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Principal component analysis based frequency-time feature extraction for seismic wave classification

Authors
Min, JeongkiKim, GwanteaKu, BonhwaLee, JiminAhn, JaekwangKo, Hanseok
Issue Date
11월-2019
Publisher
ACOUSTICAL SOC KOREA
Keywords
Seismic classification; Seismic feature extraction; Spectrogram; Mel-Spectrogram; Principle component analysis
Citation
JOURNAL OF THE ACOUSTICAL SOCIETY OF KOREA, v.38, no.6, pp.687 - 696
Indexed
SCOPUS
KCI
Journal Title
JOURNAL OF THE ACOUSTICAL SOCIETY OF KOREA
Volume
38
Number
6
Start Page
687
End Page
696
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/62008
DOI
10.7776/ASK.2019.38.6.687
ISSN
1225-4428
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.
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