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Manganese mineral prospectivity based on deep convolutional neural networks in Songtao of northeastern Guizhou

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Abstract

The world has moved into an era of hidden ore body exploration, necessitating the development of new prospecting and exploration methods. One promising approach is to use the deep convolutional neural network (DCNN) algorithm to extract spatial and correlation characteristics of multiple two-dimensional elements related to hidden ores. This paper explores this method on Datangpo manganese (Mn), constructing prediction datasets that includes geological, geochemical, geophysical and aeromagnetic features. Analyzing metallogenic conditions and control factors of Mn ores, we construct a Mn ore prediction model (Geo-DCNN) based on multiple geographical knowledge and DCNN. The Geo-DCNN model reaches ore-bearing accuracy of 79.11%, non-ore-bearing accuracy of 99.01%, overall accuracy of 95.35%, and loss value of 0.0227 after training. Based on analysis of ROC curve, P-R curve, field investigation, and target area verification, we discover that the prediction results of the Geo-DCNN model in northeast Guizhou have a high correspondence rate with known manganese deposits. This provides valuable insight for further ore exploration in the area. Additionally, the results indicate that the Geo-DCNN model is robust and portable, suggesting that it can be applied to metallogenic prediction practices for manganese ore in similar regions.

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Acknowledgements

The authors would like to thank the Guangxi Bureau of Land and Resources for providing the various datasets used in this paper.

Funding

This work has been supported by the National Natural Science Foundation of China (No:41201193); Hubei Provincial Natural Science Foundation of China (No:2021CFB506); Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing (No:KLIGIP-2023-B08); Research and Development Base for Deep Prediction and Exploration Technology of Manganese Mineral Resources [2021]4027; Science and Technology Plan Project of Guizhou Province [2020]4Y039; and Science and Technology Strategic Prospecting Project of Guizhou Province [2022] ZD003 and [2022] ZD004. The authors would like to thank the anonymous reviewers for providing valuable comments on the manuscript.

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Authors and Affiliations

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

    Kai Xu, Siyuan Zhao, Chonglong Wu, Changyu Yang & Chunfang Kong

  2. Hubei Key Laboratory of Intelligent Geo-Information Processing, Wuhan, 430074, China

    Kai Xu & Chunfang Kong

  3. Technology Innovation Center of Mineral Resources Explorations Engineering in Bedrock Zones, Ministry of Natural Resources, Guiyang, 550081, China

    Kai Xu, Chonglong Wu & Chunfang Kong

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

    Kai Xu

  5. Wuhan Dida Quanty Science & Technology Co., Ltd., Wuhan, 430205, China

    Kai Xu, Chonglong Wu, Yan Li, Yang Dong, Yongjin Wu, Shize Xiang & Chunfang Kong

  6. 103 Geological Team, Guizhou Geological and Mineral Exploration Bureau, Tongren, 554300, China

    Sui Zhang & Liangjun Yuan

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  1. Kai Xu
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Contributions

The first author contribution statement: Kai Xu: Conceptualization, Methodology, Investigation, Writing—original draft, Writing—review & editing. Siyuan Zhao: Material preparation, Methodology, Software. Chonglong Wu, Sui Zhang, and Liangjun Yuan: Methodology, Investigation, and Supervision. Changyu Yang: Software, Data collection and analysis. Yan Li, Yang Dong, Yongjin Wu, and Shize Xiang: Software and Data collection. Credit author: Chunfang Kong: Investigation, Writing—original draft, Writing—review & editing, Supervision. All authors read and approved the final manuscript.

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Correspondence to Chunfang Kong.

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Xu, K., Zhao, S., Wu, C. et al. Manganese mineral prospectivity based on deep convolutional neural networks in Songtao of northeastern Guizhou. Earth Sci Inform 17, 1681–1697 (2024). https://doi.org/10.1007/s12145-024-01224-7

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