highlights from retrospective testing of clustered ...€¦ · focal mechanisms for m≥4.5 (scedc,...
TRANSCRIPT
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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
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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
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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
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Testing RegionsTesting Regions1992 Mw 7.3 Landers 2008 Mw 6.3 Selfoss
1997 Mw Coliforito
• common datasets
• reproducible
• transparent
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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)
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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
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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
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Model OverviewModel Overview
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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
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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
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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
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Number of Events vs. TimeNumber of Events vs. Time
Days after TM
1992 Mw 7.3 Landers
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Number of Events vs. TimeNumber of Events vs. Time
Days after TM
1992 Mw 7.3 Landers
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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
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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
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ETAS-2 PerformanceETAS-2 Performance
Selfoss
Colfiorito
N-Test
L-Test
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CRS-3 PerformanceCRS-3 Performance
SelfossColfiorito
N-Test
L-Test
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Rejection ratiosRejection ratios
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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
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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
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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