Correlation of Spatial Variations in Earthquake Stress Drop and Peak Ground Acceleration(PGA) from a Non-Ergodic Modeling Perspective
Shiying Nie, & Yongfei WangPublished September 10, 2023, SCEC Contribution #12894, 2023 SCEC Annual Meeting Poster #187
The seismic stress drop is a crucial earthquake parameter and can theoretically significantly impact ground motions, especially peak ground accelerations (PGAs). However, its incorporation into ground motion prediction equations (GMPEs) is uncommon, potentially due to the absence of direct evidence linking stress drop with GMPEs and notable measurement uncertainties. To bridge this gap, we propose applying the non-ergodic approach to both ground motion and stress drop databases. Firstly, this approach helps to extract spatially relevant epistemic uncertainties from ground motion models(GMM), including source terms resulting from incomplete source information in the model. Secondly, it facilitates retrieving spatially characteristic stress drops from their significant measurement errors. We reanalyzed Trugman and Shearer (2018)'s dataset which includes both recorded PGAs and estimated stress drops of 5297 earthquakes ranging from magnitude 2 to 4 in the San Francisco Bay Area. Herein, a Bayesian inference with Integrated Nested Laplace Approximations (INLA) is used to construct non-ergodic Ground Motion Models (GMMs) and stress drop models. Our results show an even stronger spatial correlation between the non-ergodic source terms of GMMs and stress drop. This highlights the dynamic stress drop's significant role in shaping source-related aspects of GMMs. As a result, our approach may have uncertainties of GMMs reduced by incorporating the stress drop database.
Key Words
non-ergodic ground motions; stress drop
Citation
Nie, S., & Wang, Y. (2023, 09). Correlation of Spatial Variations in Earthquake Stress Drop and Peak Ground Acceleration(PGA) from a Non-Ergodic Modeling Perspective. Poster Presentation at 2023 SCEC Annual Meeting.
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