Bayesian Dynamic Finite-Fault Inversion: 2. Application to the 2016 Mw 6.2 Amatrice, Italy, Earthquake

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

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32 Scopus citations


In 2016 Central Italy was struck by a sequence of three normal-faulting earthquakes with moment magnitude (Mw) larger than 6. The Mw 6.2 Amatrice event (24 August) was the first one, causing building collapse and about 300 casualties. The event was recorded by a uniquely dense network of seismic stations. Here we perform its dynamic source inversion to infer the fault friction parameters and stress conditions that controlled the earthquake rupture. We consider a linear slip-weakening friction law with spatially variable parameters along the fault. The inversion uses a novel Bayesian framework developed in our companion paper, which combines efficient finite-difference dynamic rupture simulations and the Parallel Tempering Monte Carlo algorithm to sample the posterior probability density function. The main advantage of such formulation is that by subsequent analysis of the posterior samples we can infer stable features of the result and their uncertainty. The inversion results in a million of visited models. The preferred model ensemble reveals intriguing dynamic features. The rupture exhibits a slow and irregular nucleation followed by bilateral rupture propagation through two asperities, accelerating toward the heavily damaged city of Amatrice. The stress drop reaches locally 10–15 MPa, with slip-weighted mean of 4–4.5 MPa. The friction drop ranges from 0.1 to 0.4. The characteristic slip-weakening distance is the most heterogeneously distributed dynamic parameter, with values of 0.2–0.8 m. The radiation efficiency was rather low, 0.2, suggesting that approximately 80% of the total available energy was spent in the fracture process, while just 20% was radiated by seismic waves.
Original languageEnglish (US)
Pages (from-to)6970-6988
Number of pages19
JournalJournal of Geophysical Research: Solid Earth
Issue number7
StatePublished - Jul 3 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. Duru for providing us with the WaveQLab3D solver and for his guidance in its usage. We are grateful to Olaf Zielke and an anonymous reviewer for their valuable comments that improved the presentation of our results. The observed data were downloaded from the Engineering Strong-Motion database (Luzi, Puglia, Russo, D'Amico, et al.,; Luzi, Puglia, Russo, & ORFEUS WG5,). Most of the calculations were carried out on 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 reference 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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