Chunchi Ma, Weihao Xu, Xuefeng Ran, Tianbin Li, Hang Zhang, Dongwei Xing
Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events. Rapid interpretation of microseismic signals is crucial for the timely identification of rockbursts. However, conventional processing encompasses multi-step workflows, including classification, denoising, picking, locating, and computational analysis, coupled with manual intervention, which collectively compromise the reliability of early warnings. To address these challenges, this study innovatively proposes the "microseismic stethoscope"— a multi-task machine learning and deep learning model designed for the automated processing of massive microseismic signals. This model efficiently extracts three key parameters that are necessary for recognizing rockburst disasters: rupture location, microseismic energy, and moment magnitude. Specifically, the model extracts raw waveform features from three dedicated sub-networks: a classifier for source zone classification, and two regressors for microseismic energy and moment magnitude estimation. This model demonstrates superior efficiency compared to traditional processing and semi-automated processing, reducing per-event processing time from 0.71 seconds and 0.49 seconds to merely 0.036 seconds. It concurrently achieves 98% accuracy in source zone classification, with microseismic energy and moment magnitude estimation errors of 0.13 and 0.05, respectively. This model has been well applied and validated in the Daxiagu Tunnel case in Sichuan, China. The application results indicate that the model is as accurate as traditional methods in determining source parameters, and thus can be used to identify potential geomechanical processes of rockburst disasters. By enhancing the signal processing reliability of microseismic events, the proposed model in this study presents a significant advancement in the identification of rockburst disasters.