TY - GEN
T1 - Seismic Signal Synthesis by Generative Adversarial Network with Gated Convolutional Neural Network Structure
AU - Li, Yuanming
AU - Ku, Bonhwa
AU - Kim, Gwantae
AU - Ahn, Jae Kwang
AU - Ko, Hanseok
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/9/26
Y1 - 2020/9/26
N2 - Detecting earthquake events from seismic time series signal is a challenging task. Recently, detection methods based on machine learning have been developed to improve the accuracy and efficiency. However, accuracy of those methods rely on sufficient amount of high-quality training data. In many situations, the high-quality data is difficulty to obtain. We address and resolve this issue by using a Generative Adversarial Network (GAN) model for seismic signal synthesis. GAN already shows its powerful capability in generating high quality synthetic samples in multiple domains. In this paper, we propose a GAN model with gated CNN which can excellently capture sequential structure of seismic time series. We demonstrate its effectiveness via earthquake classification performance. The results show the synthetic data generated by our model indeed can improve the classification performance over the one trained with only real samples.
AB - Detecting earthquake events from seismic time series signal is a challenging task. Recently, detection methods based on machine learning have been developed to improve the accuracy and efficiency. However, accuracy of those methods rely on sufficient amount of high-quality training data. In many situations, the high-quality data is difficulty to obtain. We address and resolve this issue by using a Generative Adversarial Network (GAN) model for seismic signal synthesis. GAN already shows its powerful capability in generating high quality synthetic samples in multiple domains. In this paper, we propose a GAN model with gated CNN which can excellently capture sequential structure of seismic time series. We demonstrate its effectiveness via earthquake classification performance. The results show the synthetic data generated by our model indeed can improve the classification performance over the one trained with only real samples.
KW - data augmentation
KW - deep learning
KW - earthquake detection
KW - generative adversarial network
UR - http://www.scopus.com/inward/record.url?scp=85102017542&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85102017542&partnerID=8YFLogxK
U2 - 10.1109/IGARSS39084.2020.9323670
DO - 10.1109/IGARSS39084.2020.9323670
M3 - Conference contribution
AN - SCOPUS:85102017542
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 3857
EP - 3860
BT - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Y2 - 26 September 2020 through 2 October 2020
ER -