| Citation: |
HU Zhazha，ZHANG Xun，JIN Yi，et al. A method for intelligent information extraction of coal fractures based on µCT and deep learning[J]. Coal Geology & Exploration，2025，53(2)：55−66. DOI: 10.12363/issn.1001-1986.24.09.0609
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The fine-scale characterization of fractures in coal reservoirs is significant for the exploration and exploitation of coalbed methane (CBM) resources. Given that the size, orientation, and density of fractures directly affect the permeability of coal seams, the accurate information identification and extraction of fractures in coal seams plays a key role in revealing the formation and propagation mechanisms of fracture networks during reservoir volume fracturing. Conventional methods for fracture information extraction typically rely on manual labeling and feature extraction based on image processing techniques, exhibiting significantly limited accuracy and efficiency.
This study proposed a method for fracture information extraction of coals based on TransUNet and micro-computed tomography (µCT) images. TransUNet, integrating the advantages of both the Transformer modules and convolutional neural network (CNN), is capable of extracting global features and capturing local details in images, significantly enhancing the image segmentation accuracy and network robustness. First, the µCT images of coal samples were preprocessed, including improving the image quality using the difference method and increasing the sample size using data augmentation techniques. Subsequently, image segmentation was conducted using TransUNet to extract fracture features. Additionally, the image segmentation results of varying neural network models were compared.
The results indicate that the proposed method exhibited superior performance on a given dataset. Specifically, the TransUNet model yielded an accuracy of 91.3%, precision of 89.5%, F1 score of 89.8%, and Intersection over Union (IoU) of 84.0%, significantly outperforming other intelligent models like U-Net and U-Net++. Given the characteristics of fine-grained µCT images, applying TransUNet to the fracture information extraction of coals emerges as an efficient and accurate approach. This study provides a novel philosophy for image processing in the field of CBM exploration and production.
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