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<p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年4月21日

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  • Yunfeng Ge,
  • Geng Liu,
  • Haiyan Wang,
  • Huiming Tang,
  • Binbin Zhao

摘要

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.