Abstract

Earthquake clustering is a relevant feature of seismic catalogs, both in time and space. Several methodologies for earthquake cluster identification have been proposed in the literature in order to characterise clustering properties and to analyse background seismicity. We consider two recent data-driven declustering techniques, one is based on nearest-neighbor distance and the other on a stochastic point process. These two methods use different underlying assumptions and lead to different classifications of earthquakes into background events and secondary events. We investigated the classification similarities by exploiting graph representations of earthquake clusters and tools from network analysis. We found that the two declustering algorithms produce similar partitions of the earthquake catalog into background events and earthquake clusters, but they may differ in the identified topological structure of the clusters. Especially the clusters obtained from the stochastic method have a deeper complexity than the clusters from the nearest-neighbor method. All of these similarities and differences can be robustly recognised and quantified by the outdegree centrality and closeness centrality measures from network analysis.