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Data-driven Mineral Prospectivity Mapping by Joint Application of Unsupervised Convolutional Auto-encoder Network and Supervised Convolutional Neural Network

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Abstract

The excellent performance of convolutional neural network (CNN) and its variants in image classification makes it a potential perfect candidate for dealing with multi-geoinformation involving abundant spatial information. In this paper, we tested, for data-driven mineral prospectivity mapping, the efficacy of using unsupervised convolutional auto-encoder network (CAE) to support CNN modeling for synthesis of multi-geoinformation. First, two simple unsupervised CAE networks were constructed to distinguish patches of tif image (i.e., nine predictive evidence maps forming a tif-format image) with nine channels that have high reconstructed errors, which represent prospective areas (i.e., mineralized). Then, the patches of tif image with the lowest reconstructed errors were regarded as background (or non-prospective areas). We varied the CAE network architecture and training epochs and combinations of evidence maps for trials to obtain reliable results. Then, the AUC, or area under the receiver operating characteristic curve, was used to demonstrate empirically that high reconstructed errors are representative of spatial signatures of prospective areas. The proposed coherent spatial signatures, namely patches of a tif image with the highest reconstructed errors and the lowest reconstructed errors representing prospective and non-prospective areas, respectively, were used in the subsequent CNN modeling. The results of CNN modeling using training data derived from CAE exhibited strong spatial correlation with known Au deposits in the study area. The training loss and accuracy of the CNN modeling together with resulting favorability map that were comparable with results from previous study proved the plausibility of the proposed methodology, and therefore, the practice of extracting coherent spatial signatures of prospective and non-prospective areas in unsupervised manner using CAE network and then using these coherent spatial signatures in supervised learning with CNN is a new potential approach for mineral prospectivity mapping.

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Acknowledgments

Funding support for this research was derived from the National Key Research and Development Program of China (Project No. 2017YFC0601501), The China National Mineral Resources Assessment Initiative (Project Nos. 1212010733806 and 1,212,011,120,140) and China Scholarship Council (CSC No. 201906400022).

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Authors and Affiliations

  1. School of Earth Sciences and Resources, China University of Geosciences Beijing, 29 Xueyuan Road, Beijing, 100083, China

    Shuai Zhang, Fan Yang, Shihong Zhang & Yang Xu

  2. School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Westville Campus, Durban, South Africa

    Shuai Zhang & Emmanuel John M. Carranza

  3. MlR Key Laboratory of Metallogeny and Mineral Resource Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing, 100037, China

    Hantao Wei, Keyan Xiao, Jie Xiang, Shihong Zhang & Yang Xu

  4. Key Laboratory of Mineral Resources in Western China (Gansu Province), School of Earth Sciences, Lanzhou University, Lanzhou, 730000, China

    Fan Yang

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  1. Shuai Zhang
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Zhang, S., Carranza, E.J.M., Wei, H. et al. Data-driven Mineral Prospectivity Mapping by Joint Application of Unsupervised Convolutional Auto-encoder Network and Supervised Convolutional Neural Network. Nat Resour Res 30, 1011–1031 (2021). https://doi.org/10.1007/s11053-020-09789-y

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