Alan L. Kafka and John
E. Ebel
Weston Observatory, Department of Geology and Geophysics, Boston College
Abstract of poster presented at 2008
Annual Meeting of the Seismological
Society of America.
Seismological Research Letters,
79(2), p. 359.
Click
here to download the poster.
If an earthquake
forecast is to be considered
successful, it
should perform better than a reasonable “least astonishing” null
hypothesis. We
view the Cellular Seismology (CS) method as a least astonishing
hypothesis
model for forecasting locations of future earthquakes, and we propose
that CS
is a useful standard of comparison for other, more complex, forecast
models.
Recent forecast models based on analyses of earthquakes in California
such a
that of Ebel et al. (2007) and the Pattern Informatics method of Rundle
et al.
(2002, 2007) provide opportunities for comparing algorithms that
incorporate
the time dependence of the seismicity with a simpler (time-independent)
method
(i.e., CS) that only assumes that future earthquakes will occur near
epicenters
of past earthquakes. The CS method is strictly spatial; it considers a
location
to be a potential source point of future earthquakes even if only one
past
earthquake occurred near that point. By contrast, the Ebel et al.
(2007)
5-year forecast model (E07) incorporates time-dependence by mapping the
spatial
distribution of rates of seismicity, and the Pattern Informatics model
(PI) not
only considers rates of seismicity, but also incorporates changes in
rates of
seismicity as a measure of the potential for future earthquakes to
occur at
some location. Our comparison of success rates of the E07 method and
the PI
method with CS for earthquakes in