Description du sujet de thèse

Multi-scale seismic characterization of active rockslides: from internal deformation to mass movement dynamics.

This PhD project aims to advance our understanding of the dynamics of large-scale, active rockslides. The research will focus on two primary objectives: (1) to analyze and cluster internal microseismic activity to identify and interpret seismic events linked to rockslide internal deformation and material damage, and (2) to develop a novel approach that combines seismic methods with granular flow simulations to characterize the complex dynamics of mass movements.

Contexte de travail

Axis 1: Internal damage and failure precursors
The main focus of the project involves leveraging data from a spatially dense seismic array to gain insights into rockslide behavior. Seismic array processing techniques, machine learning, and deep learning algorithms, will be employed to detect, locate, and classify microseismic events within the rockslide body. This analysis will help improve the spatio-temporal characterization of rockslide dynamics, focusing on identifying internal deformation processes and potential precursors to failure.
Axis 2: Mass movement dynamics
The secondary axis involves the development of a novel methodology that integrates seismic observations of mass movements with numerical simulations of granular flow. Bridging the gap between field data and theoretical models will provide a comprehensive framework for interpreting seismic signals and understanding the mechanical processes governing mass movement dynamics.
This multi-scale approach has the potential to significantly enhance our ability to monitor landslide hazards. The main study site for this project is an active and well instrumented rockslide located in Switzerland for which multiple seismic data sets are available. The PhD student will have an opportunity to participate in field measurements. The PhD project is part of the ERC project UNREST-UNveiling dynamics of Rapid Erosion through advanced Seismic Techniques, led by Malgorzata Chmiel.
Supervisory Team:
The project is supervised by Malgorzata Chmiel (Géoazur, CNRS) and Françoise Courboulex (Géoazur, CNRS). The PhD student will work in close collaboration with Martijn van den Ende (Géoazur, CNRS), Fabian Walter (WSL, Switzerland), and Perry Bartelt (RAMMS AG).
Requirements:
Candidates with a master level in geophysics, physics, applied mathematics, or computer science will be considered. Expertise in high-level programming language like MATLAB or Python is essential. The project involves collaboration with institutions in Switzerland.