Bayesian Inversion of Finite‐Fault Earthquake Slip Model Using Geodetic Data, Solving for Non‐Planar Fault Geometry, Variable Slip, and Data Weighting. Issue 2 (28th January 2023)

• Record Type: Journal Article
• Authors: Wei, Guoguang; Chen, Kejie; Meng, Haoran
• Is Part Of: Journal of geophysical research. Volume 128:Issue 2(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-01-28
• Subjects: finite‐fault model -- Bayesian inversion -- spatially variable directions -- fault geometry -- objective data weighting -- Lushan earthquake; Geomagnetism -- Periodicals; Geochemistry -- Periodicals; Geophysics -- Periodicals; Earth sciences -- Periodicals; 551.1
• DOI: 10.1029/2022JB025225
• Languages: English
• ISSNs: 2169-9313

Abstract

Abstract: A precise finite‐fault model including the fault geometry and slip distribution is essential to understand the physics of an earthquake. However, the conventional linear inversion of geodetic data for a finite‐fault model cannot fully resolve the fault geometry. In this study, we developed a Bayesian inversion framework that can comprehensively solve a non‐planar fault geometry, the corresponding fault slip distribution with spatially variable directions, and objective weighting for multiple data types. In the proposed framework, the probability distributions of all the model parameters are sampled using the Monte Carlo method. The developed methodology removes the requirement for manual intervention for the fault geometry and data weighting and can provide an ensemble of plausible model parameters. The performance of the developed method is tested and demonstrated through inversions for synthetic oblique‐slip faulting models. The results show that the constant rake assumption can significantly bias the estimates of fault geometry and data weighting, whereas additional consideration of the variability of slip orientations can allow plausible estimates of a non‐planar fault geometry with objective data weighting. We applied the method to the 2013 M w 6.5 Lushan earthquake in Sichuan province, China. The result reveals dominant thrust slips with left‐lateral components and a curved fault geometry, with the confidence interval of the dip angles being between 20°–25° and 56°–58°. The proposed method provides useful insights into the scope of imaging a non‐planar fault geometry, and could help to interpret more complex earthquake sources in the future. Plain Language Summary: An earthquake originates from a rapid relative movement between two blocks on both sides of a fault. The earthquake can produce ground deformation, which can be measured by geodetic instruments deployed on the ground. Geophysicists can thus utilize the geodetic measurements to invert the earthquake attributes, including the fault geometry and the associated slips (i.e., the zone and amount of the relative movement between two blocks). Simultaneous estimation of the fault geometry parameters and the slip parameters is a nonlinear problem. The conventional inversion of geodetic data uses a linear adjustment method that cannot fully resolve the fault geometry. In addition, geodetic measurements have different precisions in terms of the types of instruments. We developed a nonlinear inversion based Bayesian inference framework to solve both these problems. The proposed method can correctly interpret an earthquake source with a non‐planar fault geometry. We applied the method to the 2013 M w 6.5 Lushan earthquake in southwest China and determined a statistical estimate of the fault geometry and slips. The results indicate that the proposed method could help to improve our knowledge of complicated earthquake sources in the future. Key Points: We develop a Bayesian finite‐fault inversion for non‐planar fault geometry and spatially variable slip amplitude and rake Accounting for variable directions of fault slips allows to better constrain non‐planar fault geometry and tune objective data weighting Accounting for the non‐planar fault geometry, the 2013 Lushan earthquake modeling reveals a dominant thrust slip with sinistral component