Abstract
In this paper, a deep learning-based detection and classification using multi-band frequency signals is presented for detecting earthquakes prevalent in Korea. Based on an analysis of the previous earthquakes in Korea, it is observed that multi-band signals are appropriate for classifying earthquake signals. Therefore, in this paper, we propose a deep CNN (Convolutional Neural Network) using multi-band signals as training data. The proposed algorithm extracts the multi-band signals (Low/Medium/High frequency) by applying band pass filters to mel-spectrum of earthquake signals. Then, we construct three CNN architecture pipelines for extracting features and classifying the earthquake signals by a late fusion of the three CNNs. We validate effectiveness of the proposed method by performing various experiments for classifying the domestic earthquake signals detected in 2018.
Original language | English |
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Pages (from-to) | 23-29 |
Number of pages | 7 |
Journal | Journal of the Acoustical Society of Korea |
Volume | 38 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2019 |
Bibliographical note
Publisher Copyright:© 2019 Acoustical Society of Korea. All rights reserved.
Keywords
- CNN (Convolutional Neural Network)
- Earthquake detection
- Mel-spectrum
- Multi-band frequency analysis
ASJC Scopus subject areas
- Signal Processing
- Instrumentation
- Acoustics and Ultrasonics
- Applied Mathematics
- Speech and Hearing