In 2016 Central Italy was struck by a sequence of three normal-faulting earthquakes with moment magnitude (M w ) larger than 6. The M w 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.

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.

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.

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 (08/24) 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 towards the heavily damaged city of Amatrice. The stress drop reaches locally 10-15 MPa, with slip-weighted mean of 4-4.5MPa. 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 0.2-0.8m. 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.

Simulations of broadband (>1 Hz) ground motions are of great importance to seismologists and the earthquake engineering community. Even though we often lack detailed knowledge of the subsurface and earthquake source processes at small scales, it is essential to understand the generation and characteristics of the high-frequency seismic wavefield coinciding with most buildings' resonance frequencies. Broadband ground motions have been successfully simulated using hybrid techniques (e.g., Graves & Pitarka, 2010;Mai et al., 2010; Sommerville et al., 1999) that combine low-frequency deterministic ground motion synthetics with stochastically generated high-frequency components. While classical kinematic approaches are tremendously useful specifically for seismic hazard assessment and engineering, they do not guarantee a physically consistent source description and do not permit data-driven inferences on the fundamentals of how faults slip co-seismically, specifically on smaller scales (Burjánek & Zahradník, 2007;Mai et al., 2016;Tinti et al., 2005). Dynamic rupture models provide mechanically viable correlations among macroscopic earthquake rupture parameters, such as slip rate and rupture time, rooted in laboratory-derived friction laws and elastodynamics (Guatteri et al., 2004;Savran & Olsen, 2020;Schmedes et al., 2010). Nevertheless, mainly due to the associated computational demands at high frequencies, fully dynamic rupture scenarios have rarely been validated against real seismograms in a broad frequency range.