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Application of the RI model to forecasting future large earthquakes in Japan

International symposium “Toward constructing earthquake forecast systems for Japan” 27 May 2009 at ERI, Univ. Tokyo. Application of the RI model to forecasting future large earthquakes in Japan. Kazu Z. Nanjo (ERI, Univ. of Tokyo). RI & PI. RI ( R elative I ntensity of Seismicity)

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Application of the RI model to forecasting future large earthquakes in Japan

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  1. International symposium “Toward constructing earthquake forecast systems for Japan” 27 May 2009 at ERI, Univ. Tokyo Application of the RI model to forecasting future large earthquakes in Japan Kazu Z. Nanjo (ERI, Univ. of Tokyo)

  2. RI & PI • RI (Relative Intensity of Seismicity) • Future large earthquakes  regions with high seismic intensity • More specifically, count past earthquakes for each node • PI (Pattern Informatics) • Future large earthquakes  regions with high rate change (activation and quiescence) of seism city • More specifically, the change of number of events based on past earthquakes for each node • Studies for CA, China, and Japan show • Both are similar for their forecast accuracy • RI and PI need to be optimized

  3. Forecast models using PI and RIforecasting 2000-2009 M≥5 events based on 1965-1999 M≥3 events PI RI Log10P Log10P • PI method: find seismic activation and quiescence • RI method: find seismic intensity Nanjo et al. (2006a,b) As of Aug. 2005

  4. Molchan test A test to measure of matching between forecast map based on EQs. in ≤1999 and EQs. in ≥ 2000 • PI method: find seismic activation and quiescence • RI method: find seismic intensity

  5. Application of RI to Japan • JMA catalog • CSEP testing region (Bin size: 0.1 deg) • Retrospective test: m≥5 events in the last 3 years • Optimization • Change t0 and minimum magnitude Mmin: • To see the effect of catalog completeness on forecasting • Nondeclustered and declustered catalogs: • To see aftershock effect on forecasting 2005/04/01 t0 (variable) 2008/03/31 t Forecast period 58 m≥5 targets Learning period Mmin: a variable

  6. Evolution of MC since 1970 (d≤30km)

  7. Nondeclustered Declustered RI maps LLP=-350 LLP=-370 RI RI (m>=3, t0=1985/01/01)

  8. Likelihood test ND D m>=2.5 m>=3.0 m>=4.0 • Aftershock locations are important information of forecasting future events • Catalog completeness and maximizing data need to be considered for optimization

  9. A proposed prospective forecast map

  10. Summary • Results • Aftershock location • Important information to forecast the location of future large earthquakes • The need of optimization for RI forecast • Catalog completeness • Maximize used data • (Non)declustering • Current status for submission • Under test for the testing since 2008 • Ready for submission to the 1 day forecast class if there is any proposed one-day model!

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