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

Bodenmann, Lukas; Baker, Jack W.; Stojadinović, Božidar

doi:https://doi.org/10.5194/nhess-23-2387-2023

Articles | Volume 23, issue 7

https://doi.org/10.5194/nhess-23-2387-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/nhess-23-2387-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 23, issue 7

Research article

|

05 Jul 2023

Research article |

| 05 Jul 2023

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

Lukas Bodenmann, Jack W. Baker, and Božidar Stojadinović

Data sets

Ground motions from the 2019 Ridgecrest, California J. Rekoske, E. M. Thompson, M. P. Moschetti, M. G. Hearne, B. T. Aagaard, and G. A. Parker https://doi.org/10.5066/P9REBW60

Model code and software

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

spatialCorrelationEstimation bakerjw https://github.com/bakerjw/spatialCorrelationEstimation/

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.