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A Framework for Data-Driven Mineral Prospectivity Mapping with Interpretable Machine Learning and Modulated Predictive Modeling

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Abstract

Although mineral prospectivity modeling (MPM) has undergone decades of development, it has not yet been widely adopted in the global mineral exploration industry. Exploration geoscientists encounter challenges in understanding the internal working of many mineral prospectivity models due to their black box nature. Besides, their predictive results usually delineate undesirably large high-prospectivity areas, which are biased toward existing deposits, making MPM impractical. However, there are only a few data-driven methods for MPM that address both the interpretability of black box models and the issue of bias in high prospective areas, which may result from the intrinsic stochastic uncertainty of training samples, particularly toward well-known deposits. In this study, we construct and demonstrate a framework to improve the performance and reliability of data-driven MPM in the Qulong–Jiama mineral district of Tibet. Firstly, the mineral systems concept was applied to select appropriate targeting criteria and to derive corresponding evidential features. Secondly, model-agnostic methods, such as permutation feature importance, partial dependence plot, individual conditional expectation plot, and Shapely values, were applied to interpret the machine learning models. Finally, modulated prediction models and the spatial pattern of linked uncertainties were generated by an ensemble method that combines bootstrapping and the Random Forest algorithm. The final exploration targets, which were demarcated by cells with high modulated values and low uncertainties obtained by 50 predictive models, account for just ~ 3% of the study area.

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Acknowledgments

We would like to express our sincere gratitude to the Associate Editor Prof. Renguang Zuo and two anonymous reviewers for their valuable comments. This research received support from the 2021 Graduate Innovation Fund Project of China University of Geosciences, Beijing (Grant No. ZD2021YC048), National Key Research and Development Program of China (Grant No. 2022YFC2903604), Technology Innovation Center for Exploration and Exploitation of Strategic Mineral Resources in Plateau Desert Region, Ministry Resources (Grant No. KFKT20230102), and the “Deep-time Digital Earth” Science and Technology Leading Talents Team Funds for the Central Universities for the Frontiers Science Center for Deep-time Digital Earth, China University of Geosciences (Beijing) (Fundamental Research Funds for the Central Universities; Grant No. 2652023001).

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Authors and Affiliations

  1. School of Earth Sciences and Resources, China University of Geosciences, Beijing, 100083, China

    Nini Mou, Gongwen Wang & Xiang Sun

  2. Department of Geology, Faculty of Natural and Agricultural Sciences, University of the Free State, Bloemfontein, South Africa

    Emmanuel John M. Carranza

  3. MNR Key Laboratory for Exploration Theory and Technology of Critical Mineral Resources, China University of Geosciences, Beijing, 100083, China

    Gongwen Wang

  4. Beijing Key Laboratory of Land and Resources Information Research and Development, Beijing, 100083, China

    Gongwen Wang

  5. Frontiers Science Center for Deep-time Digital Earth, China University of Geosciences (Beijing), Beijing, 100083, China

    Gongwen Wang

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  1. Nini Mou
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  2. Emmanuel John M. Carranza
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  3. Gongwen Wang
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  4. Xiang Sun
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Corresponding author

Correspondence to Gongwen Wang.

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Mou, N., Carranza, E.J.M., Wang, G. et al. A Framework for Data-Driven Mineral Prospectivity Mapping with Interpretable Machine Learning and Modulated Predictive Modeling. Nat Resour Res 32, 2439–2462 (2023). https://doi.org/10.1007/s11053-023-10272-7

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