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Volume 32 Issue 2
Apr 2021
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Journal of Earth Science  > 2021  >  32(2): 327-347.
Shi Li, Jianping Chen, Chang Liu, Yang Wang. Mineral Prospectivity Prediction via Convolutional Neural Networks Based on Geological Big Data. Journal of Earth Science, 2021, 32(2): 327-347. doi: 10.1007/s12583-020-1365-z
Citation: Shi Li, Jianping Chen, Chang Liu, Yang Wang. Mineral Prospectivity Prediction via Convolutional Neural Networks Based on Geological Big Data. Journal of Earth Science, 2021, 32(2): 327-347. doi: 10.1007/s12583-020-1365-z
shu

Mineral Prospectivity Prediction via Convolutional Neural Networks Based on Geological Big Data

doi: 10.1007/s12583-020-1365-z
  • 1.

    School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China

  • 2.

    Land Resources Information Development and Research Key Laboratory of Beijing, Beijing 100083, China

  • 3.

    Haikou Marine Geological Survey Center, China Geological Survey, Haikou 570100, China

More Information
  • Corresponding author: Jianping Chen, 3s@cugb.edu.cn
  • Received Date: 24 Aug 2020
  • Accepted Date: 07 Nov 2020
  • Publish Date: 01 Apr 2021
    Abstract
  • Today's era of big data is witnessing a gradual increase in the amount of data, more correlations between data, as well as growth in their spatial dimension. Conventional linear statistical models applied to mineral prospectivity mapping (MPM) perform poorly because of the random and nonlinear nature of metallogenic processes. To overcome this performance degradation, deep learning models have been introduced in 3D MPM. In this study, taking the Huayuan sedimentary Mn deposit in Hunan Province as an example, we construct a 3D digital model of this deposit based on the prospectivity model of the study area. In this approach, 3D predictor layers are converted from the conceptual model and employed in a 3D convolutional neural network (3D CNN). The characteristics of the spatial distribution are extracted by the 3D CNN. Subsequently, we divide the 22 extracted ore-controlling variables into six groups for contrast experiments based on various combinations and further apply the 3D CNN model and weight of evidence (WofE) method on each group. The predictive model is trained on the basis of the coupling correlation between the spatial distributions of the variables and the underground occurrence space of the Mn orebodies, and the correlation between different ore-controlling factors. The analysis of 12 factors indicates that the 3D CNN model performs well in the 3D MPM, achieving a promising accuracy of up to 100% and a loss value below 0.001. A comparison shows that the 3D CNN model outperforms the WofE model in terms of predictive evaluation indexes, namely the success rate and ore-controlling rate. In particular, the 1-12 ore-controlling factors selected in experiment 5 provide a significantly better prediction effect than the other factors. Consequently, we conclude that the Mn deposit in the study area is not only related to the stratum and interlaminar anomalous bodies but also to the spatial distribution of the faults. The experimental results confirm that the proposed 3D CNN is promising for 3D MPM as it eliminates the interference factors.

     

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