Bayesian sampling of earthquakes finite-fault models

I use state of art Markov Chain Monte Carlo (MCMC) sampling methods to investigate co-seismic slip distribution of large earthquakes. In particular, I studied 3 large events in widely different tectonic contexts:

• Static co-seismic slip inversion of the 1992 Landers Earthquake and fault damage-zone

The Mw = 7.3 Landers Earthquake is a widely-studied Southern Californian Earthquake. My work consists of obtaining a stochastic solution of the static co-seismic slip distribution on the numerous faults that ruptured. We analyze this result through the prism of Shallow Slip Deficit observed for this earthquake (and many other strike-slip earthquakes). In particular, we investigate the effect a fault damage-zone has on the inferred slip distribution.

• Kinematic slip distribution and strain budget of the 2016 Pedernales Earthquake

A Mw = 7.8 Earthquake struck Ecuador on April 16th 2016. Using Bayesian sampling methods, I explore the rupture process using geodetical dataset and strong-motion observations. Using a stochastic coupling model on the subduction interface, we investigate the strain budget on the Ecuadorian subduction margin.

• Kinematic slip distribution of the 2017 Chiapas Earthquake

On September 8th, 2017, a powerful Mw = 8.1 earthquake struck the South coast of Mexico. Much like the 2016 Pedernales earthquake, I'm using MCMC sampling methods and an extensive geodetical and seismological dataset to further understand the mechanics of intra-slab events.