Declustering algorithms, background seismicity modeling and earthquake clusters analysis

Declustering of seismic catalogs is required in wide range of earthquake forecasting and seismic hazard studies. However, different declustering methods may discriminate differently between background and clustered events in a given catalog. Hence the need to investigate to what extent the results of a specific study on background or clustered components may depend on the different declustered versions of a catalog. For this purpose we compare the declustered catalogs obtained from 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 Italian regions are considered. We first investigate the statistical properties of the obtained background time series (Benali et al., Stoch Environ Res Risk Assess, 2020), statistically checking if they meet the stationary Poissonian assumption. In case the Poissonian hypothesis is rejected, we resort to a model capable of capturing the possible heterogeneity in the background time series. Specifically, we consider the Markov Modulated Poisson Process (MMPP model), which allows the Poisson seismicity rate to change over time according to a finite (unknown) number of states of the system. We then compare the earthquake clusters extracted by the two declustering algorithms, 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.