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

    1 Citation (Scopus)

    Abstract

    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
    Volume38
    Issue number1
    DOIs
    Publication statusPublished - 2019

    Bibliographical note

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

    Keywords

    • 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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