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Earthquake events classification using convolutional recurrent neural network

Authors
Ku, BonhwaKim, GwantaeJang, SuKo, Hanseok
Issue Date
2020
Publisher
ACOUSTICAL SOC KOREA
Keywords
Earthquake events classification; Convolutional Neural Network (CNN); Recurrent Neural Network (RNN); Convolutional Recurrent Neural Network (CRNN)
Citation
JOURNAL OF THE ACOUSTICAL SOCIETY OF KOREA, v.39, no.6, pp.592 - 599
Indexed
SCOPUS
KCI
Journal Title
JOURNAL OF THE ACOUSTICAL SOCIETY OF KOREA
Volume
39
Number
6
Start Page
592
End Page
599
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/59066
DOI
10.7776/ASK.2020.39.6.592
ISSN
1225-4428
Abstract
This paper proposes a Convolutional Recurrent Neural Net (CRNN) structure that can simultaneously reflect both static and dynamic characteristics of seismic waveforms for various earthquake events classification. Addressing various earthquake events, including not only micro-earthquakes and artificial-earthquakes but also macro-earthquakes, requires both effective feature extraction and a classifier that can discriminate seismic waveform under noisy environment. First, we extract the static characteristics of seismic waveform through an attention-based convolution layer. Then, the extracted feature-map is sequentially injected as input to a multi-input single-output Long Short-Term Memory (LSTM) network structure to extract the dynamic characteristic for various seismic event classifications. Subsequently, we perform earthquake events classification through two fully connected layers and softmax function. Representative experimental results using domestic and foreign earthquake database show that the proposed model provides an effective structure for various earthquake events classification.
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공과대학 (전기전자공학부)
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