Kinematic source inversions of teleseismic data based on the QUESO library for uncertainty quantification and prediction

One fundamental aspect of seismic hazard mitigation is gaining a better understanding of the rupture process. Because direct observation of the relevant parameters and properties is not possible, other means such as kinematic source inversions are used instead. By constraining the spatial and temporal evolution of fault slip during an earthquake, those inversion approaches may enable valuable insights in the physics of the rupture process. However, due to the underdetermined nature of this inversion problem (i.e., inverting a kinematic source model for an extended fault based on seismic data), the provided solutions are generally non-unique.

Here we present a statistical (Bayesian) inversion approach based on an open-source library for uncertainty quantification (UQ) called QUESO that was developed at ICES (UT Austin). The approach has advantages with respect to deterministic inversion approaches as it provides not only a single (non-unique) solution but also provides uncertainty bounds with it. Those uncertainty bounds help to qualitatively and quantitatively judge how well constrained an inversion solution is and how much rupture complexity the data reliably resolve. The presented inversion scheme uses only tele-seismically recorded body waves but future developments may lead us towards joint inversion schemes.

After giving an insight in the inversion scheme ifself (based on delayed rejection adaptive metropolis, DRAM) we explore the method’s resolution potential. For that, we synthetically generate tele-seismic data, add for example different levels of noise and/or change fault plane parameterization and then apply our inversion scheme in the attempt to extract the (known) kinematic rupture model. We conclude with exemplary inverting real tele-seismic data of a recent large earthquake and compare those results with deterministically derived kinematic source models provided by other research groups.