Multifeature Fusion-Based Earthquake Event Classification Using Transfer Learning
- Authors
- Kim, Gwantae; Ku, Bonhwa; Ko, Hanseok
- Issue Date
- 6월-2021
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Earthquakes; Feature extraction; Convolution; Transforms; Spectrogram; Neural networks; Training; Convolution neural network (CNN); deep learning; earthquake event classification; multifeature fusion; transfer learning
- Citation
- IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, v.18, no.6, pp.974 - 978
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
- Volume
- 18
- Number
- 6
- Start Page
- 974
- End Page
- 978
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/127894
- DOI
- 10.1109/LGRS.2020.2993302
- ISSN
- 1545-598X
- Abstract
- This letter proposes a multifeature fusion model using deep convolution neural networks and transfer learning approach for earthquake event classification. There are several feature representations for seismic analysis, such as the time domain, the frequency domain, and the time-frequency domain. To successfully classify various earthquake events, we propose a novel model that combines these features hierarchically. In addition, we apply a transfer learning to mitigate overfitting problem of deep learning model while achieving high classification performance. To evaluate our approach, we conduct experiments with the Korean peninsula earthquake database from 2016 to 2018 and a large earthquake database on the Circum-Pacific belt in 2019. The experimental results show that the proposed method outperforms over the compared state-of-the-art methods.
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Collections - College of Engineering > School of Electrical Engineering > 1. Journal Articles
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