Attention-Based Convolutional Neural Network for Earthquake Event Classification
- Authors
- Ku, Bonhwa; Kim, Gwantae; Ahn, Jae-Kwang; Lee, Jimin; Ko, Hanseok
- Issue Date
- 12월-2021
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Attention module; Convolution; Data mining; Data models; Earthquakes; Feature extraction; Machine learning; Time series analysis; convolutional neural network (CNN); deep learning; earthquake classification; raw seismic waveform
- Citation
- IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, v.18, no.12, pp.2057 - 2061
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
- Volume
- 18
- Number
- 12
- Start Page
- 2057
- End Page
- 2061
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/135625
- DOI
- 10.1109/LGRS.2020.3014418
- ISSN
- 1545-598X
- Abstract
- This letter presents a deep convolutional neural network (CNN) with attention module that improves the performance of the classification of various earthquake events. Addressing all possible earthquake events, including not only microearthquakes and artificial-earthquakes but also large-earthquakes, requires both suitable feature expression and a classifier that can effectively discriminate seismic waveforms under adverse conditions. To robustly classify earthquake events, a deep CNN with an attention module was proposed in raw seismic waveforms. Representative experimental results show that the proposed method provides an effective structure for earthquake events classification and, with the Korean peninsula earthquake database from 2016 to 2018, outperforms previous state-of-the-art methods.
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