Research on Seismic Phase Recognition System Using U-shaped Neural Network Combined with BiLSTM Network

To improve the accuracy of seismic phase identification, a deep learning-based seismic phase identification method is proposed. This method is based on the BiLSTM network recognition framework, improved by introducing a U-shaped convolutional neural network into the BiLSTM network structure. The improved BiLSTM network was used to identify seismic phases, achieving accurate identification of seismic P-wave and S-wave phases. The simulation results show that this method can effectively and accurately identify seismic P-wave and S-wave phases, with an average recognition accuracy of 90.01%, an average missed detection rate of 11.00%, and a root mean square error of 0.23. Compared with BiLSTM network, commonly used seismic phase recognition MEA-BP neural network models, and CNN models, this method has higher recognition accuracy for seismic phases and obvious advantages, providing a reference for achieving accurate identification of seismic phases.