基于改进U-Net的煤层断层识别方法OA

Faults are among the key geological factors that induce major hazards such as water inrushes,gas out-bursts,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 in-telligent and precision coal mining.Therefore,a method for identifying small coal seam faults based on an im-proved 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 struc-tures 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 robust-ness 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 pro-moting the intelligent upgrading of geological hazard prevention in coal mines.