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Toward Data-Driven Mineral Prospectivity Mapping from Remote Sensing Data Using Deep Forest Predictive Model

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Abstract

Remote sensing data prove to be an effective resource for constructing a data-driven predictive model of mineral prospectivity. Nonetheless, existing deep learning models predominantly rely on neural networks that necessitate a substantial number of samples, posing a challenge during the early stages of exploration. In order to predict mineral prospectivity using remotely sensed data, this study introduced deep forest (DF), a non-neural network deep learning model. Mainly based on ASTER multispectral imagery supplemented by Sentinel-2 and geological data, gold ore in Hamissana area, NE Sudan was used to test the DF predictive model capability. In addition to four geological-based evidential layers, 20 remote sensing-based evidential layers were generated using remote sensing enhancing techniques, forming the predictor variables of the proposed model. The applicability of the DF was thoroughly examined including its accuracy for delineating prospective areas, sensitivity to amount of training samples, and adjustment of hyperparameters. The results demonstrate that DF model outperformed conventional machine learning models (i.e., support vector machine, artificial neural network, and random forest) with AUC of 0.964 and classification accuracy of 93.3%. Moreover, the sensitivity analysis demonstrated that the DF model can be trained with a limited number (i.e., < 15) of mineral occurrences. Therefore, the DF algorithm has great potential and proves to be a viable solution for data-driven prospectivity mapping, particularly in scenarios with data availability constraints.

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Acknowledgments

We express our gratitude to the editors and anonymous reviewers for their insightful feedback on how to make our work better. The funding for this study was provided by the National Natural Science Foundation of China and the Science and Technology Strategic Prospecting Project of Guizhou Province.

Funding

The National Natural Science Foundation of China (42372345, 42172333) and the Guizhou Province's Science and Technology Strategic Prospecting Project ([2022] ZD003) provided funding for this work.

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Authors and Affiliations

  1. School of Computer Science, China University of Geosciences, Wuhan, 430074, China

    Abdallah M. Mohamed Taha, Gang Liu, Qiyu Chen, Wenyao Fan, Zhesi Cui, Xuechao Wu & Hongfeng Fang

  2. Guizhou Key Laboratory for Strategic Mineral Intelligent Exploration, Guiyang, 550081, China

    Gang Liu, Qiyu Chen, Wenyao Fan, Zhesi Cui, Xuechao Wu & Hongfeng Fang

  3. Key Laboratory of Resource Quantitative Assessment and Geoscience Information, Ministry of Natural Resources, Wuhan, 430074, China

    Abdallah M. Mohamed Taha, Gang Liu, Qiyu Chen, Wenyao Fan, Zhesi Cui, Xuechao Wu & Hongfeng Fang

  4. Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software, Ministry of Education, Wuhan, 430074, China

    Gang Liu, Qiyu Chen, Wenyao Fan, Zhesi Cui, Xuechao Wu & Hongfeng Fang

Authors
  1. Abdallah M. Mohamed Taha
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  2. Gang Liu
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  3. Qiyu Chen
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  4. Wenyao Fan
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  5. Zhesi Cui
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  6. Xuechao Wu
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  7. Hongfeng Fang
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Corresponding author

Correspondence to Gang Liu.

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Mohamed Taha, A.M., Liu, G., Chen, Q. et al. Toward Data-Driven Mineral Prospectivity Mapping from Remote Sensing Data Using Deep Forest Predictive Model. Nat Resour Res 33, 2407–2431 (2024). https://doi.org/10.1007/s11053-024-10387-5

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