Refined Stochastic Source Modeling and Selection Method for Complex Fault Systems Considering Data Uncertainty: A Case Study of the 2019 Ridgecrest Earthquakes

Abstract The geometrical complexity of earthquake ruptures underscores the importance of surface deformation hazard and risk assessments considering multisegment rupture scenarios. This study presents a stochastic source modeling applied to surface displacement hazard analysis of a complex fault system where multiple, overlapping segments interact. A methodology is developed to account for the multisegment rupture feature of a complex fault system and the uncertainty of surface deformation data. The method uses geological field observations and remotely sensed data, such as Global Positioning System and Interferometric Synthetic Aperture Radar data. The stochastic source modeling characterizes the fault displacement hazard based on statistical scaling relationships and analytical equations for calculating the elastic deformation due to a fault rupture. The new method overcomes the limitation of the current stochastic source modeling approach, which has been previously applied to simple scenarios, in terms of defining multiple asperity zones for the ruptured system based on the released seismic moment scenario. The method is applied to the 2019 Ridgecrest earthquake sequence of moment magnitude (Mw) 7.1 and 6.4, for which the earthquake rupture geometry is complex with near-perpendicular segments. The results of the stochastic source characterization of the 2019 Ridgecrest earthquakes indicate that considering different released seismic moments and different slip syntheses for individual segments significantly enhances the performance of the method and the match of the corresponding surface displacements to the observed data. Moreover, the number of qualified source models based on defined criteria increases due to the increasing uncertainties of data. This means that higher uncertainty of data leads to less restrictive constraints on source models.