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Volume 36 Issue 4
Aug 2025
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Article Contents
Yunfeng Ge, Geng Liu, Haiyan Wang, Huiming Tang, Binbin Zhao. Rock Joint Detection from Borehole Imaging Logs Using a Convolutional Neural Networks Model. Journal of Earth Science, 2025, 36(4): 1700-1716. doi: 10.1007/s12583-024-1989-5
Citation: Yunfeng Ge, Geng Liu, Haiyan Wang, Huiming Tang, Binbin Zhao. Rock Joint Detection from Borehole Imaging Logs Using a Convolutional Neural Networks Model. Journal of Earth Science, 2025, 36(4): 1700-1716. doi: 10.1007/s12583-024-1989-5

Rock Joint Detection from Borehole Imaging Logs Using a Convolutional Neural Networks Model

doi: 10.1007/s12583-024-1989-5
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  • Corresponding author: Yunfeng Ge, geyunfeng@cug.edu.cn
  • Received Date: 16 Jun 2023
  • Accepted Date: 09 Mar 2024
  • Available Online: 05 Aug 2025
  • Issue Publish Date: 30 Aug 2025
  • To map the rock joints in the underground rock mass, a method was proposed to semi-automatically detect the rock joints from borehole imaging logs using a deep learning algorithm. First, 450 images containing rock joints were selected from borehole ZKZ01 in the Rumei hydropower station. These images were labeled to establish ground truth which was subdivided into training, validation, and testing data. Second, the YOLO v2 model with optimal parameter settings was constructed. Third, the training and validation data were used for model training, while the test data was used to generate the precision-recall curve for prediction evaluation. Fourth, the trained model was applied to a new borehole ZKZ02 to verify the feasibility of the model. There were 12 rock joints detected from the selected images in borehole ZKZ02 and four geometric parameters for each rock joint were determined by sinusoidal curve fitting. The average precision of the trained model reached 0.87.

     

  • Electronic Supplementary Materials
    Supplementary material (Table S1) is available in the online version of this article at https://doi.org/10.1007/s12583-024-1989-5.
    Conflict of Interest
    The authors declare that they have no conflict of interest.
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