Research on Seismic Phase Recognition Method Based on Bi-LSTM Network

In order to improve the precision of phase recognition and reduce the rate of misdetection, this paper applies the deep learning method to automatic phase recognition. In this paper, an automatic seismic phase recognition model based on the Bi-LSTM network is designed. To test the performance of thi...

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Bibliographic Details
Published inApplied sciences Vol. 14; no. 16; p. 6917
Main Authors Wang, Li, Cai, Jianxian, Duan, Li, Guo, Lili, Shi, Xingxing, Cai, Huanyu
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.08.2024
Subjects
Accuracy
Bi-LSTM
Deep learning
Earthquakes
Efficiency
Fractals
Geometry
Neural networks
seismic phase recognition
Seismology
Signal processing
Signal to noise ratio
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Summary:In order to improve the precision of phase recognition and reduce the rate of misdetection, this paper applies the deep learning method to automatic phase recognition. In this paper, an automatic seismic phase recognition model based on the Bi-LSTM network is designed. To test the performance of this model, the STEAD dataset is used for training and testing, and this model is compared with the traditional STA/LTA and AIC methods. The experimental results show that, compared to STA/LTA and AIC methods, the Bi-LSTM network can reduce the misdetection rate by about 8–15%, and improve the RSEM; especially, the prediction error of S-wave is greatly reduced.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14166917