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Triggering Models vs. Smoothed Seismicity

Triggering Models vs. Smoothed Seismicity. Testing region: California Target events: M  ≥ 3.95 Testing period : 2008 -2010 Testing method: T-test. PG = probability gain = P / P 0 IG = information gain = log e ( PG ). STEP model. PG = 1.35/eqk. Reference forecast.

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Triggering Models vs. Smoothed Seismicity

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  1. Triggering Models vs. Smoothed Seismicity Testing region: California Target events: M ≥ 3.95 Testing period: 2008-2010 Testing method: T-test PG = probability gain = P/P0 IG = information gain = loge(PG) STEP model PG= 1.35/eqk Reference forecast PG= 10/eqk Information gain per earthquake

  2. Japan and NZ Testing Regions 1 day 3 month 6 month 5 year Total New Zealand 2 8 1 4 15

  3. Darfield Aftershock Forecasting(Gerstenberger & Rhoades) Testing region: New Zealand Target events: M ≥ 4 (ETAS, PPE-1d), M ≥ 5 (PPE-3m, PPE-5y) Testing period: 4 Sept 2010 - 8 Mar 2011 Testing method: N-test Forecast Nobs = 271 (M ≥ 4) 209 are Darfield aftershocks Nobs = 17 (M ≥ 5) Number of earthquakes

  4. Darfield Aftershock Forecasting(Gerstenberger & Rhoades) Testing region: New Zealand Target events: M ≥ 4 (PPE-1d), M ≥ 5 (PPE-3m, PPE-5y) Testing period: 4 Sept 2010 - 8 Mar 2011 Testing method: T-test ETAS model PG= 99/eqk Reference forecast PG= 544/eqk PG= 1480/eqk Information gain per earthquake

  5. CSEP Testing Results • Comparative evaluations have quickly identified errors in model implementation • effective method for model verification (debugging) • 5-yr RELM testing program has been effective in ranking long-term forecasting performance for M ≥ 5 target events in California • RELM paper by Zechar, Schorlemmer, et al. • Aftershock triggering models (e.g., STEP, ETAS) obtain probability gains of 10-1000 relative to seismicity averaging models (e.g. PPE, TripleS) • Substantially more information gain can be obtained by updating forecasts more frequently than at 1-day intervals • Adaptive triggering models out-perform those with time-independent parameters • Gerstenberger’s STEP model currently shows the best short-term performance in California; adaptive models in NZ and Japan are still being evaluated

  6. CSEP Plans • Monitor the performance of 91 CSEP/Japan and 15 CSEP/NZ forecasting models during the active phases of the Tohoku and Darfield sequences • Reduce the updating interval for short-term forecasts from 1 day to 1 hr or less • Improve procedures for adapting forecasts to changes in the seismic environment • Incorporate forecasting models based on physical hypotheses about earthquake generation • e.g., Coulomb stress function, rate/state friction, dynamic vs. static triggering, slow slip events, tidal triggering • Expand prospective testing to models based on non-seismic data • Evaluate hypotheses critical to forecasting large earthquakes • e.g., fault segmentation, maximum magnitude, characteristic earthquakes, strongly coupled seismic gaps • Expand global testing program • Include model classes for legacy methods; e.g., M8/MSc • Develop CSEP capabilities to support operational earthquake forecasting • Prospectively test candidate forecasting procedures • Unify forecasting across temporal and spatial scales (e.g. long-term & short-term) • Reduce testing latency by modeling catalog completeness and accuracy • Expand retrospective testing over the entire history of instrumental catalogs • Initiate model testing using recorded ground motions • Support other prospective testing activities, including earthquake early warning

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