Investigating earthquake clusters complexity in Central Italy by network theory tools

Complex network theory has been recently applied to get new insights and a different perspective in the study of earthquake patterns. Several studies (see for instance Daskalaki et al., J Seismol, 2016; Telesca, Phys. Chem. Earth, 2015; Varini et al., J Geophys Res, 2020; Ebrahimi et al., Chaos Solitons Fractals, 2021, and references therein) were based on the preliminary mapping of the time series of earthquakes into networks, by applying visibility graph method or other clustering algorithms. In a second step, the topological properties of the obtained networks were analyze by exploiting tools of complex network theory with the aim of discovering possible precursory signatures of strong earthquakes or other features relevant to hazard assessment.

In this study we investigated the earthquake clusters extracted by two data-driven declustering algorithms: the nearest-neighbor, which classifies the earthquakes on the basis of a nearest-neighbor distance between events in the space-time-energy domain (Zaliapin and Ben-Zion, J Geophys Res, 2013), and the stochastic declustering, which is based on the space-time ETAS point process model (Zhuang et al., J Geophys Res, 2004). Case studies from selected sequences, occurred in Central Italy from 1985 to 2021, are examined in some detail.

The earthquake clusters extracted by the two declustering algorithms are compared by different tools, so as to assess the similarities and differences in their classification and characterization (Varini et al., J Geophys Res, 2020). The connections between events forming a cluster, as defined by the considered declustering method, allow us representing its hierarchical structure by means of a tree graph. The topological structure of the clusters is then investigated by means of centrality measures in the frame of Network analysis.