Understanding earthquakes with astrophysical methods

Supervisors: Benjamin Joachimi (Astro), Ana Ferreira (Earth Sciences)

Entry year: October 2021

Project description: The determination of accurate earthquake locations and faulting mechanisms is crucial for enhanced quantifications of seismic hazard and disaster response efforts. Initial estimates of earthquake models within minutes of the event are key to quantify the extent and severity of the resulting damage. On a longer timescale, robust earthquake models are essential for accurate predictions of aftershocks and for active tectonic studies, both of which are vital for understanding seismic hazard and earthquake physics.

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 (b.joachimi@ucl.ac.uk).