한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제24권1호
  • /
  • Pages.33-39
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  • 2019
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  • 1598-849X(pISSN)
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  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
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Deep Convolutional Neural Network with Bottleneck Structure using Raw Seismic Waveform for Earthquake Classification

  • Ku, Bon-Hwa (Machine learning and Big Data Research Center, Korea University) ;
  • Kim, Gwan-Tae (Dept. of Visual Information Processing, Korea University) ;
  • Min, Jeong-Ki (School of Electrical Engineering, Korea University) ;
  • Ko, Hanseok (School of Electrical Engineering, Korea University)
  • 투고 : 2018.12.06
  • 심사 : 2019.01.08
  • 발행 : 2019.01.31
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초록

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.

키워드

CPTSCQ_2019_v24n1_33_f0001.png 이미지

Fig. 1. Earthquake classifier using CNN

CPTSCQ_2019_v24n1_33_f0002.png 이미지

Fig. 2. Bottleneck structure

CPTSCQ_2019_v24n1_33_f0003.png 이미지

Fig. 3. CNN with 1x1 bottleneck structure

CPTSCQ_2019_v24n1_33_f0004.png 이미지

Fig. 4. Extract earthquake events from 24 hour continuous data

CPTSCQ_2019_v24n1_33_f0005.png 이미지

Fig. 5. Frequency histogram distribution according events

Table 1. Number of Earthquake events

CPTSCQ_2019_v24n1_33_t0001.png 이미지

Table 2. Event Dataset

CPTSCQ_2019_v24n1_33_t0002.png 이미지

Table 3.Comparison performance of proposed method (unit: %)

CPTSCQ_2019_v24n1_33_t0003.png 이미지

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