Data-driven constraints on earthquake modeling and rupture segmentation
from teleseismic multi-array backprojection and InSAR
Abstract
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.