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

8.  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 Data set for learning the envelope characteristics Most data are from TriNet, but many larger records are from COSMOS

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

10. Distinguishing between P- and S-waves

11.  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 Z ad Estimating M from ratios of P-wave motions

12. SRN STG LLS DLA PLS MLS CPP WLT  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

13. 3 sec after initial P detection at SRN M, R estimates using 3 sec observations at SRN Epi dist est=33 km M=5.5 Note: star marks actual M, R SRN Prior information: -Voronoi cells -Gutenberg-Richter Prior information: -Voronoi cells -No Gutenberg-Richter 8 km M=4.4 9 km M=4.8 Single station estimate: No prior information

14. 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

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

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 origin20 seconds after origin Near-field Far-field Near-field Far-field

19. Near-field Far-field Near-field Far-field 30 seconds after origin40 seconds after origin

20. 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

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

22. 22 Probabilistic Rupture Prediction → Probabilistic Ground Shaking B ӧ se and Heaton, in prep.

23. 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!

24. Conclusions Bayesian statistical framework allows integration of many types of information to produce most probable solution and error estimates 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 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 Managing expectations is critical … users must understand what EEW won’t do.

31. Sum of 9 point source envelopes Vertical acceleration

32. horizontal acceleration ampl rel. to ave. rock site horizontal velocity ampl rel. to ave. rock sitevertical P-wave velocity ampl rel. to ave. rock site Vertical P-wave acceleration ampl rel. to ave. rock site

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