Multi-site based earthquake event classification using graph convolution networks

Gwantae Kim, Bonhwa Ku, Hanseok Ko

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we propose a multi-site based earthquake event classification method using graph convolution networks. In the traditional earthquake event classification methods using deep learning, they used single-site observation to estimate seismic event class. However, to achieve robust and accurate earthquake event classification on the seismic observation network, the method using the information from the multi-site observations is needed, instead of using only single-site data. Firstly, our proposed model employs convolution neural networks to extract informative embedding features from the single-site observation. Secondly, graph convolution networks are used to integrate the features from several stations. To evaluate our model, we explore the model structure and the number of stations for ablation study. Finally, our multi-site based model outperforms up to 10 % accuracy and event recall rate compared to single-site based model.

Original languageEnglish
Pages (from-to)615-621
Number of pages7
JournalJournal of the Acoustical Society of Korea
Volume39
Issue number6
DOIs
Publication statusPublished - 2020

Keywords

  • Convolution neural networks
  • Earthquake event classification
  • Graph convolution networks
  • Multi-site based classification

ASJC Scopus subject areas

  • Acoustics and Ultrasonics
  • Instrumentation
  • Applied Mathematics
  • Signal Processing
  • Speech and Hearing

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