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JDR Vol.10 No.4 pp. 667-677
(2015)
doi: 10.20965/jdr.2015.p0667

Paper:

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Ground Motion Estimation Using Front Site Wave Form Data Based on RVM for Earthquake Early Warning

Yincheng Yang* and Masato Motosaka**

*Graduate School of Engineering, Tohoku University
Aramaki Aza-Aoba 468-1, Aoba-ku, Sendai 980-0845, Japan

**International Research Institute of Disaster Science, Tohoku University
Aramaki Aza-Aoba 468-1, Aoba-ku, Sendai 980-0845, Japan

Received:
February 3, 2015
Accepted:
June 19, 2015
Published:
August 1, 2015
Creative Commons License
Keywords:
ground motion prediction, earthquake early warning, Relevant Vector Machine
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, front-site 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 Vpmax/Amax 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.
Cite this article as:
Y. Yang and M. Motosaka, “Ground Motion Estimation Using Front Site Wave Form Data Based on RVM for Earthquake Early Warning,” J. Disaster Res., Vol.10 No.4, pp. 667-677, 2015.
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