Ground motion estimation using front sitewave form data based on RVM for earthquake earlywarning

Yincheng Yang, Masato Motosaka

    Research output: Contribution to journalArticlepeer-review

    1 Citation (Scopus)

    Abstract

    The use of the earthquake early warning system (EEWS), one of the most useful emergency response tools, requires that the accuracy of real-time ground motion prediction (GMP) be enhanced. This requires that waveform information at observation points along earthquake wave propagation paths (hereafter, frontsite waveform information) be used effectively. To enhance the combined reliability of different systems, such as on-site and local/regional warning, we present a GMP method using front-site waveform information by applying a relevant vector machine (RVM).We present methodology and application examples for a case study estimating peak ground acceleration (PGA) and peak ground velocity (PGV) for earthquakes in the Miyagi-Ken Oki subduction zone. With no knowledge of source information, front site waveforms have been used to predict ground motion at target sites. Five input variables – earthquake PGA, PGD, pulse rise time, average period and the V pmax/Apmax ratio – have been used for the first 4 to 6 seconds of P-waves in training a regression model. We found that RVM is a useful tool for the prediction of peak ground motion.

    Original languageEnglish
    Pages (from-to)667-677
    Number of pages11
    JournalJournal of Disaster Research
    Volume10
    Issue number4
    Publication statusPublished - 2015 Aug 1

    Keywords

    • Earthquake early warning
    • Ground motion prediction
    • Relevant vector machine

    ASJC Scopus subject areas

    • Safety, Risk, Reliability and Quality
    • Engineering (miscellaneous)

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