Affiliation:
1. College of Electronic Science and Control Engineering, Institute of Disaster Prevention, Langfang 065201, China
2. Hebei Key Laboratory of Seismic Disaster Instrument and Monitoring Technology, Institute of Disaster Prevention, Langfang 065201, China
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.
Funder
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
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