Bayesian Dynamic Finite-Fault Inversion: 1. Method and Synthetic Test

Frantisek Gallovic, Valentová, Jean-Paul Ampuero, Alice-Agnes Gabriel

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


Dynamic earthquake source inversions aim to determine the spatial distribution of initial stress and friction parameters leading to dynamic rupture models that reproduce observed ground motion data. Such inversions are challenging, particularly due to their high computational burden; thus, so far, only few attempts have been made. Using a highly efficient rupture simulation code, we introduce a novel method to generate a representative sample of acceptable dynamic models from which dynamic source parameters and their uncertainties can be assessed. The method assumes a linear slip-weakening friction law and spatially variable prestress, strength, and characteristic slip-weakening distance along the fault. The inverse problem is formulated in a Bayesian framework, and the posterior probability density function is sampled using the Parallel Tempering Monte Carlo algorithm. The forward solver combines a 3-D finite difference code for dynamic rupture simulation on a simplified geometry to compute slip rates and precalculated Green's functions to compute ground motions. We demonstrate the performance of the proposed method on a community benchmark test for source inversion. We find that the dynamic parameters are resolved well within the uncertainty, especially in areas of large slip. The overall relative uncertainty of the dynamic parameters is rather large, reaching ~50% of the averaged values. In contrast, the kinematic rupture parameters (rupture times, rise times, and slip values), also well resolved, have relatively lower uncertainties of ~10%. We conclude that incorporating physics-based constraints, such as an adequate friction law, may serve also as an effective constraint on the rupture kinematics in finite-fault inversions.
Original languageEnglish (US)
Pages (from-to)6949-6969
Number of pages21
JournalJournal of Geophysical Research: Solid Earth
Issue number7
StatePublished - Jun 26 2019
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-10
Acknowledged KAUST grant number(s): ORS-2016-CRG5-3027, ORS-2017-CRG6 3389.02
Acknowledgements: We thank L. Hanyk and J. Premus for their help with the software development and K. C. Duru for providing us with the WaveQLab3D solver and for his guidance in its usage. We are grateful to the Associate Editor and two anonymous reviewers for their valuable comments that improved the presentation of our results. The paper makes use of synthetic data of Source Inversion Validation benchmark Inv1 ( Most of the calculations were carried out on the Salmon supercomputer (Ostrava), supported by the Ministry of Education, Youth and Sports from the Large Infrastructures for Research, Experimental Development and Innovations project IT4Innovations National Supercomputing Center-LM2015070 (IT4I supercomputer). F. G., L. V., and A.-A. G. acknowledge financial support through the bilateral project of the Czech Science Foundation and DFG, 18-06716J and GA 2465/2-1, respectively. J.-P. A. acknowledges support by the French government, through the UCAJEDI Investments in the Future project managed by the National Research Agency (ANR) with the reference number ANR-15-IDEX-01. A.-A. G. acknowledges support by the European Union's Horizon 2020 research and innovation program (ExaHyPE, Grant 671698, and ChEESE, Grant 823844), by the Volkswagen Foundation (ASCETE, Grant 88479), and by KAUST-CRG (GAST, Grant ORS-2016-CRG5-3027, and FRAGEN, Grant ORS-2017-CRG6 3389.02).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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