Abstract
[1] Modern geodetic techniques, such as the global positioning system (GPS) and Interferometric Synthetic Aperture Radar (InSAR), provide high-precision deformation measurements of earthquakes. Through elastic models and mathematical optimization methods, the observations can be related to a slip distribution model. The classic linear, kinematic, and static slip inversion problem requires specification of a smoothing norm of slip parameters and a residual norm of the data and a choice about the relative weight between the two norms. Inversions for unknown fault geometry are nonlinear and, therefore, the fault geometry is often assumed to be known for the slip inversion problem. We present a new method to invert simultaneously for fault slip and fault geometry assuming a uniform stress drop over the slipping area of the fault. The method uses a full Bayesian inference method as an engine to estimate the posterior probability distribution of stress drop, fault geometry parameters, and fault slip. We validate the method with a synthetic data set and apply the method to InSAR observations of a moderate-sized normal faulting event, the 6 October 2008 Mw 6.3 Dangxiong-Yangyi (Tibet) earthquake. The results show a 45.0 ± 0.2° west dipping fault with a maximum net slip of ∼1.13 m, and the static stress drop and rake angle are estimated as ∼5.43 MPa and ∼92.5°, respectively. The stress drop estimate falls within the typical range of earthquake stress drops known from previous studies.
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