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Multi-site based earthquake event classification using graph convolution networks

Authors
Kim, GwantaeKu, BonhwaKo, Hanseok
Issue Date
2020
Publisher
ACOUSTICAL SOC KOREA
Keywords
Earthquake event classification; Multi-site based classification; Convolution neural networks; Graph convolution networks
Citation
JOURNAL OF THE ACOUSTICAL SOCIETY OF KOREA, v.39, no.6, pp.615 - 621
Indexed
SCOPUS
KCI
Journal Title
JOURNAL OF THE ACOUSTICAL SOCIETY OF KOREA
Volume
39
Number
6
Start Page
615
End Page
621
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/59091
DOI
10.7776/ASK.2020.39.6.615
ISSN
1225-4428
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.
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