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Clustering Analysis of Seismicity and Aftershock Identification

2008; American Physical Society; Volume: 101; Issue: 1 Linguagem: Inglês

10.1103/physrevlett.101.018501

1092-0145

Ilya Zaliapin, Andrei Gabrielov, V. I. Keilis‐Borok, H. Vernon Wong,

Seismology and Earthquake Studies

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 $\ensuremath{\eta}$ between earthquakes. We show that for a homogeneous Poisson marked point field with exponential marks, the distance $\ensuremath{\eta}$ 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 $\ensuremath{\eta}$ 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.