Comparative Analysis of Seismic Clustering: Deterministic Techniques vs. Probabilistic ETAS Model

Short-term seismic clustering, a crucial aspect of seismicity, has been extensively studied in literature. Existing techniques for cluster identification are predominantly deterministic, relying on specific constitutive equations to define spatiotemporal extents. Conversely, probabilistic models, such as the Epidemic Type Aftershock Sequence (ETAS) model, dominate short-term earthquake forecasting. The ETAS model, known for its stochastic nature, has been employed to decluster earthquake catalogs probabilistically. However, the challenge arises when selecting a probability threshold for cluster identification, potentially distorting the model's underlying hypothesis.This study aims to assess the consistency between seismic clusters identified by deterministic window-based techniques specifically, Gardner-Knopoff and Uhrhammer-Lolli-Gasperini and the associated probabilities predicted by the ETAS model for events within these clusters. Both deterministic techniques are implemented in the NESTOREv1.0 package and applied to the Italian earthquake catalog spanning from 2005 to 2021.The comparison involves evaluating, for each event within an identified cluster, both the probability of independence and the expected number of triggered events according to the ETAS model. Results demonstrate overall agreement between the two cluster identification methods, with identified clusters exhibiting consistency with corresponding ETAS probabilities. Any minor discrepancies observed can be attributed to the fundamentally different nature of the deterministic and probabilistic approaches.This research is supported by a grant from the Italian Ministry of Foreign Affairs and International Cooperation.