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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In order to grasp the laws of dust production from coal cutting and reduce dust production at source by optimizing cutting parameters, the cutting experiments of different brittle coals under different cutting parameters were carried out. Based on the self-developed dust generation simulation platform of road header, the effects of coal brittleness, conical pick top angle, rotational speed, and drilling speed on dust generation were studied, and the main control factors affecting dust generation were analyzed, so as to propose the suitable parameter matching scheme for different brittle coal. The research results can provide an important scientific basis for the premining evaluation of dust production characteristics of mines and the formulation of coal dust control engineering.

The evaluation of particle saltation velocity is vital for the design of dilute negative phase pneumatic conveying systems. Nonetheless, the particle saltation velocity in dilute negative phase pneumatic conveying is largely understudied, it also lacks a consistent correlation over operating conditions and particle properties. As such, this paper investigates the effects of solid-gas ratio, particle size, and drill pipe rotational speed on particle saltation velocity in the drill pipe via CFD-DEM (computational fluid dynamics-discrete element method) simulation. Consequently, the particle saltation velocity increased gradually seemingly leaning toward a constant value with the increase of solid-gas ratio. The particle saltation velocity with larger particle size under similar solid-gas ratio was higher, while the speed of the drill pipe and the particle saltation velocity showed a negative correlation. The correlation for particle saltation velocity was established by dimension analysis method geared toward accurately predicting the particle saltation velocity in dilute negative phase pneumatic conveying. A comparison of the new correlation and the experimental data found that the error between the predicted value of the correlation and the measured particle saltation velocity is within 20% and the correlation derived by the dimensional analysis method give the well agreement.