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.
22
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