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融合多尺度特征与注意力机制的地震地表裂缝智能识别与特征分析:以2025年西藏定日MS6.8地震为例
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PDF(14309 KB)
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Coseismic surface fractures triggered by earthquakes are of significant importance for understanding fault activity, seismic structural characteristics, and post-earthquake disaster assessment.This study combines high-resolution unmanned aerial vehicle (UAV) data and deep learning techniques to automatically identify and analyze the surface fracture characteristics of the 2025 M S6.8 earthquake in Dingri, Xizang, further revealing the surface fracture strike pattern and validating it through comparison with InSAR deformation data. Based on high-resolution images obtained by low-altitude UAVs, the ResPSP-CBAM model was used for intelligent recognition, successfully extracting the surface fracture distribution in the post-earthquake area. The ResPSP-CBAM model integrates the ResUNet residual structure, Pyramid Scene Parsing (PSP) module, and Convolutional Block Attention Mechanism (CBAM), significantly improving the accuracy and robustness of crack detection.The analysis indicates that the ResPSP-CBAM model performs excellently in accuracy, precision, recall, and F1 score, with respective values of 0.927, 0.829, 0.779, and 0.802. The identified surface fracture trends are highly consistent with the surface deformation directions interpreted from InSAR, further validating the effectiveness of this method.The ResPSP-CBAM deep learning model constructed in this study significantly improves the accuracy and efficiency of intelligent identification of seismic surface fractures. The identified surface fractures include both primary and secondary types, predominantly featuring primary fractures induced by fault ruptures. These fractures generally exhibit a north-south strike orientation, which aligns closely with the strike direction of the Dengmoco fault zone. This indicates that the surface fractures in the study area are closely associated with fault activity. This research provides a novel technical approach for intelligent identification of earthquake-induced surface fractures, offering robust support for understanding structural characteristics of seismic source faults, and delivering critical scientific evidence for earthquake prediction, early warning systems, and post-earthquake hazard assessments.
surface fracture / Dingri M S6.8 earthquake / UAV / InSAR / deep learning / ResPSP-CBAM / disasters
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