Comparison of the Recorded Frequency Content Parameters of the 2013 Lushan Earthquake with Empirical Model Predictions

2018 ◽  
Vol 108 (6) ◽  
pp. 3358-3367 ◽  
Author(s):  
Yuzhu Bai
2019 ◽  
Vol 109 (6) ◽  
pp. 2325-2339
Author(s):  
Yuzhu Bai

Abstract Using 72 free‐field accelerograms with the closest site‐rupture distances (Rrup) less than 300 km, this study calculates the recorded significant duration of the 2013 Mw 6.7 Lushan earthquake and compares the calculated results with empirical model predictions. The significant duration parameters are for 5%–75% (D5–75) and 5%–95% (D5–95) of the normalized Arias intensity. For Rrup<150  km, the models of Du and Wang (2017; hereafter, DW17) are relatively less biased in predicting the Lushan significant duration. The goodness‐of‐fit results indicate that the models of DW17, Afshari and Stewart (2016; hereafter, AS16), and Kempton and Stewart (2006; hereafter, KS06) are the best models to predict D5–75 of the Lushan event. Meanwhile, most of the selected empirical models predict the Lushan event better for D5–75 than for D5–95. Moreover, the enrichment of high‐frequency components of ground motion causes the recorded D5–95 of the Lushan event to be larger than median predictions in high‐frequency bands. Among the five selected earthquakes (1994 Mw 6.7 Northridge, 2003 Mw 6.5 San Simeon, 2004 Mw 6.6 Niigata, 2008 Mw 7.9 Wenchuan, and 2013 Mw 6.7 Lushan earthquakes), the Lushan earthquake is predicted relatively better by all empirical models. The intraevent and interevent residuals of AS16 model from China earthquakes are within the scatter of those from the events in the Next Generation Attenuation‐West2 (NGA‐West2) database, indicating that the China earthquake significant duration is consistent with the model developed through the NGA‐West2 database. Furthermore, the regional dependences of significant duration are not observed for the Wenchuan or the Lushan earthquake.


2015 ◽  
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pp. 3507-3526 ◽  
Author(s):  
Zhi Wang ◽  
Jinrong Su ◽  
Cunxi Liu ◽  
Xuelin Cai

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Author(s):  
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