PickCapsNet: Capsule Network for Automatic P-Wave Arrival Picking

Microseismic monitoring is an effective technique to ensure the safety of rock mass engineering. Moreover, P-wave arrival picking is crucial in the seismic/microseismic monitoring process. The existing methods of P-wave arrival picking are not fully qualified for practical application because they are mostly semiautomatic or need too much training data. To overcome the shortcoming of today's most elaborate methods, we leverage the recent advances in artificial intelligence and present PickCapsNet, a highly scalable capsule network for P-wave arrival picking from a single waveform without feature extraction. We apply the PickCapsNet to study the induced microseismic events in Dongguashan Copper Mine, China, and compare it with Akaike information criterion (AIC), short- and long-time average ratio (STA/LTA), and convolutional neural network (CNN). The differences between the PickCapsNet and manual picks have a mean value of 0.0023 s and a standard deviation of 0.0033 s; moreover, 97.46% of the picks are within 0.01 s of the manual pick. Furthermore, at different signal-to-noise ratios (SNRs), it has a higher accuracy and stability than other methods. These results indicate that the proposed method is of high picking precision and robustness.

Automated summary

PickCapsNet is a scalable capsule network for P-wave arrival picking without feature extraction, employing recent advances in artificial intelligence. The method demonstrates high accuracy and stability in the identification of P-wave arrival times, outperforming other methods in practical applications.