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.

快速准确的P波到达采集会严重影响地震预警（EEW）系统的性能。EEW中使用的自动P波拾取算法遇到了错误地拾取噪声，P波丢失和P波到达估计不准确的问题。为了解决这些问题，开发了一种基于卷积神经网络（DPick）的自动算法，并使用了数量适中的17,717个加速度图数据集对其进行了训练。与广泛使用的信号特征函数的短期平均值/长期平均值方法（STA / LTA）相比，DPick将噪声检测为P波的可能性要低1.6倍，而错过P的可能性则要低76倍-波浪。在估计P波到达时间方面，当检测任务在1 s内完成时，DPick的检测发生率为0时STA / LTA的7.4倍。误差带为05 s，误差带为0.10 s时为1.6倍。这证明了所提出的方法具有在EEW中广泛应用的潜力。