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

Jeongki Min, Gwantea Kim, Bonhwa Ku, Jimin Lee, Jaekwang Ahn, Hanseok Ko

Research output: Contribution to journalArticlepeer-review


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 languageEnglish
Pages (from-to)687-696
Number of pages10
JournalJournal of the Acoustical Society of Korea
Issue number6
Publication statusPublished - 2019

Bibliographical note

Publisher Copyright:
© 2019 Acoustical Society of Korea. All rights reserved.


  • 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


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