Earthquake Event Classification Using Multitasking Deep Learning
- Authors
- Ku, Bonhwa; Min, Jeungki; Ahn, Jae-Kwang; Lee, Jimin; Ko, Hanseok
- Issue Date
- 7월-2021
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Earthquakes; Feature extraction; Task analysis; Convolution; Deep learning; Multitasking; Data mining; Attention module; convolutional neural network (CNN); earthquake event classification; feature aggregation; multitasking deep learning
- Citation
- IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, v.18, no.7, pp.1149 - 1153
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
- Volume
- 18
- Number
- 7
- Start Page
- 1149
- End Page
- 1153
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/127754
- DOI
- 10.1109/LGRS.2020.2996640
- ISSN
- 1545-598X
- Abstract
- This letter proposes an attention-based convolutional neural network architecture for multitasking learning to accurately classify not only the presence of an earthquake but also the event type of the earthquake. In particular, to improve the performance in earthquake-type classification, we develop an attention-based feature aggregation framework embedded in multitask learning architecture. Representative experimental results show that the proposed method provides an effective structure for an earthquake detection and event classification with an earthquake database of the Korean peninsula and the Circum-Pacific belt.
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Collections - College of Engineering > School of Electrical Engineering > 1. Journal Articles
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