PREPARED

The project aims to build up and test a multi-parameter machine-learning strategy that will form the basis for the next generation systems for the automatic identification of the preparatory processes of large earthquakes in Italy. We will develop methods for the integrated analysis of seismological and geodetic data to transform them into features that provide information about the evolution of crustal stress conditions, the level of organization, and the fractal dimension of earthquake populations before moderate and large magnitudes. New methodologies will be implemented to analyze and characterize the seismological and geodetic data, including assessing their robustness and sensitivity to the physical processes occurring in different tectonic contexts. The new methodologies and technologies will be developed, applied and validated using the seismic data relevant to some of the most relevant earthquake sequences in Italy (e.g., the Mw 6.1, 2009 L’Aquila earthquake; the Mw 5.0, 2012 Pollino earthquake, in southern Italy; the Mw 5.8 and Mw 5.6, 2012 Emilia earthquakes; the Mw 6.5, 2016, Norcia earthquake).