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http://dx.doi.org/10.9708/jksci.2019.24.01.033

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)
Publication Information
Journal of the Korea Society of Computer and Information / v.24, no.1, 2019 , pp. 33-39 More about this Journal
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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