This study addresses the limitations of conventional hydraulic fracturing techniques, such as high water consumption and the potential to trigger water-lock effects. It systematically investigates the thermo-mechanical degradation behavior and fracture evolution mechanisms of low-permeability coal under the influence of cyclic heating and liquid nitrogen (LN2) cooling, a promising waterless stimulation technology. Using a combination of methods, including Brazilian splitting tests, three-point bending tests, acoustic emission (AE) monitoring, and three-dimensional (3D) surface profilometry, the progressive damage characteristics of coal's mechanical integrity over 15 cycles were revealed. Key findings include: cyclic thermal shock causes an 81.98% decrease in coal's P-wave velocity, a 63.31% reduction in tensile strength, and an 85.15% attenuation in fracture toughness, indicating significant cumulative damage due to repeated thermal shock. AE analysis confirms that microcrack nucleation and propagation intensify during the cycles, with the coal's failure mode transitioning from brittle to ductile. Fractal dimension and roughness index-based fracture surface characterization shows a significant increase in fracture network complexity with the number of cycles.

To address the challenges of coal fracture image recognition, including interference from gangue and multiscale fractures, a multiscale coal fracture segmentation network model to significantly enhance the recognition of coal fracture structures is proposed. The model significantly enhances the recognition of fracture structures based on a U-shaped network architecture and the incorporation of several advanced techniques, including transfer learning, depthwise separable atrous convolutions, and residual modules. Transfer learning, by leveraging pretrained visual geometry group 16-layer network weights, bolsters the feature extraction capabilities of an encoder. Simultaneously, the integration of depthwise separable atrous convolutions and residual modules optimizes a decoder, thereby improving segmentation accuracy and the robust recognition of fractures within images. Experimental results based on qualitative and quantitative data showed that the proposed model surpassed traditional convolutional neural networks, demonstrating proficiency in identifying multiscale fractures in complex coal images. The model was applied to the identification of fractures in roadway surrounding rock boreholes. By extracting fractures from borehole imaging videos and planar diagrams, and conducting cross-validation, the study precisely delineated the fracture distribution. Additionally, to improve coal seam gas extraction efficiency, the grouting and sealing range for cross-layer extraction boreholes was determined. Document Type: Original article Cited as: Wang, D., Li, L., Zhang, H., Li, S., Zhang, F., Xia, Y. Intelligent identification of coal fractures using an improved U-shaped network. Advances in Geo-Energy Research, 2025, 15(2): 129-141. https://doi.org/10.46690/ager.2025.02.05 References Alshammari, A. 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