Topological Comparison Between the Stochastic and the Nearest‐Neighbor Earthquake Declustering Methods Through Network Analysis

Abstract

Earthquake clustering is a significant 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 clustering properties and to analyze background seismicity. We consider two recent data‐driven declustering techniques, one 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 clustered 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 recognized and quantified by the outdegree centrality and closeness centrality measures from network analysis.

Aftershock Rate Changes at Different Ocean Tide Heights

“…Direct aftershocks found using the nearest neighbor approach do not contain the secondary aftershock sequences, and thus are correctly modeled by the Omori law. Another alternative could be a stochastic declustering of the earthquake catalogue with much deeper complexity and non-uniqueness of the clusters (Varini et al, 2020). Another disadvantage of the stochastic declustering methods is their basic hypothesis that the number of aftershocks is a function of the magnitude of the corresponding main shock.…”

Unified Scaling Law for Earthquakes: Space-Time Dependent Assessment in Friuli-Venezia Giulia Region

“…Several efforts have been already devoted to explore the different components of seismicity in the study region, namely the clustering properties Gentili, 2018, 2020;Varini et al, 2020) as well as the temporal variability of background seismicity component (Benali et al, 2020). Here we investigate changes in seismicity as a whole, without differentiating between clustered and background components.…”