Sviluppare la teoria delle leggi costitutive dell'attrito per una faglia, studiare la nucleazione dei terremoti e la propagazione dinamica della rottura sismica. Fornire un background di base sulla fisica dei terremoti per comprendere i parametri della sorgente e le leggi di scala dei terremoti. Introdurre i concetti di indebolimento della faglia durate l’inizio della rottura elastodinamica.

 

This course will provide a comprehensive summary of how machine learning (ML) is being used to predict frictional failure events, the lab equivalent of earthquakes, and improve our understanding of the physics of catastrophic earthquakes on tectonic faults.  We will review the recent works, over the past few years, that solved a 50+ year old dilemma of how to predict the time to failure of lab earthquakes using laboratory seismic data. These works show: 1) that ML can predict the timing and magnitude of labquakes using acoustic emissions (AE) that originate in the lab fault zone, 2) that in addition to passive measurements of lab AE, active source measurements of changes in fault zone elastic properties during the lab seismic cycle can be used to predict lab earthquakes, and 3) that the lab-based work can be applied to tectonic faulting in at least special cases if not more generally. Recent work shows that labquakes are preceded by a cascade of AE events and systematic changes in elastic wave speed and transmitted amplitude that foretell catastrophic failure. As in all areas of AI, the methods being used for this problem evolve rapidly. We will discuss current techniques and traditional ML techniques based on regression. A primary goal of the course will be to provide the scientific background needed to understand tenets of earthquake physics while introducing students to ML/DL methods to predict failure time and magnitudes. We will also study DL-based methods to autoregressively forecast labquakes and fault zone shear stress. Students will learn how to use lab seismic data and also how to access earthquake data from regional networks in Italy and worldwide. Our studies of earthquake physics will include the evolution of frequency magnitude statistics during the lab seismic cycle, which provides an opportunity to use ML to interrogate the physics of impending failure.  We will also see how precursors to lab earthquakes, that can be identified by AI,  provide a sensible connection between the ML-based predictions, based on AE, and the physics of failure.  In the lab, AE events represent a form of foreshock and, not surprisingly, the rate of foreshock activity correlates with fault slip rate and its acceleration toward failure.  A central theme of the course will be to learn how lab earthquake prediction can improve forecasts of earthquake precursors and tectonic faulting.