Earthquake declustering in Central Italy

Several techniques for earthquake clusters identification have been developed in the literature. They usually rely on different underlying assumptions and lead to different classifications of earthquakes into background events and clustered events. In this study we consider the approach proposed by Varini et al. (2020), in which the seismicity of North-Eastern Italy was analysed by considering two recent data-driven declustering techniques, one based on nearest-neighbor distance and the other on the Epidemic Type Aftershock Sequence stochastic model. Our goal is to similarly investigate the seismicity that has occurred in Central Italy over the last forty years, also taking into account the possible effects of changing the minimum magnitude threshold used for the analysis. In all cases, we found that the two declustering algorithms produce similar partitions of the earthquake catalogue into background events and earthquake clusters, but they may differ in the identified topological structure of the clusters. The clusters obtained from the stochastic method often have a deeper complexity than the clusters from the nearest-neighbor method. Centrality measures, mainly used in network theory, can recognize and quantify similarities and differences in the identified topological structures.