Abstract
Conventional feature of seismic classification focuses on strong seismic classification, while it is not suitable for classifying micro-seismic waves. We propose a feature extraction method based on histogram and Principal Component Analysis (PCA) in frequency-time space suitable for classifying seismic waves including strong, micro, and artificial seismic waves, as well as noise classification. The proposed method essentially employs histogram and PCA based features by concatenating the frequency and time information for binary classification which consist strong-micro-artificial/noise and micro/noise and micro/artificial seismic waves. Based on the recent earthquake data from 2017 to 2018, effectiveness of the proposed feature extraction method is demonstrated by comparing it with existing methods.
Original language | English |
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Pages (from-to) | 687-696 |
Number of pages | 10 |
Journal | Journal of the Acoustical Society of Korea |
Volume | 38 |
Issue number | 6 |
DOIs | |
Publication status | Published - 2019 |
Bibliographical note
Publisher Copyright:© 2019 Acoustical Society of Korea. All rights reserved.
Keywords
- Mel-Spectrogram
- Principle component analysis
- Seismic classification
- Seismic feature extraction
- Spectrogram
ASJC Scopus subject areas
- Signal Processing
- Instrumentation
- Acoustics and Ultrasonics
- Applied Mathematics
- Speech and Hearing