지진 이벤트 분류를 위한 Bottleneck 구조의 심층 CNN 기법
Deep Convolutional Neural Network with Bottleneck Structure using Raw Seismic Waveform for Earthquake Classification
1
조회
0
다운로드
2
인용
0
참고문헌
키워드
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.
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.
참고문헌 (20)
- Zhicheng Cui, "Multi-Scale Convolutional Neural Networks for Time Series Classification", arXiv:1603.06995, ~ (2016)
- Jogpil Lee, "SampleCNN : End-to-End Deep Convolutional Neural Networks Using Very Small Filter for Music Classifications", Applied Science, 8(1), pp.150~ (2018)
- Qingxue Zhang, "HeartID : a multiresolution convolutional neural network for ECG-based biometric human identification in smart health applications", IEEE Access, 5, pp.11805~11816 (2017)
- M. Withers, "A comparison of select trigger algorithms for automated global seismic phase and event detection", Bull. Seismol. Soc. Am, 88(1), pp.95~106 (1998)
- S. J. Gibbons, "The detection of low magnitude seismic events using array-based waveform correlation", Geophys. J. Int, 165(1), pp.149~166 (2006)
- R. J. Skoumal, "Optimizing multi-station earthquake template matching through re-examination of the Youngstown, Ohio, sequence", Earth and Planet Science Letters, 405, pp.274~280 (2014)
- C. E. Yoon, "Earthquake detection through computationally efficient similarity search", Science Advances, 1(11), pp.e1501057~ (2015)
- Thibaut Perol, "Convolutional neural network for earthquake detection and location", Science Advances, 4(2), pp.e1700578~ (2018)
- Sergey Ioffe, "Batch Normalization : Accelerating Deep Network Training by Reducing Internal Covariate Shift", arXiv:1502.03167, ~ (2015)
- Nitish Srivastava, "Dropout : A Simple Way to Prevent Neural Networks from Overfitting", Journal of Machine Learning Research, 15(1), pp.1929~1958 (2014)
- Dieleman, S., "End-to-end learning for music audio", Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp.6964~6968 (2014)
- Ardila, D, "Audio deepdream: Optimizing raw audio with convolutional networks", Proceedings of the International Society for Music Information Retrieval Conference (2016)
- Thickstun, J., "Learning features of music from scratch", arXiv, ~ (2017)
- Y. LeCun, "Deep learning", Nature, 521(7553), pp.436~444 (2015)
- X. Cui, "Data augmentation for deep neural network acoustic modeling", IEEE, 2015, pp.23(9), 1469~1477
- Simonyan, K, "Very deep convolutional networks for large-scale image recognition", arXiv:1409.1556, ~ (2015)
- Anastasia Borovykh, "Conditional Time Series Forecasting with Convolutional Neural Networks", arXiv:1703.04691, ~ (2018)
- Nima Hatami, "Classification of Time-Series Images Using Deep Convolutional Neural Networks", arXiv:1710.00886, ~ (2017)
- Hassan Ismail Fawaz, "Deep learning for time series classification: a review", arXiv: 1809.04356, ~ (2018)
- "NECIS"[URL]