HR: 14:10h
AN: S13E-03    [Abstracts]
TI: The Effect of Clustering Algorithms on Aftershock Productivity and Foreshock Rates
AU: * Christophersen, A
EM: Annemarie@sed.ethz.ch
AF: ETH Zurich, Swiss Seismological Service, Schafmattstrasse 30, Zurich, 8093, Switzerland
AU: Wiemer, S
EM: Stefan.Wiemer@sed.ethz.ch
AF: ETH Zurich, Swiss Seismological Service, Schafmattstrasse 30, Zurich, 8093, Switzerland
AU: Smith, E G
EM: Euan.Smith@vuw.ac.nz
AF: Victoria University of Wellington, Institute of Geophysics PO Box 600, Wellington, 6000, New Zealand
AB: The properties of earthquake clusters are important for the modeling of short-term hazard. In particular the forecasting of larger events is of societal importance. We apply common declustering algorithms, including Reasenberg, Gardner-Knophoff, and the model independent method by Marsan to the Southern California earthquake data to define earthquake clusters. We model the aftershock productivity as a function of mainshock magnitude M for the different clustering algorithms by N_ave = 10^α(M-M₁), where α is the growth parameter and M₁ corresponds to the magnitude that on average has one aftershock above the completeness magnitude. The number of aftershocks, hereafter called abundance, depends on the area and time in which to count aftershocks as well as the completeness magnitude. Spatial and temporal extent of aftershock sequences can vary significantly with clustering algorithm. By combining the abundance model with the Gutenberg-Richter equation for the distribution of earthquake magnitude, we can predict foreshock rates and compare them to observations. Depending on the clustering algorithm, foreshock rates can vary up to a factor of two. For some clustering algorithms, the foreshock rate is magnitude dependent, while for other algorithms it is not. However, we find that the foreshock rates predicted from aftershock abundance agree well with the observation for any consistent way of defining fore-and aftershocks. This confirms the common assumption that foreshocks trigger mainshocks in the same manner that mainshocks trigger aftershocks. Our results show that properties of earthquake sequences vary with clustering algorithm. Thus interpretations of fore-or aftershock characteristics need to give careful consideration to the clustering algorithm and data selection.
DE: 7223 Earthquake interaction, forecasting, and prediction (1217, 1242)
SC: Seismology [S]
MN: 2007 Fall Meeting