title: Clustering analysis of seismicity and aftershock identification.
abstract: We introduce a statistical methodology for clustering analysis of seismicity in the time-space-energy domain and use it to establish the existence of two statistically distinct populations of earthquakes: clustered and nonclustered. This result can be used, in particular, for nonparametric aftershock identification. The proposed approach expands the analysis of Baiesi and Paczuski [Phys. Rev. E 69, 066106 (2004)10.1103/PhysRevE.69.066106] based on the space-time-magnitude nearest-neighbor distance eta between earthquakes. We show that for a homogeneous Poisson marked point field with exponential marks, the distance eta has the Weibull distribution, which bridges our results with classical correlation analysis for point fields. The joint 2D distribution of spatial and temporal components of eta is used to identify the clustered part of a point field. The proposed technique is applied to several seismicity models and to the observed seismicity of southern California.
authors.affiliation: Department of Mathematics and Statistics, University of Nevada, Reno, Nevada 89557-0084, USA. zal@unr.edu
authors.forename: Ilya
authors.initials: I
authors.lastname: Zaliapin
authors.forename: Andrei
authors.initials: A
authors.lastname: Gabrielov
authors.forename: Vladimir
authors.initials: V
authors.lastname: Keilis-Borok
authors.forename: Henry
authors.initials: H
authors.lastname: Wong