Unraveling Earthquake Clusters Composing the 2014 Alto Tiberina Earthquake Swarm via Unsupervised Learning
David Essing, Piero Poli
Abstract
Abstract Earthquake swarms represent a particular mode of seismicity, not directly related to the occurrence of large earthquakes (e.g., aftershocks) but rather driven by external forcing such as aseismic deformation or fluid migration in fault systems. Sometimes their occurrence overlaps with observable geodetic signals in space and time, indicating a direct link. However, the low resolution of geodetic observations tends to obscure the small scale spatial and temporal dynamics of swarms. In this work, we automatically extract clusters of seismicity related to the 2014 Alto Tiberina swarm sequence (Italy) using an unsupervised clustering approach that exploits space and time information of the seismicity. The quantitative characterization of each cluster indicates that the overall swarm is composed of spatially and temporally confined (sub) swarms each of which could potentially be driven by small‐scale aseismic deformation process. This observation aligns with similar findings during slow slip events in subduction zones.