Transcript Document
Creating the
Virtual Seismologist
Tom Heaton, Caltech
Georgia Cua, ETH, Switzerland
Masumi Yamada, Kyoto Univ
Maren Böse, Caltech
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
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Shape of envelopes, relative frequency content
Robust analysis
• Capacity to assimilate different types of information
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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
Distinguishing between P- and S-waves
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
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
3 sec after initial P detection at SRN
Single station estimate:
M, R estimates using 3 sec
observations at SRN
Prior information:
-Voronoi cells
-Gutenberg-Richter
No prior information
8 km
M=4.4
M=5.5
Epi dist est=33 km
Note: star marks actual M, RSRN
Prior information:
-Voronoi cells
-No Gutenberg-Richter
9 km
M=4.8
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
Pseudovelocity [cm/sec]
Percent of area receiving
warning time T or greater (log N*=6.89-Mw)
Warning time T [sec]
Heaton, 1985
Strategy to Handle Long Ruptures
• Determine the rupture dimension by using highfrequencies to recognize which stations are near
source
• Determine the approximate slip (and therefore
instantaneous magnitude) by using lowfrequencies 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
Near-field
Far-field
20 seconds after origin
Near-field
Far-field
30 seconds after origin
Near-field
Far-field
40 seconds after origin
Near-field
Far-field
• 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
Real-time prediction of ultimate
rupture
Remaining Rupture Length
Bӧse and Heaton, in prep.
slip
Is the rupture on the San Andreas fault?
Probabilistic Rupture Prediction → Probabilistic Ground
Shaking
Bӧse and Heaton, in prep.
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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!
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.
• Sum of 9 point
source envelopes
• Vertical
acceleration
horizontal acceleration ampl rel. to ave. rock site
horizontal velocity ampl rel. to ave. rock site
Vertical P-wave acceleration ampl rel. to ave. rock site
vertical P-wave velocity ampl rel. to ave. rock site
Strategy for
acceleration envelopes
• High-frequency energy is
proportional to rupture
are (Brune scaling)
• Sum envelopes from 10km patches