Deep Convolutional Neural Network with Bottleneck Structure using Raw Seismic Waveform for Earthquake Classification (지진 이벤트 분류를 위한 Bottleneck 구조의 심층 CNN 기법)

Bonhwa Ku (구본화); Gwan-Tae Kim (김관태); Jeong-Ki Min (민정기); Hanseok Ko (고한석)

doi:10.9708/jksci.2019.24.01.033

Deep Convolutional Neural Network with Bottleneck Structure using Raw Seismic Waveform for Earthquake Classification

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2019, 24(1), pp.33-39
DOI : 10.9708/jksci.2019.24.01.033
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : December 6, 2018
Accepted : January 8, 2019
Published : January 31, 2019

Bonhwa Ku ¹, Gwan-Tae Kim ¹, Jeong-Ki Min ¹, Hanseok Ko ¹

¹고려대학교

Accredited

ABSTRACT

In this paper, we propose deep convolutional neural network(CNN) with bottleneck structure which improves the performance of earthquake classification. In order to address all possible forms of earthquakes including micro-earthquakes and artificial-earthquakes as well as large earthquakes, we need a representation and classifier that can effectively discriminate seismic waveforms in adverse conditions. In particular, to robustly classify seismic waveforms even in low snr, a deep CNN with 1x1 convolution bottleneck structure is proposed in raw seismic waveforms. The representative experimental results show that the proposed method is effective for noisy seismic waveforms and outperforms the previous state-of-the art methods on domestic earthquake database.

KEYWORDS

Convolutional neural network, earthquake classification, bottleneck structure, raw seismic waveform, centering preprocessing

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* References for papers published after 2023 are currently being built.

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[web] / NECIS / http://necis.kma.go.kr/

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Deep Convolutional Neural Network with Bottleneck Structure using Raw Seismic Waveform for Earthquake Classification

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Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Deep Convolutional Neural Network with Bottleneck Structure using Raw Seismic Waveform for Earthquake Classification

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KEYWORDS

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* References for papers published after 2023 are currently being built.