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.