Comparison of two clustering algorithms for the characterization of earthquake clusters

Earthquake clusters are prominent in the spatio-temporal distribution of any seismic catalog; they commonly show different patterns and complexities that are deemed to be strictly inherent to the physical properties of the seismic region. Identification and characterization of earthquake clusters are challenging and important tasks in order to disclose the underlying physical mechanisms of a seismic region. A spatio-temporal cluster is defined as a rate increase with respect to the typical background level of the area. Two categories of clusters are commonly defined: (1) mainshock-aftershock sequences, in which the largest magnitude event occurs first, possibly preceded by a few foreshocks, and (2) swarms, in which events occur closely clustered in time and space without a single outstanding shock. Static or dynamic stress changes are usually associated to mainshock-aftershock sequences, whereas transient aseismic slow slip has been recently hypothesized to drive a swarm-like activity (Zaliapin and Ben-Zion, 2016). Several declustering (clustering) algorithms are available in the seismological literature (see van Stiphout et al., 2012 for a review), the most popular ones being the window methods by Gardner and Knopoff (1974) and by Reasenberg (1985). In this study we compare two recent declustering algorithms that allow to recognize clusters in the time-space-magnitude domain: the stochastic declustering method (Zhuang et al., 2004) and the nearest-neighbor method (Zaliapin and Ben-Zion, 2016 and references therein).