Creating the Virtual Seismologist

1. Creating theVirtual Seismologist Tom Heaton, Caltech Georgia Cua, Univ. of Puerto Rico http://etd.caltech.edu/etd/ Masumi Yamada, 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. 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

4. 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

5. 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

6. 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

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. 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

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

10. 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

11. Distinguishing between P- and S-waves

12. 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

13. 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

14. 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

15. 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

16. 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

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

18. 10 seconds after origin 20 seconds after origin Near-field Far-field Near-field Far-field

19. 30 seconds after origin 40 seconds after origin Near-field Far-field Near-field Far-field

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

21. Sum of 9 point source envelopes • Vertical acceleration

22. 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

23. Conclusions • Bayesian statistical framework allows integration of many types of information to produce most probable solution and error estimates • Waveform envelopes can be used for rapid and robust real-time analysis • Strategies to determine rupture dimension and slip look very promising • User decision making should be based on cost/benefit analysis • Need to carry out Bayesian approach from source estimation through user response. In particular, the Gutenberg-Richter recurrence relationship should be included in either the source estimation or user response. • If a user wants ensure that proper actions are taken during the “Big One”, false alarms must be tolerated