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The physics of Earthquake faulting: learning from laboratory earthquake prediCTiON to Improve forecasts of the spectrum of tectoniC failure modes: TECTONIC

Periodic Reporting for period 1 - TECTONIC (The physics of Earthquake faulting: learning from laboratory earthquake prediCTiON to Improve forecasts of the spectrum of tectoniC failure modes: TECTONIC)

Reporting period: 2020-01-01 to 2021-06-30

Earthquakes represent one of our greatest natural hazards. Even a modest improvement in the ability to forecast these devastating events could save thousands of lives and billions of euros. The ERC-advanced project TECTONIC is focused on making such improvements by advancing our understanding of the physics of earthquake faulting using machine learning (ML). Current efforts to forecast earthquakes are limited in several ways; however, recent progress with predicting laboratory earthquakes suggests promising avenues for progress. These works show that lab earthquakes are preceded by a cascade of micro-failure events that radiate elastic energy in a manner that foretells catastrophic failure. We (the TECTONIC team) are working to understand the physics of such lab earthquakes and the processes that allow their prediction using ML. One focus of our work during the first phase of the project has been on the physical mechanisms that produce precursors to labquakes –that is changes in acoustic emission rates and elastic properties such as wave speed and transmitted amplitude. A key goal is to advance our understanding of these phenomena sufficiently to address the possibility that they could be detected prior to earthquakes on tectonic faults.
We have been developing and building the lab data set, constructing equipment to conduct new lab experiments at the Host Institution, working with seismic data and field observations from tectonic faults, and working to collect new geodetic data on the spectrum of fault slip behaviors on tectonics faults. We have published several papers, given talks at multiple scientific meetings, planned and hosted scientific meetings, and presented our project plans and results to the public. We are also: 1) training students and postdocs who will be the next generation of researchers in earthquake science and 2) leading a broad community collaboration in relation to the objectives of TECTONIC via a weekly, virtual seminar series that has been attended by more than 1000 participants
We are developing new methods to use machine learning to identify precursors to lab earthquakes and to predict the timing and magnitude of lab earthquakes as well as the fault zone stress state. One recent application is to map the distribution of a set of measurements onto another, such that for example a machine learning model trained on one dataset works on another. We want first to use this mapping from experiment to experiment, and then from experiment to field data. By the end of the project we expect to have developed methods to test the extent to which lab-based methods for prediction and forecasting can be applied to field data.