Zuo, Renguang, Cheng, Qiuming, Xu, Ying, Yang, Fanfan, Xiong, Yihui, Wang, Ziye, and Kreuzer, Oliver P. (2024) Explainable artificial intelligence models for mineral prospectivity mapping. Science China Earth Sciences, 67 (9). pp. 2864-2875.

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Abstract

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.

Item ID:	85562
Item Type:	Article (Research - C1)
ISSN:	1869-1797
Copyright Information:	© Science China Press 2024
Date Deposited:	21 May 2025 02:04
FoR Codes:	37 EARTH SCIENCES > 3704 Geoinformatics > 370499 Geoinformatics not elsewhere classified @ 30% 37 EARTH SCIENCES > 3705 Geology > 370508 Resource geoscience @ 70%
SEO Codes:	25 MINERAL RESOURCES (EXCL. ENERGY RESOURCES) > 2503 Mineral exploration > 250399 Mineral exploration not elsewhere classified @ 100%
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