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journal article·2024-08-07Open access

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

Li Wang, Jianxian Cai, Li Duan, Lili Guo, Xingxing Shi, Huanyu Cai

Applied Sciences· Volume 14· Issue 16
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Source abstract

Abstract

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.
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Research context

OpenAlex topics: Seismology and Earthquake Studies · Seismic Waves and Analysis · Seismic Imaging and Inversion Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

College of Electronic Science and Control Engineering, Institute of Disaster Prevention, Langfang 065201, China

Hebei Key Laboratory of Seismic Disaster Instrument and Monitoring Technology, Institute of Disaster Prevention, Langfang 065201, China

Funding

Scientific Research Project Item of Hebei Province Education Department · Science and Technology Innovation Program for Postgraduate students in IDP subsidized by Fundamental Research Funds for the Central Universities · Key Laboratory Open Fund Project of Hebei Provincial · College Students’ Innovation and Entrepreneurship Training Program Project of Institute of Disaster Prevention

References (23)

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