Earthquakes have been observed to rupture in segments. A good understanding of rupture segmentation is important to characterize fault geometries at depth for follow-up tectonic, stress-field or other analyses. Earthquakes with magnitudes Mw<7 are however often modeled with simple source models. We propose a data-driven strategy and develop pre-optimization methods for a segmentation-sensitive source modeling analysis.
The first method we develop is a time-domain, multi-array backprojection of teleseismic data to infer the spatio-temporal evolution of the rupture, including a potential occurrence of rupture segmentation. We calibrate the backprojection using empirical traveltime corrections and we provide robust precision estimates based on bootstrapping of the travel-time models and array weights. Secondly we apply image analysis methods on InSAR surface displacement maps to infer modeling constraints on rupture characteristics (e.g. strike and length) and the number of potential segments.
Both methods can provide model-independent constraints on fault location, dimension, orientation and rupture timing, applicable to form prior probabilities of model parameters before modeling.
We use the model-independent constrains delivered by these two newly developed methods to inform a kinematic earthquake source optimization about parameter prior probability estimates.
We demonstrate and test our methods based on synthetic tests and an application to the 25.11.2016 Muji Mw 6.6 earthquake. Our results indicate segmentation and bilateral rupturing for the 2016 Muji earthquake. The results of the backprojection using high-frequency filtered teleseismic wavforms in particular shows the capability to illuminate the rupture history with the potential to resolve the start and stop phases of individual fault segments.