Journal of Seismology

, Volume 21, Issue 4, pp 965–985 | Cite as

The smart cluster method

Adaptive earthquake cluster identification and analysis in strong seismic regions
  • Andreas M. Schaefer
  • James E. Daniell
  • Friedemann Wenzel


Earthquake clustering is an essential part of almost any statistical analysis of spatial and temporal properties of seismic activity. The nature of earthquake clusters and subsequent declustering of earthquake catalogues plays a crucial role in determining the magnitude-dependent earthquake return period and its respective spatial variation for probabilistic seismic hazard assessment. This study introduces the Smart Cluster Method (SCM), a new methodology to identify earthquake clusters, which uses an adaptive point process for spatio-temporal cluster identification. It utilises the magnitude-dependent spatio-temporal earthquake density to adjust the search properties, subsequently analyses the identified clusters to determine directional variation and adjusts its search space with respect to directional properties. In the case of rapid subsequent ruptures like the 1992 Landers sequence or the 2010–2011 Darfield-Christchurch sequence, a reclassification procedure is applied to disassemble subsequent ruptures using near-field searches, nearest neighbour classification and temporal splitting. The method is capable of identifying and classifying earthquake clusters in space and time. It has been tested and validated using earthquake data from California and New Zealand. A total of more than 1500 clusters have been found in both regions since 1980 with M m i n = 2.0. Utilising the knowledge of cluster classification, the method has been adjusted to provide an earthquake declustering algorithm, which has been compared to existing methods. Its performance is comparable to established methodologies. The analysis of earthquake clustering statistics lead to various new and updated correlation functions, e.g. for ratios between mainshock and strongest aftershock and general aftershock activity metrics.


Earthquake clustering Seismic hazard Earthquake declustering Aftershock prediction 



We acknowledge the New Zealand GeoNet project and its sponsors EQC, GNS Science and LINZ, for providing data/images used in this study.

Furthermore, we thank the anonymous reviewers of this study for their critical and helpful comments improving this script substantially.


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Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • Andreas M. Schaefer
    • 1
  • James E. Daniell
    • 1
  • Friedemann Wenzel
    • 1
  1. 1.Geophysical InstituteKarlsruhe Institute of TechnologyKarlsruheGermany

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