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.
| Original language | English |
|---|---|
| Article number | 9098918 |
| Pages (from-to) | 974-978 |
| Number of pages | 5 |
| Journal | IEEE Geoscience and Remote Sensing Letters |
| Volume | 18 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 2021 Jun |
Bibliographical note
Funding Information:Manuscript received November 26, 2019; revised March 9, 2020; accepted May 4, 2020. Date of publication May 22, 2020; date of current version May 21, 2021. This work was supported by Meteorological/Earthquake See-At Technology Development Research under Grant KMI2018-09610. (Corresponding author: Hanseok Ko.) The authors are with the School of Electrical Engineering, Korea University, Seoul 02841, South Korea (e-mail: [email protected]; [email protected]; [email protected]).
Publisher Copyright:
© 2004-2012 IEEE.
Keywords
- Convolution neural network (CNN)
- deep learning
- earthquake event classification
- multifeature fusion
- transfer learning
ASJC Scopus subject areas
- Geotechnical Engineering and Engineering Geology
- Electrical and Electronic Engineering