Supervisors: Benjamin Joachimi (Astro), Ana Ferreira (Earth Sciences)
Entry year: October 2021
Determining the origin and mechanism of earthquakes from a series of recorded seismograms as well as from remote sensing (InSAR) data is a challenging non-linear inverse problem of processes that is typically numerically simulated using high performance computing facilities.
Recently, proof-of-concept projects have demonstrated that modern data analysis tools developed for the interpretation of large astrophysical datasets can be successfully adapted to greatly accelerate the location of faint seismic events. The approach combines data compression, Bayesian inference, artificial neural networks, and other machine learning techniques. This PhD project will further develop the method for use on real seismic and InSAR data from global earthquakes. The student will then apply it for the first time to the fast quantification of robust earthquake models and of their uncertainties, shedding new light on the fundamental mechanisms driving global seismicity. The project will unite expertise from geophysics, astrophysics, and data science in a strongly interdisciplinary environment.
Funding source: "Cosmoparticle" funding from Physics and Astronomy
For more information contact Benjamin Joachimi (email@example.com).