Real-time classification of earthquake using deep learning

H. Serdar Kuyuk, Ohno Susumu

    研究成果: Conference article査読

    10 被引用数 (Scopus)


    Existing Earthquake Early Warning Systems (EEWSs) calculates the location and magnitude of an earthquake using real-time waveforms from seismic stations within a few seconds. Typically, three to six stations are necessary to estimate earthquake parameters. Waiting for primary (P-) wave information from closest stations results in a blind-zone area where the arrival of secondary (S-) wave cannot be provided around the epicenter of an earthquake. If an earthquake occurred under a city center, EEWSs would not work even though each building has a seismic sensor in a smart city in future. Here, we present a methodology to classify earthquake vibrations into near-source or far-source within one second after P-wave detection. This will allow warnings to citizens who are the residence of earthquake epicenter in case of an earthquake very close by. We trained a deep learning Long Short-Term Memory (LSTM) network for sequence-to-label classification. 305 three component accelerations recorded between 2000 and 2018 in Japan are used to train the artificial network by extracting thirteen features of one second of P-wave. The accuracy of the methodology is 98.2%. 54 out of 55 near-source waveforms classified correctly and only 2 of 80 waveforms were misclassified. We tested the LSTM network with 2018 Northern Osaka (M 6.1.) earthquakes in Japan where closest stations are correctly identified with 83.3% accuracy. Therefore, smart cities donated with smart automated shut-on/off machines and sensors will be more resilient against earthquake disaster even EEWSs are not available in the blind zone area in future.

    ジャーナルProcedia Computer Science
    出版ステータスPublished - 2018
    イベントComplex Adaptive Systems Conference with Theme: Cyber Physical Systems and Deep Learning, CAS 2018 - Chicago, United States
    継続期間: 2018 11 52018 11 7

    ASJC Scopus subject areas

    • コンピュータ サイエンス(全般)


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