Tom Heaton Caltech Geophysics and Civil Engineering

SCSN Products

Recent Earthquakes

Did You Feel It?

Shake Map

STEP: Short Term Earthquake Prediction Based on: USGS Hazards Map Rate of Earthquakes

Magistrale et al, 2002 Hauksson, 2000 Velocity Models for Southern California

4096 cores 6 Tbytes memory 20 Tflops Large-scale simulation

CISN Earthquake Early Warning Test Project

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

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

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

 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

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

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

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

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

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

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!

1000 station LA Network (Phase 2) Number of stations: 1000 Average spacing: 1.5 km Unaliased frequency: 1 Hz

Low-Cost Station Relies on host computer for communication and calculations In a USB package