Huang Yaping, Qi Xuemei, Wu Haibo, Cheng Yan, Yan Lei, Aisan Bupataimu, Dong Shouhua
Faults are among the key geological factors that induce major hazards such as water inrushes， gas outbursts， and roof falls in coal mines， and their accurate identification is a prerequisite for safe and efficient coal mining. Conventional fault interpretation methods based on post-stack seismic attributes have a relatively low identification rate for small faults with throws of 3~5 m， which has become a bottleneck severely restricting intelligent and precision coal mining. Therefore， a method for identifying small coal seam faults based on an improved U-Net is proposed. Residual structures， a convolutional block attention module （CBAM）， and an atrous spatial pyramid pooling （ASPP） module are incorporated into the classical U-Net architecture. The residual structures alleviate the vanishing-gradient problem and improve the stability of model training. The CBAM enhances the network’s ability to perceive key fault features. Moreover， the ASPP module enables the efficient capture and in-depth extraction of multi-scale information from seismic data， thus improving the accuracy and robustness of coal seam fault identification. The results obtained using a synthetic seismic test dataset， a coal seam fault model， and field seismic data demonstrate that the proposed improved U-Net model accurately identifies most coal seam faults， yielding clear fault boundaries， strong spatial continuity， and significantly improved identification accuracy. This method has important theoretical value and broad application prospects for promoting the intelligent upgrading of geological hazard prevention in coal mines.
