1 / 35

Automated Earthquake Response Systems

Automated Earthquake Response Systems. Tom Heaton, Caltech Hiroo Kanamori, Caltech Egill Hauksson, Caltech Georgia Cua, ETH, Switzerland Masumi Yamada, Kyoto Univ Maren Böse, Caltech. Earthquake Alerting … a different kind of prediction.

sutton
Download Presentation

Automated Earthquake Response Systems

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Automated Earthquake Response Systems Tom Heaton, Caltech Hiroo Kanamori, Caltech Egill Hauksson, Caltech Georgia Cua, ETH, Switzerland Masumi Yamada, Kyoto Univ Maren Böse, Caltech

  2. Earthquake Alerting … a different kind of prediction • What if earthquakes were really slow, like the weather? • We could recognize that an earthquake is beginning and then broadcast information on its development … on the news. • “an earthquake on the San Andreas started yesterday. Seismologists warn that it may continue to strengthen into a great earthquake and they predict that severe shaking will hit later today.”

  3. Shakeout Simulation (Aagaard and Graves) • ..\Documents\2008\ShakeOut_LosAngeles_lowres.mov

  4. If the earthquake is fast, can we be faster? • Everything must be automated • Data analysis that a seismologist uses must be automated • Communications must be automated • Actions must be automated • Common sense decision making must be automated

  5. How would the system work? • Seismographic Network computers provide estimates of the location, size, and reliability of events using data available at any instant … estimates are updated each second • Each user is continuously notified of updated information …. User’s computer estimates the distance of the event, and then calculates an arrival time, size, and uncertainty • An action is taken when the expected benefit of the action exceeds its cost • In the presence of uncertainty, false alarms must be expected and managed

  6. What we need is a special seismologist • Someone who has good knowledge of seismology • Someone who has good judgment • Someone who works very, very fast • Someone who doesn’t sleep • We need a Virtual Seismologist

  7. Ground motion envelope: our definition Full acceleration time history Efficient data transmission 3 components each of Acceleration, Velocity, Displacement, of 9 samples per second envelope definition– max.absolute value over 1-second window

  8. Estimating M from ratios of P-wave motions • P-wave frequency content scales with M (Allen and Kanamori, 2003, Nakamura, 1988) • Find the linear combination of log(acc) and log(disp) that minimizes the variance within magnitude-based groups while maximizing separation between groups (eigenvalue problem) • Estimating M from Zad

  9. CPP MLS WLT DLA SRN PLS LLS STG • Voronoi cells are nearest neighbor regions • If the first arrival is at SRN, the event must be within SRN’s Voronoi cell • Green circles are seismicity in week prior to mainshock

  10. What about Large Earthquakes with Long Ruptures? • Large events are infrequent, but they have potentially grave consequences • Large events potentially provide the largest warnings to heavily shaken regions • Point source characterizations are adequate for M<7, but long ruptures (e.g., 1906, 1857) require finite fault

  11. Strategy to Handle Long Ruptures • Determine the rupture dimension by using high-frequencies to recognize which stations are near source • Determine the approximate slip (and therefore instantaneous magnitude) by using low-frequencies and evolving knowledge of rupture dimension • We are using Chi-Chi earthquake data to develop and test algorithms

  12. 10 seconds after origin 20 seconds after origin Near-field Far-field Near-field Far-field

  13. 30 seconds after origin 40 seconds after origin Near-field Far-field Near-field Far-field

  14. Once rupture dimension is known • Obtain approximate slip from long-periods • Real-time GPS would be very helpful • Evolving moment magnitude useful for estimating probable rupture length • Magnitude critical for tsunami warning

  15. Real-time prediction of ultimate rupture Bӧse and Heaton, in prep. Remaining Rupture Length slip Is the rupture on the San Andreas fault?

  16. Distributed and Open Seismic Network • Just in the gedanken phase • Tens of thousands of inexpensive seismometers running on client computers. • Sensors in buildings, homes, buisinesses • Data managed by a central site and available to everyone. • It will change the world!

  17. Conclusions • Earthquake warning is an extension of current systems that provide rapid information in earthquakes (e.g. ShakeMap) • Significant warning times can be achieved for moderate to light shaking • Heavily damaged areas in moderate events (M 6) will not receive warning • Heavily damaged areas in great earthquakes (M 7.5) will receive • Strategies to determine rupture dimension and slip look very promising • User decision making should be based on cost/benefit analysis …need to develop a community that develops optimal responses • Managing expectations is critical … users must understand what EEW won’t do.

  18. Probabilistic Rupture Prediction → Probabilistic Ground Shaking Bӧse and Heaton, in prep.

  19. We are experimenting with different Linear Discriminant analyses to distinguish near-field from far-field records

  20. Percent of area receiving warning time T or greater(log N*=6.89-Mw) Pseudovelocity [cm/sec] Warning time T [sec] Heaton, 1985

  21. 3 sec after initial P detection at SRN Epi dist est=33 km M=5.5 Single station estimate: • Prior information: • Voronoi cells • Gutenberg-Richter M, R estimates using 3 sec observations at SRN No prior information 8 km M=4.4 • Prior information: • Voronoi cells • No Gutenberg-Richter 9 km M=4.8 Note: star marks actual M, RSRN

  22. Virtual Seismologist (VS) method for seismic early warning • Bayesian approach to seismic early warning designed for regions with distributed seismic hazard/risk • Modeled on “back of the envelope” methods of human seismologists for examining waveform data • Shape of envelopes, relative frequency content • Robust analysis • Capacity to assimilate different types of information • Previously observed seismicity • State of health of seismic network • Known fault locations • Gutenberg-Richter recurrence relationship

  23. Data set for learning the envelope characteristics Most data are from TriNet, but many larger records are from COSMOS • 70 events, 2 < M < 7.3, R < 200 km • Non-linear model estimation (inversion) to characterize waveform envelopes for these events • ~30,000 time histories

  24. Average Rock and Soil envelopes as functions of M, R rms horizontal acceleration

  25. Distinguishing between P- and S-waves

  26. Sum of 9 point source envelopes • Vertical acceleration

  27. horizontal acceleration ampl rel. to ave. rock site Vertical P-wave acceleration ampl rel. to ave. rock site horizontal velocity ampl rel. to ave. rock site vertical P-wave velocity ampl rel. to ave. rock site

  28. Strategy for acceleration envelopes • High-frequency energy is proportional to rupture are (Brune scaling) • Sum envelopes from 10-km patches

More Related