Seismic tremors form a broad class of signals generated by internal sources that are different from regular earthquakes. Volcanic tremors have been known for a long time, and tectonic tremors associated with seismogenic fault zones have been described more recently. While the physical origin of seismic tremors remains to be fully understood, they are related to slow transient energy release processes that occur in active geological systems during the accumulation of mechanical energy that is then released during catastrophic events, such as strong earthquakes or volcanic eruptions. Therefore, seismic tremors represent a unique source of information that can be used to understand the physics of these ‘preparation’ processes and to design new monitoring and forecasting approaches.

Modern digital seismological networks record huge numbers of tremors in different active regions, and breakthroughs can be achieved with systematic exploration of these observations that includes data analysis and physical modeling. My goal is to undertake such an effort via the development of a new unified framework for the study of seismic tremors. I plan to combine advanced methods for data mining, signal processing, and numerical simulations of the generating processes, to apply these to different large datasets of volcanic and tectonic tremors.

I will develop an innovative and holistic approach based on massive analysis of observations that requires high performance computing and will be combined with advanced physical modeling of the generating dynamical processes. This will produce the new framework that can be used on the one hand for an understanding of the physical tremor-generating mechanisms, and on other hand for the development of new adaptive methods for monitoring volcanoes and seismic faults. The implementation of these will involve machine learning approaches to gain information from continuous fluxes of data from dense seismological networks.