Seismic Signal Synthesis by Generative Adversarial Network with Gated Convolutional Neural Network Structure

Yuanming Li, Bonhwa Ku, Gwantae Kim, Jae Kwang Ahn, Hanseok Ko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3857-3860
Number of pages4
ISBN (Electronic)9781728163741
DOIs
Publication statusPublished - 2020 Sep 26
Event2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Virtual, Waikoloa, United States
Duration: 2020 Sep 262020 Oct 2

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
CountryUnited States
CityVirtual, Waikoloa
Period20/9/2620/10/2

Keywords

  • data augmentation
  • deep learning
  • earthquake detection
  • generative adversarial network

ASJC Scopus subject areas

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

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