Multi-scale seismicity: from the laboratory to natural fault systems

Earthquake generation process is highly nonlinear and the physical parameters governing the natural system are difficult to constrain. Yet, natural seismicity exhibits some systematic statistical behavior (scaling laws, space and time clustering, foreshock-mainshock-aftershock sequences, susceptibiility to small perturbations). In the framework of ANR project PREMs (Predictability of Earthquakes and Mathematical Models; https://aochihi.github.io/anr-prems/), we aim to understand the physics behind these detectable observables.

The main purpose of this thesis is to consider how physics-based simulations can constrain seismicity. On one hand, recent laboratory experiences now provide high quality of microseismic catalogs under controlled conditions. On the other hand, physics-based models have been developed with various ingredients such as rate- and state-dependent friction, multi-scale heterogeneity in fault friction, spatial distribution of seismogenic faults, as well as various stress loading system. In this project, we first aim to start modelling the experimental scale, emphasizing on the question of predictability of the seismicity in controlled laboratory settings. In a second step, we hope to extend our model to the field scale. A highly motivated student is expected to work to:

• develop numerical method based on the boundary integral equation method.
• Carry out sensitivity tests in numerical modeling.
• Analyze the simulation results through statistical quantities.
• Participate to laboratory experiments
• Analyze laboratory and field seismicity catalogs

We seek an internationally competitive and highly motivated student. It is expected to collaborate with the researchers from the University of Tokyo, in particular, and participate actively in the international communications.

The PHD student is expected to work collaboratively with different researchers and students within BRGM, ENS and the other partners of the project PREMs.

- Master’s degree in Earth Sciences, Physics or Engineering - Motivation - Capacity of work in a team - Programming in Matlab, Python or other scientific software - Prior knowledge in numerical modelling or experimental techniques is a plus - Writing & reading scientific papers