Earthquake Early Warning Research and Development in California, USA
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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
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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
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• CISN real-time testing of 3 algorithmsτc-Pd On-site algorithm, VS, & ElarmS
• State-wide implementation382 stations with 585 broadband & strong motion instruments
• Many small to moderate earthquakes 2007 Mw5.4 Alum Rock & 2008 Mw5.4 Chino Hills2010 Mw7.2 Baja California
• CISN EEW Testing Center established at University of Southern California (USC)/SCEC
τc-Pd
On-site Algorithm
Single sensor
Virtual Seismologist
(VS)ElarmS
Sensor network Sensor network
Caltech ETH Zurich/Caltech UC Berkeley
CISN EEW Algorithm Testing (2007-2009)
Progress:
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CISN ShakeAlert (2009-2012)Project Goals
Year 1 (2009/10):Implementation
Year 2 (2010/11):Testing/Optimization
Year 3 (2011/12):Evaluation
System specificationsCode design specificationsCode developmentDefine formats and protocols
Implement end-to-end processingTesting with archived dataTesting with real-time dataImprove performanceTesting at the SCEC Testing CenterTesting with selected users
Prototype system in operationAdd features to Decision ModuleResearch adding GPS RT positionsResearch on finite sourcesPlans for future systems
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Results
τc-Pd
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 sensor Sensor network Sensor network
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Results
τc-Pd
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 sensor Sensor network Sensor 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
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Results
τc-Pd
On-site Algorithm
Virtual Seismologist
(VS)ElarmS
Examples:Mw5.4 Alum Rock: 5 sec before peak shaking in San Francisco.Mw5.4 Chino Hills: 6 sec warning at Los Angeles City Hall. Mw7.2 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 sensor Sensor network Sensor network
± 0.5 ± 0.2 ± 0.4* *includes M>7 data from JapanMMI: ±0.7
false alerts: (M>6.5) 1* 0
0* three month period
Mag.: Mw:Reliability:
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(2009-2012)
- most probable… Mw
… location… origin time… ground motion and uncertainties- probability of false trigger, i.e. no earthquake- CANCEL message if needed
Bayesian approachup-dated with time
Task 1: increase reliability
Decision Module(Bayesian)
CISN ShakeAlertτc-Pd
On-site Algorithm
Virtual Seismologist
(VS)ElarmS
Single sensor Sensor network Sensor network
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USER Module- Single site warning- Map view
SCEC/ EEW Testing Center
Decision Module(Bayesian)
Test users
CISN ShakeAlertτc-Pd
On-site Algorithm
Virtual Seismologist
(VS)ElarmS
(2009-2012)
Task 1: • increase reliability
Task 2: demonstrate
• predicted and observed ground motions• available warning time• probability of false alarm•…
feed
-bac
k
Single sensor Sensor network Sensor network
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CISN EEW Testing Center
CISN ShakeAlertτc-Pd
On-site Algorithm
Virtual Seismologist
(VS)ElarmS
Decision Module(Integration Module) feed-back
by test users
User Display
M. Boese
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CISN ShakeAlert
• platform independent (Java)
• ability to add multiple map layers & navigational features (OpenMap application programming interface)
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User Display
M. Boese
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CISN ShakeAlert
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• remaining time until S-wave arrival
User Display
M. Boese
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CISN ShakeAlert
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• remaining time until S-wave arrival• expected intensity at user site
User Display
M. Boese
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CISN ShakeAlert
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• remaining time until S-wave arrival• expected intensity at user site• (moment) magnitude
User Display
M. Boese
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CISN ShakeAlert
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• locations of epicenter & user
User Display
user
epicenter
M. Boese
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CISN ShakeAlert
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• locations of epicenter & user• locations of P- /S-wavefronts
User Display
P-waveS-wave
M. Boese
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CISN ShakeAlert
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• locations of epicenter & user• locations of P- /S-wavefronts• intensity map (ShakeMaps color-code)
User Display
M. Boese
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CISN ShakeAlert
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• siren• voice announcement:
• count-down• “weak shaking”, “strong shaking”…
User Display
future: different announcements depending on distance
M. Boese
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CISN ShakeAlert
See also Doug Given’s Webpage: http://pasadena.wr.usgs.gov/office/given/eew/
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
User Display - Demos
M. Boese
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CISN ShakeAlertProblem:
Point source approximationExpected intensity in LA:
point source: IV light shaking
M. Boese
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CISN ShakeAlertProblem:
Point source approximationExpected intensity in LA:
point source: IV light shakingfinite fault: VIII severe shaking
M. Boese
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Finite Fault Detector
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Near/far-source Classification
e.g, 7.233*log10(Za) + 6.813*log10(Hv)-15.903 0 . (Yamada et al., 2007)Za: vertical acceleration cm/s2
Hv: horzontal velocity cm/s
near-source
M. Boese
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Finite Fault Detector
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1. Estimated Magnitude: 6.6 2. Estimated Magnitude: 6.9
3. Estimated Magnitude: 7.1 4. Estimated Magnitude: 7.5
Real-time near/far-source classification
M. Boese
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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)
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Conclusions
• Finally have put the elements together to produce real-time alerts
• much work remains to produce a reliable system for general use