Earthquake events classification using convolutional recurrent neural network

Bonhwa Ku, Gwantae Kim, Su Jang, and Hanseok Ko

doi:10.7776/ASK.2020.39.6.592

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2020 Vol.39, Issue 6 Preview Page Next Page

Research Article

Earthquake events classification using convolutional recurrent neural network 합성곱 순환 신경망 구조를 이용한 지진 이벤트 분류 기법

November 2020. pp. 592-599

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Abstract

본 논문은 다양한 지진 이벤트 분류를 위해 지진 데이터의 정적인 특성과 동적인 특성을 동시에 반영할 수 있는 합성곱 순환 신경망(Convolutional Recurrent Neural Net, CRNN) 구조를 제안한다. 중규모 지진뿐만 아니라 미소 지진, 인공 지진을 포함한 지진 이벤트 분류 문제를 해결하려면 효과적인 특징 추출 및 분류 방법이 필요하다. 본 논문에서는 먼저 주의 기반 합성곱 레이어를 통해 지진 데이터의 정적 특성을 추출 하게 된다. 추출된 특징은 다중 입력 단일 출력 장단기메모리(Long Short-Term Memory, LSTM) 네트워크 구조에 순차적으로 입력되어 다양한 지진 이벤트 분류를 위한 동적 특성을 추출하게 되며 완전 연결 레이어와 소프트맥스 함수를 통해 지진 이벤트 분류를 수행한다. 국내외 지진을 이용한 모의 실험 결과 제안된 모델은 다양한 지진 이벤트 분류에 효과적인 모습을 보여 주었다.

키워드

지진 이벤트 분류

합성곱 신경망

순환 신경망

합성 순환 신경망

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.

Keywords

Earthquake events classification

Convolutional Neural Network (CNN)

Recurrent Neural Network (RNN)

Convolutional Recurrent Neural Network (CRNN)

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Information

Publisher :The Acoustical Society Of Korea
Publisher(Ko) :한국음향학회
Journal Title :The Journal of the Acoustical Society of Korea
Journal Title(Ko) :한국음향학회지
Volume : 39
No :6
Pages :592-599
Received Date :2020. 07. 31
Revised Date :2020. 09. 14
Accepted Date : 2020. 10. 19
DOI :https://doi.org/10.7776/ASK.2020.39.6.592

The Journal of the Acoustical Society of KoreaISSN:1225-4428(Print) 2287-3775(Online)한국음향학회

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