Annual Meeting - SCEC.org

Report | SCEC Research Accomplishments
Figure 80 and Figure 81 illustrate CSEP evaluations of reliability and skill for short-term forecasting models. An important
result is the high performance of the STEP model of Gerstenberger et al. (2005, 2007) in the California testing region, which
supports the USGS use of STEP as an operational forecasting model for California.
Interesting results have also been obtained for the seismic sequence in the Canterbury region of New Zealand, which began
with the MW 7.1 Darfield earthquake of 3 Sept 2010. The time-dependent ETAS model out performs the PPE and other timeindependent
models; probability gains above 1000 have been achieved by ETAS during the Darfield sequence.
New data are also coming in rapidly from the Japan testing region. The aftershock numbers predicted by ETAS models for the
Tohoku sequence are under-estimates of the actual number (Figure 82). These results indicate that the ETAS updating interval
of 1 day is too long to capture the aftershock activity, which decays rapidly after the mainshock. (This conclusion has also been
drawn from the California experiments.) Figure 6 also shows that the aftershock number is higher during the post-interval
relative to pre-event intervals, which may be related to artificially high magnitude cutoffs in the ETAS model of aftershock
productivity.
94 | Southern California Earthquake Center
Figure 80. Examples of CSEP results that assess the absolute
reliability of four models by testing the number of earthquakes
forecast (dots with 95% confidence intervals) against the number
observed (dashed lines) in the New Zealand testing region during
the interval 4 Sept 2010 – 8 Mar 2011. ETAS_1day is a timedependent
(Omori-Utsu) Epidemic Type Aftershock Sequence
model, updated daily, which passes the test (indicated by green
dot). PPE_3month, PPE_1day, and PPE_5year are timeindependent
(Poisson) Proximity to Past Earthquakes models with
updating intervals 1 day, 3 months, and 5 years, respectively, which
fail the test (indicated by red dots). The ETAS and PPE_1day tests
comprised 271 events with M ≥ 4, whereas the PPE_3month and
PPE_5year test comprised 17 events with M ≥ 5. (209 of the 271
events in the former set were aftershocks of the 3 Sept 2010
Darfield earthquake.) Results from the New Zealand Testing Center
are courtesy of D. Rhoades and M. Gerstenberger of GNS Science.
Figure 81. Examples of CSEP testing results that assess the
relative skill of forecasting models. Upper panel: Information gain
(IG) per earthquake of the ETAS_1day model relative to three PPE
models for target events recorded in the New Zealand testing
region during the interval 4 Sept 2010 – 8 Mar 2011. The
PPE_1day comparison comprised 271 events with M ≥ 4, whereas
the PPE_3month and PPE_5year comparisons comprised 17
events with M ≥ 5. (209 of the 271 events in the former set were
aftershocks of the 3 Sept 2010 Darfield earthquake.) In this
experiment, ETAS_1day achieved a probability gain (PG) of 99/eqk
relative to PPE_1day and 1480/eqk relative to PPE_5year, and the
null hypothesis of no probability gain can be rejected (indicated by
red dots with 95% confidence intervals). Results from the New
Zealand Testing Center are courtesy of D. Rhoades and M.
Gerstenberger of GNS Science. Lower panel: IG per earthquake of
Gerstenberger’s STEP_1day model relative to Zuang’s
ETAS_1day model and Zechar & Jordan’s TripleS_1day model for
301 target events with M ≥ 3.95 recorded in the California testing
region during the 3-year interval 2008-2010. All three of the
forecasting models were updated on a daily basis. In this
experiment, STEP_1day achieved a PG of 1.35/eqk relative to
ETAS_1day and 10/eqk relative to TripleS_1day, and the null
hypothesis of no probability gain can be rejected (indicated by red
dots with 95% confidence intervals). STEP and ETAS are timedependent
(Omori-Utsu) models; TripleS and PPE are timeindependent
(Poisson) models. All models were evaluated using a
paired T test, developed by D. Rhoades based on the student’s t
distribution. Information gain is the natural logarithm probability
gain.