HOw Predictable are Earthquakes

Introduction

Earthquakes are spectacular natural disasters, as exemplified by the 2004 Sumatra and 2011 Tohoku-Oki earthquakes. Predicting earthquakes remains one of the biggest societal challenges in natural science. This research project will attempt to answer the following question: How predictable are earthquakes?

Research Approach

We propose a multidisciplinary approach articulated around three main axes:

1. Deterministic Predictability in Simple Faults
   This will be studied by reproducing and understanding earthquake phenomena in the laboratory.

2. Deterministic Predictability in Complex Faults
   This will be studied by laboratory experiments producing multiple earthquake cycles on faults with controlled heterogeneities.

3. Statistical Predictability of Earthquakes
   This will be studied by forecasting the spatial distribution of experimental seismicity using machine learning.

Experimental Setup

At the core of this project lies the development of a new dedicated experimental setup to generate multiple earthquake cycles along a fault with prescribed complex geometry and rheology.

Experimental Program

With this new capability, we will conduct a threefold experimental program to:

1. Compute the complete energy budget of laboratory earthquakes.
2. Study the sensitivity of rupture nucleation, propagation, and arrest to heterogeneities.
3. Study the effect of heterogeneities on the relation between fault seismic coupling and seismicity.

Expected Outcomes

Our work will provide insights for earthquake hazard mitigation, constrain the physics underlying ubiquitously observed seismological statistical laws (Omori, Gutenberg-Richter), and test seismic slip inversion and dynamic rupture modeling techniques in unprecedented data sets on rock fracture dynamics in experiments that mimic field conditions.

Infrastructure Development

The new infrastructure we plan to install will reproduce earthquake rupture processes with a spatio-temporal imaging resolution never achieved before.