HYQUAKE - Hydromechanical coupling in tectonic faults and the origin of aseismic slip, quasi-dynamic transients and earthquake rupture

ID Call: ERC-2021-STG ERC Starting Grant

Ruolo di Sapienza nel progetto: Host Institution

Principal Investigator: Marco Maria Scuderi

Dipartimento: Scienze della terra

Data inizio progetto: 01/06/2022

Data fine progetto: 31/05/2027

Abstract del progetto:

Earthquakes and tectonic fault slip are among the most hazardous and unpredictable natural phenomena. Fluids play a key role in tectonic faulting and recent research suggests that fluids are central in both human induced seismicity and the mode of fault slip, ranging from episodic tremor and slip to slow earthquakes. However, the lack of accessibility to earthquake faults and the complexity of physical processes has limited our ability to develop holistic models for hydromechanical coupling in fault zones. Geophysical observations have the potential for illuminating precursors to failure for the spectrum of tectonic faulting, however we lack key laboratory data to connect these observations with predictive, physics-based models. The ambitious goal of HYQUAKE is to build a physically based framework to understand and predict fluid pressure induced fault slip for a range of fault motion, from aseismic creep to destructive earthquakes. The HYQUAKE approach is interdisciplinary and at the frontier of laboratory earthquake physics, seismology and data/computer science, with the goal of providing unprecedented quantitative constraints on the key physical processes that couple fault friction, the dynamics of strain localization and fluid flow controlling earthquakes and fault slip behavior. Specifically, I will build a research program around unusually well controlled rock deformation experiments tightly connected to numerical models of faulting. HYQUAKE will integrate lab data on fault zone elastic properties, frictional rheology, and hydromechanical parameters using state-of-the-art experimental equipment built within the project with machine learning to forecast labquake. Details of deformation processes, fluid flow, and fault failure will be imaged using novel acoustic techniques. These data will set the stage for the upscaling of laboratory observations to the prediction of natural faulting by coupling physics-based machine learning with 3D hydro-mechanical models.