Detect and characterize swarm-like seismicity

Tectonic earthquake swarms exhibit a distinct temporal and spatial pattern compared to mainshock-aftershock sequences. Unlike the latter ones, where the earthquake sequence typically starts with the largest earthquake that triggers an Omori-Utsu temporal decay of aftershocks, earthquake swarms show a unique increase in seismic activity without a clear mainshock. The largest earthquake(s) in a swarm sequence often occur(s) later, and the sequence consists of multiple earthquake bursts showing spatial migration. This erratic clustering behavior of earthquake swarms arises from the interplay between the long-term accumulation of tectonic elastic strain and short-term transient forces. Detecting and investigating earthquake swarms challenges the community and ideally requires an unsupervised approach, which has led in recent decades to the emergence of numerous algorithms for earthquake swarm identification.In a comprehensive review of commonly used techniques for detecting earthquake clusters, we applied a blend of declustering algorithms and machine learning clustering techniques to synthetic earthquake catalogs produced with a state-of-the-art ETAS model, with a time-dependent background rate mimicking realistic swarm-like sequences. This approach enabled the identification of boundaries in the statistical parameters commonly used to distinguish earthquake cluster types, i.e., mainshock-aftershock clusters versus earthquake swarms. The results obtained from synthetic data helped to have a more accurate classification of seismicity clusters in real earthquake catalogs, as it is the case for the 2010-2014 Pollino Range (Italy) seismic sequence, the Húsavík-Flatey transform fault seismicity (Iceland), and the regional catalog of Utah (USA). However, the classification obtained through automated application of these findings to real cases depends on the clustering algorithm utilized, the statistical completeness of catalogs, the spatial and temporal distribution of earthquakes, and benefits of a posteriori manual inspection. Nevertheless, the systematic assessment and comparison of commonly used methods - benchmarked in this work to synthetics catalogs and real seismicity – allows the community to have clear and thorough guidelines to identify swarm-like seismicity.