Creating the Virtual Seismologist Tom Heaton, Caltech Georgia Cua, Univ. of Puerto Rico Masumi...
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![Page 1: Creating the Virtual Seismologist Tom Heaton, Caltech Georgia Cua, Univ. of Puerto Rico Masumi Yamada, Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d3a5503460f94a14c25/html5/thumbnails/1.jpg)
Creating the Virtual Seismologist
Tom Heaton, CaltechGeorgia Cua, Univ. of Puerto Rico
http://etd.caltech.edu/etd/Masumi Yamada, Caltech
![Page 2: Creating the Virtual Seismologist Tom Heaton, Caltech Georgia Cua, Univ. of Puerto Rico Masumi Yamada, Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d3a5503460f94a14c25/html5/thumbnails/2.jpg)
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.”
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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
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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
![Page 5: Creating the Virtual Seismologist Tom Heaton, Caltech Georgia Cua, Univ. of Puerto Rico Masumi Yamada, Caltech.](https://reader030.fdocuments.in/reader030/viewer/2022032800/56649d3a5503460f94a14c25/html5/thumbnails/5.jpg)
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
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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
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Full acceleration time history
envelope definition– max.absolute value over 1-second window
Ground motion envelope: our definition
Efficient data transmission3 components each ofAcceleration, Velocity, Displacement, of9 samples per second
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70 events, 2 < M < 7.3, R < 200 kmNon-linear model estimation (inversion) to characterize waveform envelopes for these events~30,000 time histories
Data set for learningthe envelope characteristics
Most data are fromTriNet, but many larger records are from COSMOS
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Average Rock and Soil envelopes as functions of M, R rms horizontal acceleration
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horizontal 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
Vertical P-wave acceleration ampl rel. to ave. rock site
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Distinguishing between P- and S-waves
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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
Estimating M from ratios of P-wave motions
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SRN
STGLLS
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
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3 sec after initial P detection at SRN
M, R estimates using 3 sec observations at SRN
Epi dist est=33 km
M=
5.5
Note: star marks actual M, RSRN
Prior information:-Voronoi cells-Gutenberg-Richter
Prior information:-Voronoi cells-No Gutenberg-Richter
8 kmM=4.4
9 kmM=4.8
Single station estimate:
No prior information
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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
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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
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• We are experimenting with different Linear Discriminant analyses to distinguish near-field from far-field records
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10 seconds after origin 20 seconds after origin
Near-fieldFar-field
Near-fieldFar-field
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Near-fieldFar-field
Near-fieldFar-field
30 seconds after origin 40 seconds after origin
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Strategy for acceleration envelopes
• High-frequency energy is proportional to rupture are (Brune scaling)
• Sum envelopes from 10-km patches
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• Sum of 9 point source envelopes
• Vertical acceleration
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• 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
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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