A Bayesian Source Characterization of the 19 May 2011 Mw 5.9 Simav Earthquake Using Joint Inversion of Seismic and Geodetic Data

Modern earthquake source estimation studies increasingly employ nonlinear optimization strategies to determine kinematic rupture parameters, often integrating geodetic and seismic data. The Mw 5.9 Simav Earthquake, which occurred on May 19, 2011, in the western Anatolia region of Turkey, serves as a significant case study for comprehensive source characterization. Understanding the active normal faulting mechanisms and stress distributions in this region is crucial. The earthquake occurred along the Simav Fault Zone (SFZ), an active fault system approximately 15–20 km in length and 2–3 km in width, characterized by WNW-ESE trending listric normal faults. This structure forms part of the broader extensional tectonic regime and graben systems that dominate western Anatolia, shaped by slab rollback and extensional forces driven by the subduction of the African Plate beneath the Anatolian Block along the Hellenic and Cyprus arcs. Surface ruptures trending WNW-ESE observed during the Simav Earthquake confirm the active nature of the fault.

Integrating seismic and geodetic data allows for more accurate estimation of source parameters. This process includes several key steps: modeling fault geometry, calculating Green's Functions (typically within a layered elastic half-space), and estimating distributed final slip alongside other kinematic source parameters. Aftershocks of the 2011 Simav Earthquake, concentrated at depths of 10 to 22 km, provide critical insights into fault geometry and rupture dynamics. Additionally, Coulomb stress analysis highlights the essential role of stress transfer in this region.

In this study, Bayesian inference was employed to integrate data and model uncertainties, yielding posterior distributions of source parameters. For Bayesian analysis, the Bayesian Earthquake Analysis Tool (BEAT) was utilized. BEAT is a robust tool specifically designed for modeling complex earthquake sources by integrating seismic and geodetic data. The Bayesian approach accounts for measurement and estimation errors, thereby reducing model uncertainties. Informative priors were applied to narrow the parameter space, resulting in more efficient and reliable outcomes. In the case of the Simav Earthquake, this method facilitated the robust determination of source parameters within the context of a layered medium.

Innovative sampling algorithms further enhanced the analysis by efficiently exploring high-dimensional parameter spaces, leading to improved estimates of fault geometry and mechanisms compared to earlier studies. These advancements provide a more robust understanding of source model parameters and their uncertainties. Comprehensive investigations of the Simav Fault Zone, incorporating surface ruptures and deformation analyses derived from GNSS data, significantly contribute to our understanding of the region's stress regime and seismic risk assessment.