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The availability of a tremendous amount of seismic data demands seismologicalresearchers to analyze seismic phases efficiently. Recently, deep learningalgorithms exhibit a powerful capability of detecting and picking on P- andS-wave phases. However, it is still a challenge to process teleseismic phasesfast and accurately. In this study, we detect and pick the PcP and PKiKP phasesfrom a Hinet dataset with 7386 seismograms by applying a deep-learning-basedscheme. The scheme consists of three steps: first, we prepare latent phasedata, which is truncated from the whole seismogram with the theoretical arrivaltime; second, we identify and evaluate the latent phase via a convolutionalneural network; third, we pick the first break of good or fair phase with afully convolutional neural network. The detection result shows that the schemerecognizes 92.15% and 94.13% of PcP and PKiKP phases. The picking result has anabsolute mean error of 0.0742 s and 0.0636 s for the PcP and PKiKP phases,respectively. The performance of the picking algorithm is compared with thetraditional approach of STA/LTA. The scheme processes all 7386 seismogramsapproximately in 2 hours, especially only cost about five minutes on the lasttwo steps.

Applying deep learning to teleseismic phase detection and picking: PcP and PKiKP cases