CT理论与应用研究2025，Vol.34Issue(2)：205-215,11.DOI:10.15953/j.ctta.2024.279
长短期记忆网络在P波初至震相识别中的实验研究
Experimental Study on Long Short-term Memory Networks for Identifying P-wave Primary Phase
摘要
Abstract
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.关键词
深度学习/初至震相/卷积循环神经网络/长短期记忆网络/时间序列Key words
deep learning/primary phase/convolutional recurrent neural network/long and short-term network/time series分类
信息技术与安全科学引用本文复制引用
王天哲,张万佶,祁善博,江国明..长短期记忆网络在P波初至震相识别中的实验研究[J].CT理论与应用研究,2025,34(2):205-215,11.基金项目
地球深部探测与矿产资源勘查国家科技重大专项(2024ZD1000100). （2024ZD1000100）
