Japan Geoscience Union Meeting 2018

Presentation information

[EE] Oral

S (Solid Earth Sciences) » S-SS Seismology

[S-SS05] Effective usage of PSHA

Tue. May 22, 2018 3:30 PM - 5:00 PM A07 (Tokyo Bay Makuhari Hall)

convener:Matt Gerstenberger(GNS Science), Danijel Schorlemmer(GFZ German Research Centre for Geosciences), Ken Xiansheng Hao(防災科学技術研究所 防災システムセンター, 共同), Kuo-Fong Ma(Institute of Geophysics, National Central University, Taiwan, ROC), Chairperson:Gerstenberger Matthew(GNS Science, New Zealand), Schorlemmer Danijel(GFZ-Potsdam, Germany), Hao Ken(National Research Institute for Earth Science and Disaster Resilience, Japan), Ma Kuo-Fong(Department of Earth Sciences, Institute of Geophysics, National Central University, Taiwan)

4:15 PM - 4:30 PM

[SSS05-04] Application of Standard Deviation for Single-station Ground-motion Prediction Model in a Probabilistic Seismic-hazard Analysis

*CHIH HSUAN SUNG1, CHYI TYI LEE1 (1.Institute of Applied Geology, National Central University)

Keywords:strong motion, prediction model, single station, single path, variance decomposition, standard deviation

The results of probabilistic seismic hazard analysis for empirical ground-motion prediction equations (GMPEs) are sensitive to the standard deviation, especially with long return periods. Recent studies have proven that variability decomposition cannot reduce the hazard level when moving the epistemic uncertainty into the logic tree unless we have a reasonable reason to remove it directly. In this study, we propose the use of single-station GMPEs to solve this problem. The single-station model is established from the observation records at a station, so the epistemic uncertainty of the site term can be ignored. We use 20,466 records for 506 crustal earthquakes with moment magnitudes greater than 4.0 obtained from the Taiwan Strong Motion Instrumentation Program (TSMIP) network to build the single-station GMPEs for 570 stations showing the peak ground acceleration (PGA) and spectral accelerations (SAs). The comparison is made of the total sigma of the regional GMPE (σT), the single-station sigma of the regional GMPE which is estimated by the variability decomposition method (σSS) and the single-station sigma of single-station GMPEs (σSS,S) for the different periods. Finally, we find that with the ideal for the path diagram approach as proposed by Sung and Lee (2016), we can separate the epistemic variance arising from single-station uncertainty, and through the decomposition of the variance components we can get the aleatory variability, which is indicative of the single-path sigma per station, σSP,S. Because the variability varies with geographical location, the analysis of the spatial correlation of these variances among sites can be plotted as σSS,S and σSP,S distribution maps. The results show that the σSS,S for the PGA is 50% to 70% smaller than the σT and the σSP,S for the PGA is 70% to 90% smaller than the σT in southern and northern Taiwan.