Journal
BULLETIN OF EARTHQUAKE ENGINEERING
Volume 20, Issue 12, Pages 6377-6406Publisher
SPRINGER
DOI: 10.1007/s10518-022-01467-z
Keywords
Arias intensity; Earthquake-induced landslide; Sichuan-Yunnan region; Attenuation relationship
Funding
- National Natural Science Foundation of China [51808444, 51979218, U1965107]
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In this study, a region-specific Arias intensity attenuation relationship was developed using the China Strong-Motion Networks Center (CSMNC) database. It was recommended to be applied in the Sichuan-Yunnan region for moment magnitudes ranging between 4.2 and 7.0, distances ranging between 0 and 300 km, and with V-s30 ranging between 128 and 760 m/s. This relationship showed the best performance in fitting and predicting the data from the Sichuan-Yunnan region, although it had greater intra-event, inter-event, and total standard deviations compared to other regions.
Arias intensity is an essential ground motion measure correlating with the potential for earthquake-induced landslides. The Sichuan-Yunnan region, which is primarily mountainous, is a high incidence region of earthquake-induced landslides in China. However, there is no available attenuation relationship for this intensity measure due to the backward construction of the stations. In this study, we developed a region-specific Arias intensity attenuation relationship using the China Strong-Motion Networks Center (CSMNC) database which was established in 2008. We recommend this relationship be applied in the Sichuan-Yunnan region for moment magnitudes ranging between 4.2 and 7.0, distances ranging between 0 and 300 km and with V-s30 (the average shear-wave velocity in the upper 30 m of a soil profile) ranging between 128 and 760 m/s. The current study finds that this relationship's intra-event, inter-event, and total standard deviations are greater than for other regions. This is likely caused by the complicated seismotectonic activities, nonlinear site effects, error from inferring V-s30, basin effects, etc. However, this relationship has the best performance in fitting and predicting the data from the Sichuan-Yunnan region.
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