Deep learning for P-wave arrival picking in earthquake early warning

Fast and accurate P-wave arrival picking significantly affects the performance of earthquake early warning (EEW) systems. Automated P-wave picking algorithms used in EEW have encountered problems of falsely picking up noise, missing P-waves and inaccurate P-wave arrival estimation. To address these issues, an automatic algorithm based on the convolution neural network (DPick) was developed, and trained with a moderate number of data sets of 17,717 accelerograms. Compared to the widely used approach of the short-term average/long-term average of signal characteristic function (STA/LTA), DPick is 1.6 times less likely to detect noise as a P-wave, and 76 times less likely to miss P-waves. In terms of estimating P-wave arrival time, when the detection task is completed within 1 s, DPick's detection occurrence is 7.4 times that of STA/LTA in the 0.05 s error band, and 1.6 times when the error band is 0.10 s. This verified that the proposed method has the potential for wide applications in EEW.