Is this blob real? Resolution analysis of earthquake slip distributions using Bayesian inference techniques
Understanding how earthquakes happen is key to unravel the physics of earthquake sources and, ultimately, to quantify seismic hazard. Earthquakes are typically studied by building space-time maps of earthquake slip from seismic data recorded worldwide using geophysical inverse approaches. However, the retrieved maps can be highly non-unique and yield large uncertainties, which are rarely quantified. This project addresses these issues by using recently developed Bayesian inference approaches to assess the resolution of earthquake slip distributions. The reversible jump Markov Chain Monte Carlo technique will be used for the first time to assess which and how many slip parameters can be constrained by global seismic datasets, including comprehensive uncertainty analyses. The student will receive training in retrieving and analysing big global seismic data sets, in statistical modelling and interpretation. This self-contained project will potentially open the way for more quantitative interpretations of earthquake slip maps and of their implications.