Creating the Virtual Seismologist

1
Creating the Virtual 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 . Users 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 doesnt 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 lt M lt 7.3, R lt 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

• Voronoi cells are nearest neighbor regions
• If the first arrival is at SRN, the event must
  be within SRNs Voronoi cell
• Green circles are seismicity in week prior to
  mainshock

14
3 sec after initial P detection at SRN

• Prior information
• Voronoi cells
• Gutenberg-Richter

Single station estimate
M, R estimates using 3 sec observations at SRN
No prior information
8 km M4.4

• Prior information
• Voronoi cells
• No Gutenberg-Richter

9 km M4.8
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
  Mlt7, 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