Preprints
https://doi.org/10.5194/nhess-2022-267
https://doi.org/10.5194/nhess-2022-267
09 Jan 2023
 | 09 Jan 2023
Status: a revised version of this preprint was accepted for the journal NHESS and is expected to appear here in due course.

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

Lukas Bodenmann, Jack W. Baker, and Božidar Stojadinović

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: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-267', Anonymous Referee #1, 06 Feb 2023
    • AC1: 'Preliminary Reply on RC1', Lukas Bodenmann, 28 Feb 2023
  • RC2: 'Comment on nhess-2022-267', Anonymous Referee #2, 27 Feb 2023
    • AC2: 'Preliminary reply on RC2', Lukas Bodenmann, 17 Mar 2023
  • RC3: 'Comment on nhess-2022-267', Anonymous Referee #3, 10 Mar 2023
    • AC3: 'Preliminary reply on RC3', Lukas Bodenmann, 17 Mar 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-267', Anonymous Referee #1, 06 Feb 2023
    • AC1: 'Preliminary Reply on RC1', Lukas Bodenmann, 28 Feb 2023
  • RC2: 'Comment on nhess-2022-267', Anonymous Referee #2, 27 Feb 2023
    • AC2: 'Preliminary reply on RC2', Lukas Bodenmann, 17 Mar 2023
  • RC3: 'Comment on nhess-2022-267', Anonymous Referee #3, 10 Mar 2023
    • AC3: 'Preliminary reply on RC3', Lukas Bodenmann, 17 Mar 2023

Lukas Bodenmann et al.

Model code and software

Bayesian parameter estimation for ground-motion correlation models Lukas Bodenmann https://doi.org/10.5281/zenodo.7124213

Lukas Bodenmann et al.

Viewed

Total article views: 851 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
641 191 19 851 5 5
  • HTML: 641
  • PDF: 191
  • XML: 19
  • Total: 851
  • BibTeX: 5
  • EndNote: 5
Views and downloads (calculated since 09 Jan 2023)
Cumulative views and downloads (calculated since 09 Jan 2023)

Viewed (geographical distribution)

Total article views: 833 (including HTML, PDF, and XML) Thereof 833 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 26 May 2023
Download
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.
Altmetrics