Experimental Study on Long Short-term Memory Networks for Identifying P-wave Primary Phase

Identifying primary phases of seismic waveforms is a routine task in seismic data processing. Owing to the low efficiency of manual identification and the influence of human subjective factors, many methods for the automatic identification of the primary phase have been developed in recent years. Most of these methods determine the arrival time based on the ratio between ambient noise and seismic signals. However, they typically require a threshold value, making their implementation in complex seismic regions and handling massive seismic data challenging. In this study, a seven-layer convolutional recurrent neural network based on long short-term memory (LSTM) network was constructed, and an experimental study was conducted to identify the P-wave primary phase. The network was trained and tested using a data set from Southern California. Compared with the traditional convolutional neural network, automatic identification algorithm, Pick-Net, and EQtransformer network, the recognition accuracy of our new convolutional recurrent neural network is relatively higher; therefore, the seismic waveform data can be directly used as a time series for training. Additionally, while the new convolutional recurrent neural network has only seven network layers, it achieves an accurate phase identification of complex network models, showcasing the strengths of convolutional neural networks. In summary, our study presents a convolutional recurrent neural network based on the LSTM, offers a new idea for the automatic identification of the primary phase, and provides technical support for the rapid and accurate automatic identification of the seismic phase.