1. Application of the RI model to forecasting future large earthquakes in Japan Kazu Z. Nanjo (ERI, Univ. of Tokyo) International symposium “Toward constructing earthquake forecast systems for Japan” 27 May 2009 at ERI, Univ. 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 RI forecasting 2000-2009 M≥5 events based on 1965-1999 M≥3 events PI RI PI method: find seismic activation and quiescence RI method: find seismic intensity PI method: find seismic activation and quiescence RI method: find seismic intensity Nanjo et al. (2006a,b) Log 10 P 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 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 t 0 and minimum magnitude M min : To see the effect of catalog completeness on forecasting -Nondeclustered and declustered catalogs: To see aftershock effect on forecasting t t 0 (variable) 2005/04/01 2008/03/31 Forecast period 58 m≥5 targets Learning period M min : a variable

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

7. RI maps NondeclusteredDeclustered RI (m>=3, t 0 =1985/01/01) LL P =-350 LL P =-370

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