highlights from retrospective testing of clustered ...€¦ · focal mechanisms for m≥4.5 (scedc,...

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