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Mineral Prospectivity Prediction by Integration of Convolutional Autoencoder Network and Random Forest

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Abstract

The convolutional neural networks used widely in mineral prospectivity prediction usually perform mixed feature extraction for multichannel inputs. This results in redundant features and impacts further improvement of predictive performance. To solve this limitation, this paper utilized convolutional autoencoder networks to mine latent high-level features for each predictor variable in parallel to prevent the mixing of features and to enhance the feature mapping outcomes of each channel. Convolutional autoencoder networks, as unsupervised learning methods, can handle input images with high-dimensional features. They can train samples without differences such that the reconstructed outputs can restore inputs as accurately as possible to reduce the extraction of irrelevant feature information. Moreover, convolutional autoencoder networks focus on finding the fewest features for representing all inputs, and the extracted features express internal spatial relations at a high level. It is helpful to improve the performance of metallogenic prediction. Hence, this paper arranged these obtained features into one-dimensional vectors to establish the inputs of classifiers. Through modeling with four classifiers (logistic regression, support vector machine, artificial neural network, and random forest), we achieved different models for mineral prospectivity prediction. According to the comprehensive evaluations, the random forest model outperformed the other models. Taking the prediction of gold deposits in the Fengxian region of Southern Qinling in China as an example, the predictive capability of the proposed integrated method was shown to be effective and reliable. The predicted high-potential areas can provide significant guidance for gold deposit exploration in the study area.

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Availability of Data and Materials

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-CAE 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: 48 Kb. The source files can be downloaded from Github: https://github.com/yangna815/Mineral-Prospectivity-Prediction-by-CAE.

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Acknowledgments

No conflicts of interest exist in this article, and this article has been approved by all authors for publication. This research was supported by the research on intelligent mineral prospectivity prediction of gold deposits in Fengtai ore cluster area, Shaanxi Province, China (2021KJXX-87), the study on 3D intelligent prediction of gold deposit resources in Pangjiahe, Fengxian County, Shaanxi Province, China (Grant No. 202103), the study on gold 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).

Funding

This research was funded by the research on intelligent mineral prospectivity prediction of gold deposits in Fengtai ore cluster area, Shaanxi Province, China (2021KJXX-87), the study on 3D intelligent prediction of gold deposit resources in Pangjiahe, Fengxian County, Shaanxi Province, China (Grant No. 202103), the study on gold 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 Center of Mineral Geological Survey, Xi’an, 710068, China

    Zhenkai Zhang

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

    Zhenkai Zhang

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

    Zenglin Hong

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  1. Na Yang
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Contributions

All authors contributed to the study conception and design. Material preparation and data collection were performed by Zhenkai Zhang. The development and the testing of the presented method were performed by Na Yang. The first draft of the manuscript was written by Na Yang and Zhenkai Zhang. The review of the manuscript was completed by Jianhua Yang and Zenglin Hong. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Jianhua Yang or Zenglin Hong.

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Yang, N., Zhang, Z., Yang, J. et al. Mineral Prospectivity Prediction by Integration of Convolutional Autoencoder Network and Random Forest. Nat Resour Res 31, 1103–1119 (2022). https://doi.org/10.1007/s11053-022-10038-7

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