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Highlights from Retrospective Testing of Highlights from Retrospective Testing of Clustered Seismicity ModelsClustered Seismicity Models
J. Woessner
A.M. Lombardi, W. Marzocchi
and
Participant in WP5 of EU – FP6 Project SAFER
MotivationMotivation
Learn from retrospective tests of various aftershock
sequences
Compare and test forecast capabilities of physically
based and purely statistical models
Explore the power of the CSEP-Tests on short-time and
spatial scales
Provide a MODEL RANKING
OutlineOutline
Testing regions and models Testing rules Overview on forecast models
Results of data consistency tests: L-Test: Spatial consistency N-Test: Consitency in total number
Summary and Outlook
Testing RegionsTesting Regions1992 Mw 7.3 Landers 2008 Mw 6.3 Selfoss
1997 Mw Coliforito
• common datasets
• reproducible
• transparent
Retrospective Test SetupsRetrospective Test Setups
Landers Colfiorito Selfoss
Magnitude range 3 ≤ ML ≤ 8 2.5 ≤ ML ≤ 7 2.5 ≤ ML ≤ 7
Forecast Period 90 days / 24h 43 days / 24h 60 days / 24h
Number of Models 12 8 7
Gridding 0.05º 0.02º 0.005º
Earthquake Catalog SCEDC relocated (Hauksson)
CSI catalog (Castello et al.,2005)
NISN catalog; automatic solution
Focal mechanisms For M≥4.5 (SCEDC, Hauksson)
From Catalli et al., (2007)
Not available
Background Stress Field
N7ºE N160ºE N45ºE
Slip Model Wald & Heaton (1994)
Hernandez (2004)Nostro et al. (2005)
Decriem et al., (2008)
Statistical Forecast ModelsStatistical Forecast Models
M-STEP: Woessner et al.,2009, Gerstenberger et al.,2005
Modified Short-Term Earthquake Probability model:
Cascading model, combination of Omori-Utsu and
Gutenberg-Richter model
ETAS (1-5): Lombardi et al., 2003,2006; Hainzl et al., 2008
Epidemic Type Aftershock Sequence models
different smoothing kernels (power-law, Gaussian)
spatially homogeneous / heterogeneous background
variable productivity parameter
different forecasting procedures
ETAS 6: Double-branching model (Lombardi, 2006)
background varies in space and time
Coulomb + Rate & State Forecast ModelsCoulomb + Rate & State Forecast Models
CRS-1: Catalli et al., 2007
multiple stress steps, resolved on specified receiver
planes
CRS-2: Hainzl et al., 2009
Including stress heterogeneity
Resolve ΔCFS on Optimally Oriented Planes (OOP)
CRS-3: Hainzl et al.
Including stress heterogeneity, adding poro-elastic
effects in ΔCFS calculation, OOP
CRS--4: Enescu B.
based on the approach of Toda et al. (1998), OOP
Model OverviewModel Overview
Testing ClassTesting Class
TM
Learning period
Forecast evaluation timeT
Ei with i =1, 2, ..., N
Forecast period 1
TM
+N
TM
: Time of main shockT
L: Learning period
TEi
: Forecast evaluation time
TE1
Forecast period
Testing ClassTesting Class
TM
Learning period
Forecast evaluation timeT
Ei with i =1, 2, ..., N
Forecast period 2
TM
+N
TE2
Forecast period
TM
: Time of main shockT
L: Learning period
TEi
: Forecast evaluation time
Forecast Models: Landers exampleForecast Models: Landers example
Model 1 Model 3 Model 4 Model 8
STEP ETAS 2 Coulomb Toda et al. 1998 modified k-variable stress hetero. OOP
Snapshots of Forecast day 1: TE1
Number of Events vs. TimeNumber of Events vs. Time
Days after TM
1992 Mw 7.3 Landers
Number of Events vs. TimeNumber of Events vs. Time
Days after TM
1992 Mw 7.3 Landers
N-Test: Time seriesN-Test: Time series
ETAS-3M-STEP
Days after TL
Days after TL
Cumulative N-TestDaily N-Test
Result:- Daily N-Test evaluations have large variability- Cumulative N-Test rejects the models in the long run
1992 Mw 7.3 Landers
Time series Model CRS-3Time series Model CRS-3L-TestN-Test
Days after TL
Days after TL
Result:- Daily N-Test evaluations have large variability- Cumulative N-Test rejects the models in the long run- Spatial forecast capability best in early period
Cumulative Daily
ETAS-2 PerformanceETAS-2 Performance
Selfoss
Colfiorito
N-Test
L-Test
CRS-3 PerformanceCRS-3 Performance
SelfossColfiorito
N-Test
L-Test
Rejection ratiosRejection ratios
SummarySummary
Not a unique summary from the application to
the three sequences
Models perform best on Landers sequence
ETAS-2 model (Hainzl) leads to smallest
rejection ratios
Preliminary ranking retrspective: ETAS-2, CRS-1, M-STEP
Spatial kernels need improvements
Statistical tests need improments
OutlookOutlook
Perform tests on more sequences Funding? Establish an Aftershock Sequence Laboratory at the
CSEP EUTesting Center in Zurich
Implement new S-Test / M-Test from CSEP
Overcome problems in statistical test procedures Provide forecast as distribution per time-space-
magnitude bin
THANKS THANKS
GFZ: S. Hainzl, B. Enescu (now Tsukuba), F. Roth, R.
Wang
INGV M. Cocco, W. Marzocchi, F. Catalli, A. M. Lombardi
ETHZ: S. Wiemer, M. Werner, A. Christophersen
GNS Science: M. C. Gerstenberger