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http://dx.doi.org/10.7776/ASK.2019.38.6.687

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

Min, Jeongki (고려대학교 전기전자공학부)
Kim, Gwantea (고려대학교 영상정보처리학부)
Ku, Bonhwa (고려대학교 전기전자공학부)
Lee, Jimin (기상청 지진화산연구과)
Ahn, Jaekwang (기상청 지진화산연구과)
Ko, Hanseok (고려대학교 전기전자공학부)
Publication Information
The Journal of the Acoustical Society of Korea / v.38, no.6, 2019 , pp. 687-696 More about this Journal
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.
Keywords
Seismic classification; Seismic feature extraction; Spectrogram; Mel-Spectrogram; Principle component analysis;
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