Preparatory phase of large earthquakes illuminated by unsupervised categorization of earthquake catalog features

This study introduces an unsupervised machine learning framework to categorize seismicity patterns and identify, when present, seismicity transients preceding large earthquakes. Predicting large earthquakes remains a significant challenge due to the complexity of fault systems and the variability of preparatory processes. The authors focus on five large earthquakes and extract seismo-mechanical features per families of events, defined as clustered events in space, time and magnitude.

They show that for those cases displaying a preparatory phase, specific long-lasting families belonging to a critical category signalling an upcoming earthquake occur during the preparatory phase. Compared to other periods, critical categories reflect a higher spatial-temporal localization, earthquake interaction and strain release. The method will not detect such a transient for earthquakes with no detectable seismic preparatory phase. Finally, the researchers demonstrate that the method is capable of identifying preparatory phases (when present), showing potential for operational earthquake forecasting.