We are looking for a highly motivated PhD student to study the Seismic Signatures of Aseismic Processes, using machine and deep learning techniques.
The PhD position is one out of 11 positions at universities from across Europe, which together form a Marie Sklodowska-Curie Doctoral Network: the TREAD project ('daTa and pRocessess in sEismic hAzarD'). That is, your project will be integrated with 10 other, related PhD projects. While mainly based at ETH Zurich, you will also be working with Université Grenoble-Alpes in France, and you will regularly participate in training and science meetings with your doctoral network fellows.
You can find further details here.
Job description
Main Supervisor: Men-Andrin Meier (ETH Zurich); Co-Supervisor: David Marsan (UGA)
Objectives: Aseismic processes can play a first-order role in the build-up to large earthquakes, but they are hard to detect and monitor. The strong recent advances in deep learning powered seismic monitoring is an opportunity to fundamentally improve the detection and characterisation of the subtle seismic signatures that aseismic processes leave behind. The doctoral candidate will i) develop new DL methods that are tailored to characterise the seismic signatures of aseismic slip during earthquake sequences, in particular stress migration and rotation, strain acceleration as captured by repeating earthquakes, and fluid pressure build-up as evidenced by seismic swarms; and ii) study the predictive value of aseismic observations for anticipating large earthquakes. We will use some of the recent, exceptionally well recorded earthquake sequences to constrain transient aseismic deformation, including deformation caused by underground fluid flow. From this observational basis we will be able to develop a mixture seismicity model that accounts for the observed triggering of earthquakes by both previous shocks and by aseismic transients. This will allow us to study how the total deformation is partitioned into seismic and aseismic contributions, in space and in time. The goal is to understand the physics of the hard-to-observe aseismic deformation, and to design a seismicity model that provides substantially improved probability gain, compared to state-of-the-art models.
Expected Results: (1) Development and implementation of DL monitoring method to generate next-level, deep seismicity catalogues; (2) Observational monitoring and inference of aseismic deformation, and their underlying driving mechanisms such as fluid flow; (3) Operational seismicity model for predicting the evolution of earthquake sequences; (4) Improved understanding of the interactions between seismic and aseismic deformation mechanisms; (5) Data-driven, objective inference of fault structures and geometries at small, decametre scale.
Your profile
- We are looking for an enthusiastic researcher who is curious about earthquake science and hazard, and who likes to work as part of a team.
- Programming skills and a strong interest in quantitative and deep learning methods are appreciated.
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