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A Convolutional Neural Network of GoogLeNet Applied in Mineral Prospectivity Prediction Based on Multi-source Geoinformation

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Abstract

The traditional convolutional neural networks applied in mineral prospectivity mapping usually extract features from only one scale at each iteration, resulting in plain features. To combat this limitation, this study utilized a convolutional neural network based on GoogLeNet to predict prospectivity for gold deposits in the Fengxian study area, China. The GoogLeNet adopted four groups of convolution kernels to extract and integrate features from multiple scales, obtaining abundant and comprehensive features related to mineralization. According to a multi-source geoinformation analysis, we selected 11 exploration criteria, including three geological factors (NW-trending brittle-ductile faults, NE-trending brittle faults, and anticline axes) and eight geochemical exploration data layers (Au, Ag, As, Hg, Pb, Zn, Cu, and Sb). Then, we created predictor samples to train the models to mine evidential features. Following to a comprehensive analysis, we formed a fusion model of GoogLeNet for mineral prospectivity modeling. The results demonstrated that the fusion model achieved an optimized predictive accuracy of 93.1% and an area under curve of 0.968. This fusion model outperformed the other models with superior success rate and prediction area rate performances, capturing 72% of the known gold deposits in just 27.3% of the research area. The results indicate the effectiveness of GoogLeNet in mineral prospectivity mapping. Finally, we classified the Fengxian district into three areas according to their different mineral prospectivity. The high-prospectivity areas provide significant implications for further exploration of gold deposits in the study area.

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Availability of data and material

The datasets analyzed during the current study are not publicly available because the exploration data used in this study are confidential.

Code availability

Name of code: Mineral-Prospectivity-Prediction. Developer and contact details: Na Yang, School of Automation, Northwestern Polytechnical University, 710029, China; e-mail: yangnalys@mail.nwpu.edu.cn. Year first available: 2021. Hardware required: a computer with 1.8 GHz and 16 GB. Software required: Python Spyder and torch, scikit-learn and numpy packages. Program language: The code is written in Python 3.6. Program size: 38.1 kb. The source files can be downloaded from GitHub: https://github.com/yangna815/Mineral-Prospectivity-Prediction

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Acknowledgments

This research was funded by the study on gold metallogenic regularity and prospecting direction in Fengtai deposit concentration area, Shaanxi Province, China (Grant No. 201918), the study on 3D intelligent prediction of gold deposit resources in Pangjiahe, Fengxian County, Shaanxi Province, China (Grant No. 202103), the research on intelligent mineral prospectivity prediction of gold deposits in Fengtai deposit concentration area, Shaanxi Province, China (2021KJXX-87), and the key research and development plan of Shaanxi Province, China (2020GY-143)

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Authors and Affiliations

  1. School of Automation, Northwestern Polytechnical University, Xi’an, 710072, China

    Na Yang, Zhenkai Zhang, Jianhua Yang, Zenglin Hong & Jing Shi

  2. Research Center of Intelligent Geological Survey, Northwestern Polytechnical University, Xi’an, 710072, China

    Na Yang, Zhenkai Zhang, Jianhua Yang, Zenglin Hong & Jing Shi

  3. Research Center of Intelligent Geological Survey, Shaanxi Center of Mineral Geological Survey, Xi’an, 710068, China

    Zhenkai Zhang

  4. Shaanxi Institute of Geological Survey, Xi’an, 710054, China

    Zenglin Hong

  5. School of Earth Science and Land Resource, Chang’an University, Xi’an, 710054, China

    Zenglin Hong

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  1. Na Yang
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Correspondence to Jianhua Yang or Zenglin Hong.

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Yang, N., Zhang, Z., Yang, J. et al. A Convolutional Neural Network of GoogLeNet Applied in Mineral Prospectivity Prediction Based on Multi-source Geoinformation. Nat Resour Res 30, 3905–3923 (2021). https://doi.org/10.1007/s11053-021-09934-1

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