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Lin Binhua, Jin Xing, Chen Huifang, Liao Shirong, Huang Lingzhu, Zhang Yanming, Wu Lihua. 2019: Analysis and revision of magnitude ML deviation between Fujian and Taiwan based on BP neural network. Acta Seismologica Sinica, 41(6): 723-734. DOI: 10.11939/jass.20190038
Citation: Lin Binhua, Jin Xing, Chen Huifang, Liao Shirong, Huang Lingzhu, Zhang Yanming, Wu Lihua. 2019: Analysis and revision of magnitude ML deviation between Fujian and Taiwan based on BP neural network. Acta Seismologica Sinica, 41(6): 723-734. DOI: 10.11939/jass.20190038
shu

Analysis and revision of magnitude ML deviation between Fujian and Taiwan based on BP neural network

  • 1.

    College of Civil Engineering，Fuzhou University，Fuzhou 350108，China

  • 2.

    Fujian Earthquake Agency，Fuzhou 350003，China

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  • Received Date: February 20, 2019
  • Revised Date: May 16, 2019
  • Available Online: December 15, 2019
  • Published Date: October 31, 2019
    Graphical Abstract
    • Abstract
  • In this paper, the shallow earthquake events in Taiwan island from 2012 to 2018 were used for analyses on magnitude deviation measured by Taiwan Weather Bureau （TWB） and Fujian Seismic Network Center （FSNC）. The result shows the differences in magnitude between Taiwan region and Fujian region were affected by magnitude value, focal depth and geographic location. And then we made linear regressions for magnitude ML of the earthquakes measured by TWB and FSNC, respectively. In the meanwhile, a BP neural network with 4−9−9−9−4 five-layer model was introduced into the prediction training of these two regions. For the model, the Taiwan earthquakes in 2012−2017 were taken as training set, and the events in 2018 were used as the testing set. After the revision of BP neural network, the deviation values of magnitude were basically controlled within −0.4 to 0.3, which is better than those from the traditional linear regression method, especially for multiple earthquakes and minority earthquakes. Furthermore, the testing results also validate the abilities of non-linear data fitting and generalization.
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    • References (27)
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