1. Earthquake Early Warning Research and Development in California, USA Hauksson E., Boese M., Heaton T., Seismological Laboratory, California Institute of Technology, Pasadena, CA, Given D., USGS, Pasadena, CA, Oppenheimer D., USGS, Menlo Park, CA, Allen R., Hellweg P., Seismological Laboratory, UC Berkeley, Berkeley, CA, Cua G., Fischer M., Caprio M. Swiss Seismological Service, ETH Zurich California

2. ANSS/CISN Early Warning R&D Project Collaboration: USGS Caltech UC-Berkeley ETH, Zurich USC/SCEC Develop EEW algorithms to detect and analyze earthquakes within seconds Identify needed improvements to the existing monitoring networks Implement an end-to-end prototype test system trigger time + 1 sec + 3 sec EEW requirements: -- Rapid earthquake detection -- Early Mag. estimation -- Ground shaking prediction -- Robust seismic networks -- Well trained uses EEW requirements: -- Rapid earthquake detection -- Early Mag. estimation -- Ground shaking prediction -- Robust seismic networks -- Well trained uses

3. 3 CISN real-time testing of 3 algorithms τ c -P d On-site algorithm, VS, & ElarmS State-wide implementation 382 stations with 585 broadband & strong motion instruments Many small to moderate earthquakes 2007 M w 5.4 Alum Rock & 2008 M w 5.4 Chino Hills 2010 Mw7.2 Baja California CISN EEW Testing Center established at University of Southern California (USC)/SCEC τ c -P d On-site Algorithm Single sensor Virtual Seismologist (VS) ElarmS Sensor network CaltechETH Zurich/CaltechUC Berkeley CISN EEW Algorithm Testing (2007-2009) Progress:

4. 4 CISN Shake Alert(2009-2012) Project Goals Year 1 (2009/10): Implementation Year 2 (2010/11): Testing/Optimization Year 3 (2011/12): Evaluation System specifications Code design specifications Code development Define formats and protocols Implement end-to-end processing Testing with archived data Testing with real-time data Improve performance Testing at the SCEC Testing Center Testing with selected users Prototype system in operation Add features to Decision Module Research adding GPS RT positions Research on finite sources Plans for future systems

5. 5 Results τ c -P d On-site Algorithm Virtual Seismologist (VS) ElarmS Speed: What causes delays? CISN EEW Algorithm Testing (2007-2009) R. Allen Median: ~ 5.2 sec California Data latency (datalogger/telemetry delays) Station density 0 10 20 sec Single sensorSensor network

6. 6 Results τ c -P d On-site Algorithm Virtual Seismologist (VS) ElarmS Speed: CISN EEW Algorithm Testing (2007-2009) R. Allen California Data latency (datalogger/telemetry delays) Station density 0 10 20 sec Single sensorSensor network How can these delays be reduced in the future ? 1. reduce data latency up-grade of ~220 CISN stations with new Q330s dataloggers (~1-2 sec delay) before Sept-2011 (ARRA stimulus funding) 2. increase processing speed current delays: ~5 sec 3. Increase station density 4. Decreas number of stations required for trigger

7. 7 Results τ c -P d On-site Algorithm Virtual Seismologist (VS) ElarmS Examples: M w 5.4 Alum Rock: 5 sec before peak shaking in San Francisco. M w 5.4 Chino Hills: 6 sec warning at Los Angeles City Hall. M w 7.2 Baja Calif.70?? sec warning at Los Angeles City Hall. Speed: after O.T. > 5 sec~20 sec~30 sec CISN EEW Algorithm Testing (2007-2009) Single sensorSensor network ± 0.5 ± 0.2 ± 0.4* *includes M>7 data from Japan MMI: ±0.7 false alerts: (M>6.5) 1* 0 0 * three month period Mag.: M w : Reliability:

8. 8 (2009-2012) - most probable … M w … location … origin time … ground motion and uncertainties - probability of false trigger, i.e. no earthquake - CANCEL message if needed Bayesian approach up-dated with time Task 1: increasereliability Decision Module (Bayesian) CISN Shake Alert τ c -P d On-site Algorithm Virtual Seismologist (VS) ElarmS Single sensorSensor network

9. 9 USER Module - Single site warning - Map view SCEC/ EEW Testing Center Decision Module (Bayesian) Test users CISN Shake Alert τ c -P d On-site Algorithm Virtual Seismologist (VS) ElarmS (2009-2012) Task 1: increase reliability increase reliability Task 2: demonstrate predicted and observed ground motions available warning time probability of false alarm … feed-back Single sensorSensor network

10. 10 CISN EEW Testing Center CISN Shake Alert τ c -P d On-site Algorithm τ c -P d On-site Algorithm Virtual Seismologist (VS) ElarmS Decision Module (Integration Module) Decision Module (Integration Module) feed-back by test users User Display M. Boese

11. CISN Shake Alert platform independent (Java) ability to add multiple map layers & navigational features (OpenMap application programming interface) 11 User Display M. Boese

12. CISN Shake Alert 12 remaining time until S-wave arrival User Display M. Boese

13. CISN Shake Alert 13 remaining time until S-wave arrival expected intensity at user site User Display M. Boese

14. CISN Shake Alert 14 remaining time until S-wave arrival expected intensity at user site (moment) magnitude User Display M. Boese

15. CISN Shake Alert 15 locations of epicenter & user User Display user epicenter M. Boese

16. CISN Shake Alert 16 locations of epicenter & user locations of P- /S-wavefronts User Display P-wave S-wave M. Boese

17. CISN Shake Alert 17 locations of epicenter & user locations of P- /S-wavefronts intensity map (ShakeMaps color-code) User Display M. Boese

18. CISN Shake Alert 18 siren voice announcement: count-down “weak shaking”, “strong shaking”… User Display future: different announcements depending on distance M. Boese

19. 19 CISN Shake Alert See also Doug Given’s Webpage: http://pasadena.wr.usgs.gov/office/given/eew/ http://pasadena.wr.usgs.gov/office/given/eew/ 2008 M5.4 Chino Hills 1994 M6.7 Northridge 1989 M6.9 Loma Prieta1989 M6.9 Loma Prieta (UCB) 1989 M6.9 Loma Prieta 1989 M6.9 Loma Prieta (San Jose) M7.8 ShakeOut Scenario User Display - Demos M. Boese

20. 20 CISN Shake Alert Problem: Point source approximation Expected intensity in LA: point source:IVlight shaking M. Boese

21. 21 CISN Shake Alert Problem: Point source approximation Expected intensity in LA: point source:IVlight shaking finite fault:VIIIsevere shaking M. Boese

22. Finite Fault Detector 22 Near/far-source Classification e.g, 7.233*log 10 (Za) + 6.813*log 10 (Hv)-15.903  0. (Yamada et al., 2007) Za: vertical acceleration cm/s 2 Hv: horzontal velocity cm/s near- source M. Boese

23. Finite Fault Detector 23 1. Estimated Magnitude: 6.62. Estimated Magnitude: 6.9 3. Estimated Magnitude: 7.1 4. Estimated Magnitude: 7.5 Real-time near/far-source classification M. Boese

24. Basic Research Projects Development of algorithms to analyze long ruptures (Heaton, Böse, and Karakus; Allen and Brown) Development of User Decision module based on cost/benefit (Beck and Wu) Development of slip detectors based on real- time GPS (Hudnut and Herring)

25. Conclusions Finally have put the elements together to produce real-time alerts much work remains to produce a reliable system for general use