Self-Supervised Convolutional Clustering for Picking the First Break of Microseismic Recording

Accurate first break picking is essential for tunnel microseismic monitoring. Here, we propose a self-supervised convolutional clustering picking (SCCP) method for automatically picking the first break of microseismic recordings. The time-frequency features are decomposed and reconstructed using accurate convolutional encoding and decoding under self-supervision. Then, the autoencoder output is unsupervisedly clustered into useful and invalid waveform sections employing the fuzzy c -means (FCMs) algorithm under long short-term memories, global attention, and self-attention constraints. Furthermore, the first point of the useful waveform is determined as the first break. Our results demonstrate that the proposed SCCP method outperforms the short-term average/long-term average (STA/LTA) and Akaike information criterion (AIC). Compared with PhaseNet, a supervised deep-learning method, the SCCP, produces similar performance without using human-labeled data. Practically, when the signal-to-noise ratio (SNR) is reduced to -6 dB, the average mean absolute error and standard deviation of the picking results remain at 1.12 and 9.19 ms, respectively.