Rapid Estimation of Seismic Intensities Using a New Algorithm That Incorporates Array Technologies and Ground-Motion Prediction Equations (GMPEs)

地震学 地质学 强度(物理) 地震灾害 地震动 峰值地面加速度 地震模拟 振幅 断层(地质) 强地震动 大地测量学 算法 计算机科学 物理 量子力学
作者
Wenkai Chen,Dun Wang,Hongjun Si,Can Zhang
出处
期刊:Bulletin of the Seismological Society of America [Seismological Society of America]
卷期号:112 (3): 1647-1661 被引量:11
标识
DOI:10.1785/0120210207
摘要

ABSTRACT Rapid seismic intensity maps for damaging earthquakes enable the swift implementation of earthquake disaster mitigation action, issuance of accurate tsunami warnings, and prevention of associated secondary disasters. However, many countries lack dense local seismic observation networks, making it infeasible to obtain accurate seismic intensity maps of earthquakes within a few hours, particularly for earthquakes that have considerable source extents. In this study, we developed a new algorithm for rapidly obtaining seismic intensity maps of damaging earthquakes. With our model, source energy radiation is acquired using backprojection, and then the locations and relative amplitudes of the fault geometry and subevents are determined. Peak ground accelerations and peak ground velocities (PGVs) are subsequently calculated based on ground-motion prediction equations and the distribution of the estimated subevents. PGVs are then further site-corrected using the VS30 database (Wald and Allen, 2007; Heath et al., 2020). The algorithm was applied to the 2008 Mw 7.9 Wenchuan and 2010 Mw 6.9 Yushu earthquakes, and the resulting seismic intensity maps were highly similar to those generated by field surveys. The algorithm is simple and straightforward to use, and local real-time instrument observations are not required. Calculations can be performed automatically, and reliable seismic intensity maps can be issued within 30 min following damaging earthquakes. The model’s application may assist greatly with rescue and recovery efforts, and enable tsunami hazards to be evaluated immediately following earthquakes, particularly in regions lacking dense observation networks.
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