A Partially Non-Ergodic Ground-Motion Prediction Equation for Europe
Nicolas M. Kuehn, Frank Scherbaum

TL;DR
This paper develops a hierarchical, partially non-ergodic ground-motion prediction model for Europe and the Middle East, accounting for regional differences and borrowing strength across regions using Bayesian inference.
Contribution
Introduces a hierarchical Bayesian model for regional ground-motion prediction that captures regional differences and quantifies epistemic uncertainty.
Findings
Regionalized models outperform non-regional models.
Hierarchical modeling improves estimates for regions with limited data.
Models remain physically sound despite regional data disparities.
Abstract
A partially non-ergodic ground-motion prediction equation is estimated for Europe and the Middle East. Therefore, a hierarchical model is presented that accounts for regional differences. For this purpose, the scaling of ground-motion intensity measures is assumed to be similar, but not identical in different regions. This is achieved by assuming a hierarchical model, where some coefficients are treated as random variables which are sampled from an underlying global distribution. The coefficients are estimated by Bayesian inference. This allows one to estimate the epistemic uncertainty in the coefficients, and consequently in model predictions, in a rigorous way. The model is estimated based on peak ground acceleration data from nine different European/Middle Eastern regions. There are large differences in the amount of earthquakes and records in the different regions. However, due to…
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Taxonomy
TopicsSeismic Performance and Analysis · Structural Health Monitoring Techniques · Seismic Waves and Analysis
