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Prospectivity and Uncertainty Analysis of Tungsten Polymetallogenic Mineral Resources in the Nanling Metallogenic Belt, South China: A Comparative Study of AdaBoost, GBDT, and XgBoost Algorithms

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Abstract

Supervised machine learning algorithms are utilized to predict undiscovered mineral resources by analyzing the correlation between geological data and mineral deposits. The scarcity of mineralization and the uncertainty arising from the selection of training samples also the accuracy and generalization of such algorithms. This study employed the adaptive boosting (AdaBoost), gradient boosting decision tree (GBDT), and extreme gradient boosting (XgBoost) algorithms to map the prospectivity of tungsten polymetallic mineral resources in the Nanling metallogenic belt. Firstly, the under-sampling and synthetic minority oversampling technique (SMOTE) methods were used to generate training datasets. Secondly, 50 groups of training datasets were generated using under-sampling, and another 50 groups of training datasets were generated using the SMOTE method. These datasets were used to separately train different boosting algorithms in order to assess the uncertainty associated with the selection of negative samples and the generation of positive samples. Finally, the risk–return analysis was used to mitigate uncertainty, and an enhanced prediction–area (P–A) plot was proposed to evaluate the performance. The results indicate that AdaBoost is the least affected by the selection of negative samples, followed by XgBoost. The SMOTE not only enhances the performance of AdaBoost and XgBoost algorithms but it also reduces the uncertainty related to the selection of negative samples and the generation of positive samples. In addition, the enhanced P–A plot can simultaneously account for both prediction accuracy and uncertainty, making it a potential tool for model evaluation. According to the results, eight potential areas with high return and low risk have been identified as priority areas for exploration. This research not only introduces a new method for mineral prospectivity mapping and uncertainty evaluation but also provides guidance for mineral exploration in this region.

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Acknowledgments

This research is supported by the National Natural Science Foundation of China (No. 42172331), the High-Level Talent Research Start-up Project of West Anhui University (WGKQ2021063), the Key R&D Project of the Science and Technology Department of Jiangxi Province (20212BBG73045), the Training Plan for Young Science and Technology Leaders of Jiangxi Bureau of Geology (2022JXDZKJRC02), the Yingtan Science and Technology Plan (No. 20233-185656), and the Geological Exploration Project funded by Jiangxi Provincial Finance (No. 20220014). We greatly appreciate the comments and suggestions provided by the Editor-in-Chief John Carranza, the Associate Editor Professor Zuo, and two other anonymous reviewers, which significantly improved our manuscript.

Funding

This research is supported by the National Natural Science Foundation of China (No. 42172331), the High-Level Talent Research Start-up Project of West Anhui University (WGKQ2021063), the Key R&D Project of Science and Technology Department of Jiangxi Province (20212BBG73045), the Training Plan for Young Science and Technology Leaders of Jiangxi Bureau of Geology (2022JXDZKJRC02), the Yingtan Science and Technology Plan (No. 20233-185656), and the Geological Exploration Project funded by Jiangxi Provincial Finance (No. 20220014).

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Authors and Affiliations

  1. School of Environment and Tourism, West Anhui University, Lu’an, 237012, China

    Tongfei Li, Qiankun Liu & Taotao Li

  2. The Tenth Geological Brigade of Jiangxi Geological Bureau, Jiangxi Geological Bureau, No. 36 in the Zhanjiang Road, Yingtan, 335001, China

    Yongpeng Ouyang

  3. School of Earth Resources, China University of Geosciences, No. 388 in the Lumo Road, Wuhan, 430074, China

    Qinglin Xia

  4. Yingtan Key Laboratory of Exploration and Research of Scarce and Advantage Minerals, The Tenth Geological Brigade of Jiangxi Geological Bureau, No. 36 in the Zhanjiang Road, Yingtan, 335001, China

    Tongfei Li, Yongpeng Ouyang & Runling Zeng

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  1. Tongfei Li
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  2. Qinglin Xia
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Correspondence to Qinglin Xia or Yongpeng Ouyang.

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Li, T., Xia, Q., Ouyang, Y. et al. Prospectivity and Uncertainty Analysis of Tungsten Polymetallogenic Mineral Resources in the Nanling Metallogenic Belt, South China: A Comparative Study of AdaBoost, GBDT, and XgBoost Algorithms. Nat Resour Res (2024). https://doi.org/10.1007/s11053-024-10321-9

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