TY - JOUR
T1 - Graph Convolution Networks for Seismic Events Classification Using Raw Waveform Data from Multiple Stations
AU - Kim, Gwantae
AU - Ku, Bonhwa
AU - Ahn, Jae Kwang
AU - Ko, Hanseok
N1 - Funding Information:
This work was supported by the Development of earthquake, tsunami, volcano monitoring and prediction technology under Grant NTIS: 1365003423.
Publisher Copyright:
© 2004-2012 IEEE.
PY - 2022
Y1 - 2022
N2 - This letter proposes a multiple station-based seismic event classification model using a deep convolution neural network (CNN) and graph convolution network (GCN). To classify various seismic events, such as natural earthquakes, artificial earthquakes, and noise, the proposed model consists of weight-shared convolution layers, graph convolution layers, and fully connected layers. We employed graph convolution layers in order to aggregate features from multiple stations. Representative experimental results with the Korean peninsula earthquake datasets from 2016 to 2019 showed that the proposed model is superior to the single-station based state-of the-art methods. Moreover, the proposed model significantly reduced false alarms when using continuous waveforms of long duration. The code is available at.1
AB - This letter proposes a multiple station-based seismic event classification model using a deep convolution neural network (CNN) and graph convolution network (GCN). To classify various seismic events, such as natural earthquakes, artificial earthquakes, and noise, the proposed model consists of weight-shared convolution layers, graph convolution layers, and fully connected layers. We employed graph convolution layers in order to aggregate features from multiple stations. Representative experimental results with the Korean peninsula earthquake datasets from 2016 to 2019 showed that the proposed model is superior to the single-station based state-of the-art methods. Moreover, the proposed model significantly reduced false alarms when using continuous waveforms of long duration. The code is available at.1
KW - Convolution neural network (CNN)
KW - deep learning
KW - graph convolution network (GCN)
KW - multiple station
KW - seismic event classification
UR - http://www.scopus.com/inward/record.url?scp=85119448017&partnerID=8YFLogxK
U2 - 10.1109/LGRS.2021.3127874
DO - 10.1109/LGRS.2021.3127874
M3 - Article
AN - SCOPUS:85119448017
VL - 19
JO - IEEE Geoscience and Remote Sensing Letters
JF - IEEE Geoscience and Remote Sensing Letters
SN - 1545-598X
ER -