Unraveling Earthquake Clusters Composing the 2014 Alto Tiberina Earthquake Swarm via Unsupervised Learning

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. An earthquake swarm is characterized by an elevated rate of earthquake occurrences in a specific region, surpassing the typical seismic activity, without being preceded by a major seismic event. Earthquake swarms are thought to be driven by mechanisms like slow deformation or the migration of fluids. In this study we take a closer look into the seismicity of an earthquake swarm in the Apennines in Italy, exploiting unsupervised machine learning methods. This approach indicates that the swarm sequence consists of a general, smooth increase in seismicity, occasionally punctuated by short accelerations of seismic activity. The subsequent analysis of the accelerated seismicity suggests a connection with several small, slow deformation processes that collectively constitute a large-scale deformation process, as measured by geodetic data. Combining hierarchical and density-based clustering enables to automatically separate background and clustered seismicityThe quantitative analysis of the extracted clusters reveals a swarm-like characterThe swarm-like clusters further indicate relations to aseismic processes