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Mineralized-Anomaly Identification Based on Convolutional Sparse Autoencoder Network and Isolated Forest

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Abstract

According to the characteristic that mineralized-anomaly samples have larger reconstruction errors, traditional autoencoder networks have been applied widely in mineralized-anomaly identification. However, they easily ignore spatial coupling information of multi-source ore-indicating factors have lower generalization abilities caused by excessive feature redundancies, and rely on known non-mineralization samples when they are utilized in mineralized-anomaly identification. This paper utilizes a convolutional sparse autoencoder network for realizing mineralized-anomaly identification. The proposed method retains the extraction of spatial coupling correlations of geological and geochemical variables through the addition of convolutional operations, learning the relationship between mineralized-features and the locations of mineralization, and being conducive to analyzing results with geological structures. The establishment of sparse terms of using ReLU activation functions and adding sparsity constraints into the objective loss function improves the generalization ability of the whole network through suppression of several feature units to reduce redundant features. Moreover, the isolated forest is employed as an autonomous extractor of background multichannel image samples, overcoming the limitation of traditional autoencoder networks that rely on labeled non-mineralization samples. The integration of convolutional sparse autoencoder network and isolated forest can accurately predict more known mineral deposits (68.96%) in smaller prospective areas (16.77%) in the Fengxian District, Shaanxi Province, China. The obtained mineralized-anomaly map reveals that most of the known mineralization is distributed in the delineated areas of larger reconstruction errors, demonstrating that this approach can effectively identify mineralized-anomalies without relying on prior knowledge.

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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 paper is confidential.

Code Availability

Name of code: Mineral-Prospectivity-Prediction-by-CSAE.

Developer and contact details: Na Yang, School of Automation, Northwestern Polytechnical University, 710029, China; e-mail: yangnalys@mail.nwpu.edu.cn.

Available time: three months.

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: 32 Kb.

The source files can be downloaded from Github: https://github.com/yangna815/Mineral-Prospectivity-Prediction-by-CSAE.

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Acknowledgments

This research was supported by the research on intelligent mineral prospectivity prediction of Au deposits in Fengtai ore cluster area, Shaanxi Province, China (2021KJXX-87), the study on 3D intelligent prediction of Au deposit resources in Pangjiahe, Fengxian County, Shaanxi Province, China (Grant No. 202103), the study on Au metallogenic regularity and prospecting direction in Fengtai ore cluster area, Shaanxi Province, China (Grant No. 201918), and the construction of geoscience big data in Shaanxi Province (No. 20180301).

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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

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

    Na Yang, Zhenkai Zhang, Jianhua Yang & Zenglin Hong

  3. Shaanxi Geological and Intelligent Remote Sensing Application Innovation Center, Xi’an, 710072, China

    Na Yang & Jianhua Yang

  4. Shaanxi Center of Mineral Geological Survey, Xi’an, 710068, China

    Zhenkai Zhang

  5. Natural Resources Shaanxi Satellite Application Technology Center, Xi’an, 710119, China

    Zhenkai Zhang

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

    Zenglin Hong

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  1. Na Yang
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Correspondence to Zhenkai Zhang or Jianhua Yang.

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Yang, N., Zhang, Z., Yang, J. et al. Mineralized-Anomaly Identification Based on Convolutional Sparse Autoencoder Network and Isolated Forest. Nat Resour Res 32, 1–18 (2023). https://doi.org/10.1007/s11053-022-10143-7

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