Digitalization of Rock Fracture Signal Identification From Tunnel Microseismic Data

Microseismic monitoring technology serves as an effective means of providing early warning signs of rockburst, a type of disaster that poses a serious threat to life safety in tunnels. Given the substantial number of signals received during monitoring, manual rock fracture signal identification is a time-consuming and effort-intensive task. Therefore, automatic and robust digitalization of manual signal identification is urgently needed. This paper introduces a novel method for identifying rock fracture signals based on waveform characteristics. This method simulates and digitalizes the manual observation of microseismic signals. We simplify the waveform identification process to three automatic steps. The proposed method is tested on data from the Jinping Tunnel and Bayu Tunnel, which were excavated by a TBM and by drilling and blasting methods, respectively. The rock fracture signal recognition accuracies are 95.64% and 93.92%, with false-negative rates of 3.99% and 4.31%, respectively. Two field cases demonstrate that the digital identification method is fully automated and practical for MS monitoring in tunnels.