EGU2020-20584
https://doi.org/10.5194/egusphere-egu2020-20584
EGU General Assembly 2020
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Dynamic Rupture and Ground Motion Modeling of the 2016 M6.2 Amatrice and M6.5 Norcia, Central Italy, Earthquakes Constrained by Bayesian Dynamic Source Inversion

Taufiq Taufiqurrahman1, Alice-Agnes Gabriel1, Frantisek Gallovic2, and Lubica Valentova2
Taufiq Taufiqurrahman et al.
  • 1Ludwig-Maximilians-Universität München, Geophysics, Department of Earth and Environmental Sciences, München, Germany
  • 2Charles University, Faculty of Mathematics and Physics, Department of Geophysics, Prague, Czech Republic

The complex evolution of earthquake rupture during the 2016 Central Italy sequence and the uniquely dense seismological observations provide an opportunity to better understand the processes controlling earthquake dynamics, strong ground motion, and earthquake interaction. 

We here use fault initial stress and friction conditions constrained by a novel Bayesian dynamic source inversion as a starting point for high-resolution dynamic rupture scenarios. The best-fitting forward models are chosen out of ~106 highly efficient simulations restricted to a simple planar dipping fault. Such constrained, highly heterogeneous dynamic models fit strong motion data well. Utilizing the open-source SeisSol software (www.seissol.org), we then take into account non-planar (e.g., listric) fault geometries, inelastic off-fault damage rheology, free surface effects and topography which cannot be accounted for in the highly efficient dynamic source inversion. SeisSol is based on the discontinuous Galerkin method on unstructured tetrahedral meshes optimized for modern supercomputers. 

We investigate the effects of including the realistic modeling ingredients on rupture dynamics and strong ground motions up to 5 Hz. Synthetic PGV mapping reveals that specifically fault listricity decreases ground motion amplitudes by  ~50 percent in the extreme near field on the foot-wall. On the hanging-wall shaking is increased by ~150 percent as a consequence of wave-focusing effects within 10 km away from the fault. Dynamic modeling thus suggests that geometrical fault complexity is important for seismic hazard assessment adjacent to dipping faults but difficult to identify by kinematic source inversions.

How to cite: Taufiqurrahman, T., Gabriel, A.-A., Gallovic, F., and Valentova, L.: Dynamic Rupture and Ground Motion Modeling of the 2016 M6.2 Amatrice and M6.5 Norcia, Central Italy, Earthquakes Constrained by Bayesian Dynamic Source Inversion, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20584, https://doi.org/10.5194/egusphere-egu2020-20584, 2020.

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Display material version 1 – uploaded on 06 May 2020
  • CC1: This is a summary of Qs and As from the live chat, Henriette Sudhaus, 07 May 2020

    Q: What sized structures (distances) do you think might be possible to resolve?
    A: we observe the effect of adding non-planar fault geometry (ie. listric fault geometry) up to 10km distance away from the fault
    A: the spatial resolution for the Amatrice dynamic source inversion was 100m (details here:https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019JB017512) - the more complex dynamic rupture models informed by the inversion best model can achieve higher resolution
    Comment: thanks. Will read the paper. 100 m is impressive if that is actually resolved

    Q: for the Norcia earthquake we learned earlier today that two separate segments are needed to explain the data. I was wondering how you then got the data fit with just one segment.
    A: Elisa [Tinti, D1619 EGU2020-10142, same session (HS)] inferred 2 plane model also used InSAR data, here the one plane model matches strong motion data

    Comment: Perhaps the second segment (if true) is "projected" on the main fault in the inversion result.

    A:  An interesting aspect in perfoming dynamic source inversion is the question which source complexity can be mapped from data to simple (geometry, friction) models and which not. Taufiq's inversion informed listric fault geometry model of the Amatrice event matches local data better then 10^6 simpler planar forward models ran in the framework of the Bayesion inversion