Intelligent extraction of Micro-CT fissures in coal based on deep learning and its application

Abstract

To address the challenges of fracture recognition in the CT scanning images of coal or rock, particularly the interference of gangue and the recognition of fractures at different scales, proposed and implemented a network model for coal-rock fracture extraction based on deep learning (MCSN). According to the U-Net architecture, the model utilized its encoder-decoder structure and skip connections to segment the fracture structure images from complex coal-rock body images. Firstly, the fracture structures in the CT scanning images were annotated manually using the internal scan images captured by a coal industrial CT scanning system. And the annotated original data was augmented to create a coal-rock CT fracture dataset. Subsequently, to make the extraction network of main features have a stronger extraction capability of fracture structure features, the weights of a pre-trained VGG16 model were transferred to the U-Net encoder through a transfer learning technique. Simultaneously, the decoder part of the U-Net model was improved using the deep separable dilated convolutional modules (DCAC) and residual modules to effectively boost the recognition capability of fracture structures in the CT images, demonstrating a superior segmentation accuracy and robustness. To validate the effectiveness of the coal-rock fracture extraction network model proposed, the results obtained by the MCSN were compared with those of classical convolutional neural networks and threshold segmentation methods. Experimental comparisons revealed a significant advantage of the model proposed in both qualitative and quantitative analyses. The proposed model, employing a multi-scale fusion strategy, demonstrated the capability to effectively extract fractures in complex coal-rock images, thereby enhancing the efficiency and accuracy of fracture identification. The model was applied to the identification of fractures in roadway surrounding rock based on borehole imaging. Fracture extraction was performed through the analysis of borehole videos and planar unfolded images collected by a borehole imaging instrument. The results from both sources were cross-validated to obtain an accurate distribution of fractures in roadway surrounding rock. Furthermore, the model provides guidelines for the injection and sealing of boreholes, increasing volume fraction of coal seam gas extraction.

Keywords

• crack identification and extraction
• ct scanning
• deep learning
• convolutional neural networks
• dilated convolution