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Wei Yonggang, Yang Qianli, Wang Tingting, Jiang Changsheng, Bian Yinju. 2019: Earthquake and explosion identification based on Deep Learning residual network model. Acta Seismologica Sinica, 41(5): 646-657. DOI: 10.11939/jass.20190030
Citation: Wei Yonggang, Yang Qianli, Wang Tingting, Jiang Changsheng, Bian Yinju. 2019: Earthquake and explosion identification based on Deep Learning residual network model. Acta Seismologica Sinica, 41(5): 646-657. DOI: 10.11939/jass.20190030
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Earthquake and explosion identification based on Deep Learning residual network model

  • Institute of Geophysics，China Earthquake Administration，Beijing 100081，China

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  • Received Date: February 13, 2019
  • Revised Date: April 01, 2019
  • Available Online: September 26, 2019
  • Published Date: August 31, 2019
    Graphical Abstract
    • Abstract
  • In order to enhance the property identification of earthquakes and explosions recorded by seismic network, this paper proposed a new technology of explosion discrimination based on the residual network model in Deep Learning technology, and utilized it to identifying explosion and surrounding earthquakes in Sanhe Quarry of Hebei Province. According to the waveform data recorded by the Beijing Digital Telemetry Seismic Network and China Center of Digital Seismic Network, and the released seismic phase reports, we analyzed the waveform power spectrum of 93 explosion events and 54 surrounding seismic events in Sanhe Quarry of Hebei Province. Moreover, 100 independent random sampling sub-tests, 1 000 independent random sampling sub-tests and leave-one-out-cross-validation test were conducted by adopting different training sample proportions, respectively. The test results show that the residual network model in Deep Learning has a high recognition rate and a stable effect in identifying the property of earthquakes and explosions, therefore it has a wonderfully potential application.
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    • References (38)
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