09 Jan 2023
09 Jan 2023
Status: this preprint is currently under review for the journal NHESS.

Accounting for path and site effects in spatial ground-motion correlation models using Bayesian inference

Lukas Bodenmann1, Jack W. Baker2, and Božidar Stojadinović1 Lukas Bodenmann et al.
  • 1Department of Civil, Environmental and Geomatic Engineering, ETH Zurich, Zurich, Switzerland
  • 2Department of Civil and Environmental Engineering, Stanford University, CA, USA

Abstract. Ground-motion correlation models play a crucial role in regional seismic risk modelling of spatially distributed built infrastructure. Such models predict the correlation between ground-motion amplitudes at pairs of sites, typically as a function of their spatial proximity. Data from physics-based simulators and event-to-event variability in empirically derived model parameters suggest that spatial correlation is additionally affected by path and site effects. Yet, identifying these effects has been difficult due to scarce data, and a lack of modelling and assessment approaches to consider more complex correlation predictions. To address this gap, we propose a novel correlation model that accounts for path and site effects via a modified functional form. To quantify the estimation uncertainty, we perform Bayesian inference for model parameter estimation. The derived model outperforms traditional isotropic models in terms of the predictive accuracy for training and testing data sets. We show that the previously found event-to-event variability in model parameters may be explained by the lack of accounting for path and site effects. Finally, we examine implications of the newly proposed model for regional seismic risk simulations.

Lukas Bodenmann et al.

Status: open (until 20 Feb 2023)

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Lukas Bodenmann et al.

Model code and software

Bayesian parameter estimation for ground-motion correlation models Lukas Bodenmann

Lukas Bodenmann et al.


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Short summary
Understanding spatial patterns in earthquake-induced ground-motions is key for assessing the seismic risk of distributed infrastructure systems. To study such patterns, we propose a novel model that accounts for spatial proximity, as well as site and path effects, and estimate its parameters from past earthquake data by explicitly quantifying the inherent uncertainties.