Creating the Virtual Seismologist Tom Heaton, Caltech Masumi Yamada, Caltech Georgia Cua, Swiss Seismological Service

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.”

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

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

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

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

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

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

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

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

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

Voronoi cells are nearest neighbor regions 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

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

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

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

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

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

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

Sum of 9 point source envelopes Vertical acceleration

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

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

Distinguishing between P- and S-waves

3 sec after initial P detection at SRN 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 Epi dist est=33 km M=5.5 Note: star marks actual M, RSRN