Earthquake clustering and centrality measures

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 characterize geophysical clustering properties and to analyze background seismicity. We consider two recent data-driven declustering techniques, one is based on nearest-neighbor distance and the other on a point process model. Since the different assumptions underlying each method may lead to different classifications of earthquakes into main events and secondary events, we investigate the classification similarities by exploiting graph representations of earthquake clusters and tools from Network analysis.