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Research Article

Earthquake detection based on convolutional neural network using multi-band frequency signals

다중 주파수 대역 convolutional neural network 기반 지진 신호 검출 기법

Seung-Il Kim1
,
Dong-Hyun Kim1
,
Hyun-Hak Shin1
,
Bonhwa Ku1
,
Hanseok Ko1∗
김 승일1
,
김 동현1
,
신 현학1
,
구 본화1
,
고 한석1∗
1고려대학교 전기전자 공학부
∗Corresponding Author
31 January 2019. pp. 23-29
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Abstract

In this paper, a deep learning-based detection and classification using multi-band frequency signals is presented for detecting earthquakes prevalent in Korea. Based on an analysis of the previous earthquakes in Korea, it is observed that multi-band signals are appropriate for classifying earthquake signals. Therefore, in this paper, we propose a deep CNN (Convolutional Neural Network) using multi-band signals as training data. The proposed algorithm extracts the multi-band signals (Low/Medium/High frequency) by applying band pass filters to mel-spectrum of earthquake signals. Then, we construct three CNN architecture pipelines for extracting features and classifying the earthquake signals by a late fusion of the three CNNs. We validate effectiveness of the proposed method by performing various experiments for classifying the domestic earthquake signals detected in 2018.

Keywords
Multi-band frequency analysis
Earthquake detection
CNN (Convolutional Neural Network)
Mel-spectrum

본 논문에서는 국내에서 발생한 지진 신호를 검출 및 식별하기 위한 방법을 다루었다. 국내에서 발생한 지진 신호들을 분석해 본 결과 서로 다른 주파수 대역 신호의 특징들이 각각 분류를 위한 특징으로 적절함을 확인할 수 있었다. 이러한 분석 결과를 바탕으로 지진 신호에서 추출한 다중 주파수 대역 특징을 기반으로 하는 CNN(Convolutional Neural Network) 기법에 대해서 제안하였다. 제안하는 다중 주파수 대역 CNN 기법은 지진 신호에서 추출한 멜 스펙트럼에 대해서 각각 필터를 적용하여 서로 다른 주파수 대역(저/중/고 주파수)의 신호를 추출하였다. 추출된 신호들을 바탕으로 각각 CNN 기반 분류를 수행하였고, 수행된 결과를 융합하여 최종적으로 지진 이벤트에 대해 식별하였다. 2018년 동안 대한민국에서 발생한 실제 지진데이터를 기반으로 하는 실험을 통해 제안하는 기법에 대한 효용성을 검증하였다.

키워드
다중 주파수 분석
지진 신호 검출
CNN (Convolutional Neural Network)
멜 스펙트럼
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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 : 38
  • No :1
  • Pages :23-29
  • Received Date : 2018-11-29
  • Accepted Date : 2019-01-23
  • DOI :https://doi.org/10.7776/ASK.2019.38.1.023
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