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.
deep learningseismic phase identificationBiLSTM networkU-shaped convolutional neural network
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