Earthquake detection based on convolutional neural network using multi-band frequency signals

Seung Il Kim, Dong Hyun Kim, Hyun Hak Shin, Bonhwa Ku, Hanseok Ko

Research output: Contribution to journalArticlepeer-review


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 languageEnglish
Pages (from-to)23-29
Number of pages7
JournalJournal of the Acoustical Society of Korea
Issue number1
Publication statusPublished - 2019


  • 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


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