Quantifying the features of earthquake clusters in north-Central Iran, based on nearest-neighbor distances and network analysis

Abstract

The statistical features of earthquake clusters in North-Central Iran (Tehran Region) are investigated, with the aim of quantitatively characterizing the properties of earthquake triggering and allow exploring their possible relations with the tectonic setting of the study area.The nearest-neighbor approach is used for the identification of the earthquake clusters in the space-time-energy domain. This approach permits for a data-driven identification of clusters so that, within multi-event clusters, the features of secondary and higher orders dependent events can be explored. The study is based on a revised dataset that is extracted from the catalog compiled by the Iranian Seismological Center (IRSC) for the period of 1996–2022. In order to exclude the effect of non-tectonic events, which turn out quite numerous within the study region, explosions within quarry-rich areas are removed; the identification of non-tectonic events is performed by considering the normalized ratios of daytime to nighttime events in an iterative removal procedure. According to preliminary analysis of the resulting catalog, an area is selected, within which a satisfactory completeness level is assessed for events with magnitude >2.0. Robust values of the scaling parameters, namely the b-value and the fractal dimension of epicenters, are also computed and are used to calculate the nearest-neighbor distances and to identify the earthquake clusters.The nearest-neighbor method also permits to investigate the internal structure of earthquake sequences, and to differentiate the spatial properties of seismicity according to the different topological features of the clusters structure. The obtained results allow us identifying two macro-areas, approximately separated by the 52°E meridian, which are characterized by different clustering features, namely: high complexity indexes, indicating simple (burst-like) structure of clusters, to the East; low complexity index, corresponding to complex multi-level (swarm-like) structure of clusters, to the West. The complexity measures, borrowed from network theory (i.e. the Closeness and Outdegree Centralization indexes), consistently capture the complexity of the identified clusters, and confirm that the cluster structures have distinct preferred geographic locations. The territorial heterogeneity of the examined clustering properties can be related with the spatial variability of tectonic, structural and geophysical features of the Alborz region, in good agreement with findings from the Alps-Dinarides junction (Northeastern Italy), a region also characterized by a contractional structural setting, mainly including reverse and strike-slip faulting systems, and by moderate to high seismic activity.