space by combining and analyzing geological prospecting big data.Such geological big data are too large and complex for humans to effectively handle and interpret.Artificial intelligence(AI)algorithms,which are powerful tools for mining nonlinear mineralization patterns in big data obtained from mineral exploration,have demonstrated excellent performance in MPM.However,AI-driven MPM faces several challenges,including difficult interpretability,poor generalizability,and physical inconsistencies.In this study,based on previous studies,we devised a novel workflow that aims to constructing more transparent and explainable artificial intelligence(XAI)models for MPM by embedding domain knowledge throughout the AI-driven MPM,from input data to model design and model output.This newly proposed approach provides strong geological and conceptual leads that guide the entire AI-driven MPM model training process,thereby improving model interpretability and performance.Overall,the development of XAI models for MPM is capable of embedding prior and expert knowledge throughout the modeling process,presenting a valuable and promising area for future research designed to improve MPM.

Artificial intelligenceMineral prospectivity mappingGeological prospecting big dataDomain knowledgeInterpretability

Renguang ZUO、Qiuming CHENG、Ying XU、Fanfan YANG、Yihui XIONG、Ziye WANG、Oliver P.KREUZER

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State Key Laboratory of Geological Processes and Mineral Resources,China University of Geosciences,Wuhan 430074,China

School of Earth Sciences and Engineering,Sun Yat-sen University,Zhuhai 519000,China

Corporate Geoscience Group(CGSG),PO Box 5128 Rockingham Beach,WA 6969,Australia

Economic Geology Research Centre(EGRU),College of Science and Engineering,James Cook University,Townsville,QLD 4811,Australia

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2024

10.1007/s11430-024-1309-9

中国科学:地球科学(英文版)
中国科学院

中国科学:地球科学(英文版)

影响因子：1.002
ISSN：1674-7313
年,卷(期)：2024.67(9)