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

Abstract

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.