Uncovering environmental and other exotic seismic sources with machine learning

Seismology, beyond the study of earthquakes, has become an indispensable tool for understanding environmental changes, offering unique insights into a wide range of phenomena and natural risks, from slope instabilities to glacial dynamics and hydrological hazards. However, the sheer volume and complexity of modern seismic datasets, amplified by the emergence of dense seismic networks and technologies such as Distributed Acoustic Sensing (DAS), pose significant challenges. Recent advances in Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized our ability to analyze these datasets, enabling a deeper exploration of seismic data to find rare and exotic environmental seismic sources. 

Supervised learning approaches have been successfully applied to create large-scale instrumental catalogs of landslides and other environmental processes, at different  spatio-temporal scales, from short-term datasets recorded on dense local seismic stations networks, to chronicles spanning decades on seismic networks covering whole regions of the world (Alaska, Alps, Greenland). These techniques achieve high detection rates and robust classification of seismic events, even for low-magnitude or rare signals that traditional methods might overlook. Supervised learning approaches also allow us to advance our capability to estimate physical properties from seismic waves, such as the use of machine  learning to infer mass and kinematics of slope instabilities, which provide critical inputs for understanding the dynamics of these events and their associated hazards. These methodologies not only allow us to document environmental processes more exhaustively but also open up possibilities for studying poorly understood or previously undetectable seismic sources. Going beyond supervised learning, we have developed workflows based on self-supervised and unsupervised approaches to analyze continuous seismic data, uncovering unexpected patterns and revealing hidden environmental seismic sources recorded by dense seismic stations networks. Distributed Acoustic Sensing represents another frontier, turning fiber optic cables into dense seismic networks. By combining DAS with innovative AI-driven methods, we have demonstrated the potential to detect and classify low-magnitude earthquakes and anthropogenic sources, even in noisy environments, paving the way for real-time seismic monitoring on unprecedented scales.

By applying these AI-driven approaches, we are enhancing the field of environmental and exotic sources seismology, improving our ability to analyze vast seismic archives, and offering new tools to monitor, understand, and mitigate geohazards in a changing environment. This talk will highlight the latest methodological advances and showcase how they are applied to various geological and environmental contexts, from landslides, avalanches and glaciers in the Alps to fiber optic networks at different scales, underscoring the far-reaching implications of AI for seismological sources identification.