Clustering Analysis of Seismicity and Aftershock Identification

Ilya Zaliapin, Andrei Gabrielov, Vladimir I. Keilis-Borok, & H. Wong

Published 2008, SCEC Contribution #1137

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 non-clustered. This result can be used, in particular, for non-parametric aftershock identification. The proposed approach expands the analysis of Baiesi and Paczuski [PRE, 69, 066106 (2004)] 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.

Zaliapin, I., Gabrielov, A., Keilis-Borok, V. I., & Wong, H. (2008). Clustering Analysis of Seismicity and Aftershock Identification. Physical Review Letters,. doi: 10.1103/PhysRevLett.101.018501.