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

Gallovič, F. and Valentová, Ľ. and Ampuero, J.-P. and Gabriel, A.-A. (2019) Bayesian Dynamic Finite-Fault Inversion: 1. Method and Synthetic Test. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20191223-094147579

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Abstract

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 3D finite difference code for dynamic rupture simulation on a simplified geometry to compute slip rates, and pre-calculated 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, 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.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.31223/osf.io/tmjv4DOIDiscussion Paper
ORCID:
AuthorORCID
Gallovič, F.0000-0002-9268-3923
Ampuero, J.-P.0000-0002-4827-7987
Gabriel, A.-A.0000-0003-0112-8412
Additional Information:GNU Lesser General Public License (LGPL) 2.1. SUBMITTED ON February 07, 2019; LAST EDITED May 28, 2019. 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. 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, no. 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 no. 671698 and ChEESE, grant no. 823844), by the Volkswagen Foundation (ASCETE, grant no. 88479) and by KAUST-CRG (GAST, grant no. ORS-2016-CRG5-3027 and FRAGEN, grant no. ORS-2017-CRG6 3389.02).
Group:Seismological Laboratory
Funders:
Funding AgencyGrant Number
Grantová Agentura České Republiky18‐06716J
Deutsche Forschungsgemeinschaft (DFG)GA 2465/2-1
Agence Nationale pour la Recherche (ANR)ANR-15-IDEX-01
European Research Council (ERC)671698
European Research Council (ERC)823844
Volkswagen Foundation88479
King Abdullah University of Science and Technology (KAUST)ORS-2016-CRG5-3027
King Abdullah University of Science and Technology (KAUST)ORS-2017-CRG6 3389.02
Record Number:CaltechAUTHORS:20191223-094147579
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20191223-094147579
Official Citation:Gallovic, F., Valentova, L., Ampuero, J., & Gabriel, A. (2019, February 12). Bayesian Dynamic Finite-Fault Inversion: 1. Method and Synthetic Test. https://doi.org/10.31223/osf.io/tmjv4
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:100401
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:23 Dec 2019 17:52
Last Modified:23 Dec 2019 17:52

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