Analysis of normalization effect for earthquake events classification

Shou Zhang; Bonhwa Ku; Hansoek Ko

doi:10.7776/ASK.2021.40.2.130

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2021 Vol.40, Issue 2 Preview Page Next Page

Research Article

Analysis of normalization effect for earthquake events classification 지진 이벤트 분류를 위한 정규화 기법 분석

31 March 2021. pp. 130-138

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Abstract

This paper presents an effective structure by applying various normalization to Convolutional Neural Networks (CNN) for seismic event classification. Normalization techniques can not only improve the learning speed of neural networks , but also show robustness to noise. In this paper, we analyze the effect of input data normalization and hidden layer normalization on the deep learning model for seismic event classification. In addition an effective model is derived through various experiments according to the structure of the applied hidden layer. As a result of various experiments, the model that applied input data normalization and weight normalization to the first hidden layer showed the most stable performance improvement.

Keywords

Seismic event classification

Convolutional neural network

Normalization

Hidden layer

본 논문에서는 지진 이벤트 분류를 위한 다양한 정규화 기법 분석 및 효과적인 합성곱 신경망(Convolutional Neural Network, CNN)기반의 네트워크 구조를 제안하였다. 정규화 기법은 신경망의 학습 속도를 개선할 뿐만 아니라 잡음에 강인한 모습을 보여 준다. 본 논문에서는 지진 이벤트 분류를 위한 딥러닝 모델에서 입력 정규화 및 은닉 레이어 정규화가 모델에 미치는 영향을 분석하였다. 또한, 적용 은닉 레이어의 구조에 따른 다양한 실험을 통해 효과적인 모델을 도출하였다. 다양한 모의실험 결과 입력 데이터 정규화 및 제1 은닉 레이어에 가중치 정규화를 적용한 모델이 가장 안정적인 성능 향상을 보여 주었다.

키워드

지진 이벤트 분류

합성곱 신경망

정규화 기법

은닉 레이어

References

T. Perol, M. Gharbi, and M. Denolle, "Convolutional neural network for earthquake detection and location," Science Advances, 4, e1700578, (2018). 10.1126/sciadv.170057829487899PMC5817932

B. Ku, G. Kim, J.-K. Ahn, J. Lee, and H. Ko, "Attention- based convolutional neural network for earthquake event classification," IEEE Geoscience and Remote Sensing Letters, 1-5 (2020). 10.1109/LGRS.2020.3014418

S. M. Mousavi, W. Zhu, Y. Sheng, and G. C. Beroza, "CRED: A deep residual network of convolutional and recurrent units for earthquake signal detection," Scientific reports, 9, 1-14 (2019). 10.1038/s41598-019-45748-131311942PMC6635521

G. Kim, B. Ku, and H. Ko, "Multifeature fusion-based earthquake event classification using transfer learning," IEEE Geoscience and Remote Sensing Letters, 1-5 (2020). 10.1109/LGRS.2020.2993302

G. Kim, B. Ku, and H. Ko, "Multi-site based earthquake event classification using graph convolution networks" (in Korean), J. Acoust. Soc. Kr. 39, 615- 621 (2020).

S. Ioffe and C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift," arXiv preprint arXiv:1502.03167 (2015).

J. Bjorck, C. Gomes, B. selman, and K. Q. Weinberger "Understanding batch normalization," NeurIPS (2018).

T. Salimans and D. P. Kingma, "Weight normalization: A simple reparameterization to accelerate training of deep neural networks," arXiv preprint arXiv:1602. 07868 (2016).

J. L. Ba, J. R. Kiros, and G. E. Hinton, "Layer normalization," arXiv preprint arXiv:1607.06450 (2016).

Y. Wu and K. He, "Group normalization," Proc. ECCV. 1-17 (2018).

S. M. Mousavi, Y. Sheng, W. Zhu, and G. C. Beroza, "STanford EArthquake Dataset (STEAD): A global data set of seismic signals for AI," IEEE Access, 7, 179464-179476 (2019). 10.1109/ACCESS.2019.2947848

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 : 40
No :2
Pages :130-138
Received Date : 2021-01-18
Revised Date : 2021-02-23
Accepted Date : 2021-03-02
DOI :https://doi.org/10.7776/ASK.2021.40.2.130

The Journal of the Acoustical Society of KoreaISSN:1225-4428(Print) 2287-3775(Online)한국음향학회

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