Earthquake Early Warning Research and Development in California, USA

Transcription

1 Earthquake Early Warning Research and Development in California, USA Hauksson E., Boese M., Heaton T., Seismological Laboratory, California Ins>tute 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

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" EEW requirements: - - Rapid earthquake detec>on trigger >me - - Early Mag. es>ma>on - - Ground shaking predic>on - - Robust seismic networks - - Well trained uses + 1 sec + 3 sec

3 CISN EEW Algorithm Testing ( ) Single sensor τ c - P d On- site Algorithm Progress: Sensor network Virtual Seismologist (VS) CISN real- >me tes>ng of 3 algorithms τ c - P d On- site algorithm, VS, & ElarmS State- wide implementa>on 382 staions with 585 broadband & strong moion 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 Sensor network ElarmS Caltech ETH Zurich/Caltech UC Berkeley CISN EEW Tes>ng Center established at University of Southern California (USC)/SCEC 3

4 Project Goals Year 1 (2009/10): ImplementaIon Year 2 (2010/11): TesIng/OpImizaIon CISN Shake Alert ( ) System specifica>ons Code design specifica>ons Code development Define formats and protocols Implement end- to- end processing Tes>ng with archived data Tes>ng with real- >me data Improve performance TesIng at the SCEC TesIng Center TesIng with selected users Year 3 (2011/12): EvaluaIon Prototype system in opera>on Add features to Decision Module Research adding GPS RT posi>ons Research on finite sources Plans for future systems 4

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

6 CISN EEW Algorithm Testing ( ) Single sensor Sensor network Sensor network Speed: τ c - P d On- site Algorithm Results Data latency (datalogger/ telemetry delays) Virtual Seismologist (VS) ElarmS How 1. reduce can these data delays latency be reduced in the future? up- grade of ~220 CISN sta>ons with new Q330s dataloggers (~1-2 sec delay) before Sept (ARRA s>mulus funding) 2. increase processing speed current delays: ~5 sec 3. Increase staion density Station density sec 4. Decreas number of staions required for trigger R. Allen 6

7 CISN EEW Algorithm Testing ( ) Single sensor Sensor network Sensor network Speed: τ c - P d On- site Algorithm Virtual Seismologist (VS) Results ElarmS aeer O.T. > 5 sec ~20 sec ~30 sec 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. Reliability: Mag.: M w : ± 0.5 ± 0.2 ± 0.4* MMI: ±0.7 *includes M>7 data from Japan false alerts: (M>6.5) 1* 0 0 * three month period 7

8 CISN Shake Alert ( ) Single sensor Sensor network Sensor network τ c - P d On- site Algorithm Virtual Seismologist (VS) ElarmS Task 1: increase reliability Decision Module (Bayesian) - most probable M w locaion origin Ime ground moion and uncertainies - probability of false trigger, i.e. no earthquake - CANCEL message if needed Bayesian approach up- dated with Ime 8

9 CISN Shake Alert ( ) Single sensor Sensor network Sensor network τ c - P d On- site Algorithm Virtual Seismologist (VS) ElarmS Task 1: increase reliability Task 2: demonstrate Decision Module (Bayesian) feed- back SCEC/ EEW TesIng Center USER Module - Single site warning - Map view Test users predicted and observed ground moions available warning Ime probability of false alarm 9

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

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

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

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

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

15 CISN Shake Alert User Display epicenter user locations of epicenter & user 15

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

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

18 CISN Shake Alert User Display siren voice announcement: count-down weak shaking, strong shaking future: different announcements depending on distance 18

19 CISN Shake Alert User Display - Demos 2008 M5.4 Chino Hills 1994 M6.7 Northridge 1989 M6.9 Loma Prieta (UCB) 1989 M6.9 Loma Prieta (San Jose) M7.8 ShakeOut Scenario See also Doug Given s Webpage: 19

20 CISN Shake Alert Problem: Point source approximation Expected intensity in LA: point source: IV light shaking 20

21 CISN Shake Alert Problem: Point source approximation Expected intensity in LA: point source: IV light shaking finite fault: VIII severe shaking 21

22 Finite Fault Detector Near/far-source Classification e.g, 7.233*log 10 (Za) *log 10 (Hv) (Yamada et al., 2007) Za: vertical acceleration cm/s 2 Hv: horzontal velocity cm/s nearsource 22

23 Finite Fault Detector Real-time near/far-source classification 1. Estimated Magnitude: Estimated Magnitude: Estimated Magnitude: Estimated Magnitude:

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- >me GPS (Hudnut and Herring)

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