%0 Journal Article
%@ 0031-9007
%A Zaliapin, Ilya
%A Gabrielov, Andrei
%A Keilis-Borok, Vladimir
%A Wong, Henry
%A Department of Mathematics and Statistics, University of Nevada, Reno, Nevada 89557-0084, USA,
%A Departments of Mathematics and Earth and Atmospheric Sciences, Purdue University, West Lafayette, Indiana 47907-1395, USA,
%A Institute of Geophysics and Planetary Physics, and Department of Earth and Space Sciences, University of California Los Angeles, 3845 Slichter Hall, Los Angeles, California 90095-1567, USA,
%D 2008
%F epos:195
%I The American Physical Society
%J Physical Review Letters
%N 1
%T Clustering Analysis of Seismicity and Aftershock Identification
%U https://episodesplatform.eu/eprints/195/
%V 101
%X 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)] based on the space-time-magnitude nearest-neighbor distance η between earthquakes. We show that for a homogeneous Poisson marked point field with exponential marks, the distance η 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 η 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.