Volume 43 Issue 5
Sep.  2021
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Jia L,Xie J J,Li X J,Wen Z P,Chen W B,Zhou J. 2021. Empirical prediction models of time-averaged shear wave velocity vS20 and vS30 in Sichuan and Yunnan areas. Acta Seismologica Sinica,43(5):628−642 doi: 10.11939/jass.20200193
Citation: Jia L,Xie J J,Li X J,Wen Z P,Chen W B,Zhou J. 2021. Empirical prediction models of time-averaged shear wave velocity vS20 and vS30 in Sichuan and Yunnan areas. Acta Seismologica Sinica43(5):628−642 doi: 10.11939/jass.20200193

Empirical prediction models of time-averaged shear wave velocity vS20 and vS30 in Sichuan and Yunnan areas

doi: 10.11939/jass.20200193
  • Received Date: 2020-11-26
  • Rev Recd Date: 2021-01-21
  • Available Online: 2021-11-11
  • Publish Date: 2021-09-30
  • The time-averaged shear wave velocity of overburden soil is an important parameter for site classification and reflecting site effects on ground motion, which is widely used in earthquake ground motion prediction models. Using the lithology and wave velocity profile data of 973 boreholes in Sichuan and Yunnan, we study the regional prediction model of the average shear wave velocity. Based on the bottom constant velocity (BCV) model, log-linear model and Markov independent model, the empirical prediction models of vS20 and vS30 in this region were established. The results show that, the BCV method has the largest prediction error. When the depth of the shear wave velocity is less than 10 m, this method will significantly underestimate the average wave velocity of the actual site. Based on the log-linear model of Boore method, we establish an empirical prediction model. By comparison, we find that the average wave speed prediction results in Sichuan and Yunnan are close to those in Beijing and California, and significantly lower than those in Japan. Through the comparative analysis of prediction error of three different extrapolation methods, we find that the prediction results based on Markov independence model have the smallest error at different depths, and it is preferred to use this method to set up regional prediction model.


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