Advances in physics-based earthquake simulations, utilizing high-performance computing, have been exploited to better understand the generation and characteristics of the high-frequency seismic wavefield. However, direct comparison to ground motion observations of a specific earthquake is challenging. We here propose a new approach to simulate data-fused broadband ground motion synthetics using 3D dynamic rupture modeling of the 2016 Mw 6.2 Amatrice, Italy earthquake. We augment a smooth, best-fitting model from Bayesian dynamic rupture source inversion of strong-motion data (
|Original language||English (US)|
|Journal||Geophysical Research Letters|
|State||Published - Nov 15 2022|
Bibliographical noteKAUST Repository Item: Exported on 2022-11-30
Acknowledgements: We thank Eric Dunham for helpful discussions on Text S2 in Supporting Information S1. We thank the Associate Editor Germán Prieto and two anonymous reviewers for their constructive suggestions. The work presented in this paper was supported by funding from the German Research Foundation (DFG, GA 2465/2-1). TT, AAG and TU acknowledge additional funding from the European Union's Horizon 2020 research and innovation programme (TEAR ERC Starting Grant 852992; ChEESE, grant agreement No. 823844; DT-Geo, grant agreement No. 101058129; Geo-Inquire, grant agreement No. 101058518), DFG (GA 2465/3-1), NSF (EAR-2121666) and SCEC (awards 20046, 21010). The authors acknowledge the Gauss Centre for Supercomputing e.V. (www.gauss-centre.eu, project pr63qo) for funding this project by providing computing time on the GCS Supercomputer SuperMUC-NG at Leibniz Supercomputing Centre (www.lrz.de), and King Abdullah University of Science and Technology (KAUST) Supercomputing Laboratory (www.hpc.kaust.edu.sa) for computing time on Shaheen II (project k1343).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)