An Empirical Ground-Motion Model Based on Truncated Regression: A Case Study in the Middle East
Session: Advances in Understanding Near-Field Ground Motions: Observation, Prediction and Application II
Type: Oral
Date: 4/22/2021
Presentation Time: 02:00 PM Pacific
Description:
We present an empirical earthquake ground-motion model (GMM) for the Middle East. The model is developed on a database of about 9500 strong-motion records from the Middle East, from which abou 2800 are used in model development. The model is developed as a Bayesian multi-level model, which accunts for any systematic group effects (event, site, region). Prior distributions for the parameters are based on physical considerations and predictions from global ground-motion models.
The strong-motion observations come from stations that only record data if the peak ground acceleration (PGA) exceeds a certain trigger threshold. This presents a problem, since no observations with PGA smaller than the trigger threshold are available. This results in a truncated distribution for PGA and spectral acceleration. If this is not taken into account during the model development, the model will be biased. Typically, this is done by using only data up to a certain (magnitude-dependent) distance, making the assumption that the effect of data truncation only has an influence at larger distances on the model. Such an approach would lead to a great reduction in the usable number of records for the database, which would lead to a poorly constrained model. Hence, we directly model the truncation of PGA and spectral periods in the regression. We model the joint occurrence of PGA and pseudo-spectral acceleration, while conditioning on the truncation for PGA.
The parameters of the model are estimated via Bayesian inference, with strong informative prior distributions based on published global GMMs. The estimated model, based on truncated regression, is in reasonable agreement with previously published global models, with a notably steeper attenuation compared to models that do ot account for truncation. We pay attention to epistemic uncertainty associated with the model predictions, which can be assessed from the posterior distribution of the model parameters.
Presenting Author: Nicolas Kuehn
Student Presenter: No
Authors
Nicolas Kuehn Presenting Author Corresponding Author kuehn@ucla.edu University of California, Los Angeles |
Tadahiro Kishida tadahiro.kishida@kustar.ac.ae Khalifa University of Science and Technology |
Mohammad AlHamaydeh malhamaydeh@aus.edu American University of Sharjah |
Yousef Bozorgnia yousefbozorgnia@ucla.edu University of Californa, Los Angeles |
Sean Ahdi sahdi@ucla.edu University of California, Los Angeles |
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An Empirical Ground-Motion Model Based on Truncated Regression: A Case Study in the Middle East
Session
General Session