Explainable artificial intelligence models for mineral prospectivity mapping

Explainable artificial intelligence models for mineral prospectivity mapping

September 2024 Vol.67 No.9: 2864–2875 | Renguang ZUO1*, Qiuming CHENG1,2, Ying XU1, Fanfan YANG1, Yihui XIONG1, Ziye WANG1 & Oliver P. KREUZER3,4
The article "Explainable Artificial Intelligence Models for Mineral Prospectivity Mapping" by Renguang Zuo et al. addresses the challenges of using AI in mineral prospectivity mapping (MPM) and proposes a novel workflow to enhance the interpretability and transparency of AI-driven MPM models. The authors highlight that while AI algorithms have shown excellent performance in MPM, they often suffer from poor generalizability, interpretability, and physical inconsistencies. To address these issues, they introduce a framework that integrates domain knowledge throughout the AI-driven MPM process, from data preprocessing to model design and output. This approach aims to improve the interpretability of the models by incorporating geological and conceptual insights, thereby enhancing the reliability and decision-making capabilities in mineral exploration. The study emphasizes the importance of feature engineering, data augmentation, and the use of visualization techniques to understand the underlying processes and the contribution of input variables to the formation of mineral deposits. Overall, the development of explainable AI (XAI) models for MPM is seen as a promising area for future research to improve the practical application of AI in mineral exploration.The article "Explainable Artificial Intelligence Models for Mineral Prospectivity Mapping" by Renguang Zuo et al. addresses the challenges of using AI in mineral prospectivity mapping (MPM) and proposes a novel workflow to enhance the interpretability and transparency of AI-driven MPM models. The authors highlight that while AI algorithms have shown excellent performance in MPM, they often suffer from poor generalizability, interpretability, and physical inconsistencies. To address these issues, they introduce a framework that integrates domain knowledge throughout the AI-driven MPM process, from data preprocessing to model design and output. This approach aims to improve the interpretability of the models by incorporating geological and conceptual insights, thereby enhancing the reliability and decision-making capabilities in mineral exploration. The study emphasizes the importance of feature engineering, data augmentation, and the use of visualization techniques to understand the underlying processes and the contribution of input variables to the formation of mineral deposits. Overall, the development of explainable AI (XAI) models for MPM is seen as a promising area for future research to improve the practical application of AI in mineral exploration.
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