Bayesian Inference of Fault Slip Heterogeneity and Overlap: Application to 2019 Ridgecrest Earthquakes and Afterslip

Abstract

Fault slip occurs in both seismic and aseismic styles with spatial variability across scales, yet non‐unique source inference limits our ability to resolve slip complexity and dynamics. Here we evaluate the effect of spatial smoothing regularization in Bayesian frameworks by comparing an unregularized (BUR) approach that samples distributed slip parameters and a regularized (BR) approach that samples hyperparameters for smoothed slip inversions. Synthetic tests demonstrate their performance differences under varying geodetic data accuracy, spatial coverage, slip heterogeneity, and prior choices. With sufficient data accuracy and coverage, BUR models preserve slip roughness and perform better than BR models, particularly at shallower depths and for high‐gradient or irregular slip patterns. We apply both methods to joint Interferometric Synthetic Aperture Radar/Global Navigation Satellite System inversions of the 2019 Ridgecrest earthquake and 3‐month early afterslip. Integrating a layered elastic structure, near‐fault data, and their uncertainties is essential for accurately resolving shallow slip variations. Inferred large‐slip estimates are consistent with field measurements, while discrepancies at geometrically complex zones are attributable to off‐fault deformation and near‐surface slip gradients. A comparison with seven published models shows similar along‐strike moment distributions, while our BUR model concentrates of total mo6ment at seismogenic depths ( km), suggesting spatially variable and locally high static stress drops. Coseismic and postseismic slip zones have about moment overlap for 10%‐peak‐slip thresholds in BUR models, well below 60% in BR models. These results indicate that the unregularized approach can minimize artifacts and better capture characteristics and relationships of different slip modes, advancing studies of fault rheology and heterogeneity.