vertically integrated seismic analysis
DESCRIPTION
Vertically Integrated Seismic Analysis. Outline. Seismic event monitoring as probabilistic inference Vertically integrated probability models … Connect events to sensor data and everything in between Associate events and detections optimally Automatically take nondetections into account - PowerPoint PPT PresentationTRANSCRIPT
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Vertically Integrated Seismic Analysis
Stuart RussellComputer Science Division, UC Berkeley
Nimar Arora, Erik Sudderth, Nick Hay
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Outline
Seismic event monitoring as probabilistic inference Vertically integrated probability models …
Connect events to sensor data and everything in between Associate events and detections optimally Automatically take nondetections into account May improve low-amplitude detection and noise rejection
Inference using MCMC (poster) Empirical estimation of model components Preliminary experimental results
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Bayesian model-based learning
Generative approach P(world) describes prior over what is (source), also over
model parameters, structure P(signal | world) describes sensor model (channel) Given new signal, compute
P(world | signal) ~ P(signal | world) P(world)
Learning Adapt model parameters or structure to improve fit Operates continuously as data are acquired and analyzed
Substantial recent advances in modeling capabilities, general-purpose inference algorithms
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Generative model for IDC arrival data
Events occur in time and space with magnitude Natural spatial distribution a mixture of Fisher-Binghams Man-made spatial distribution uniform Time distribution Poisson with given spatial intensity Magnitude distribution Gutenberg-Richter Aftershock distribution (not yet implemented)
Travel time according to IASPEI91 model+corrections Detection depends on magnitude, distance, station* Detected azimuth, slowness w/ empirical residuals False detections with station-dependent distribution
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Seismic event
Travel times
Seismic event
Travel times
Station 1picks
Station 2picks
Generative structure
Detected atStation 1?
Detected atStation 2?
Station 1noise
Station 2 noise
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Inference
MCMC (Markov chain Monte Carlo) (see poster S31B-1713 for details)
Efficient sampling of hypothetical worlds (events, travel times, detections, noise, etc.)
Converges to true posterior given evidence Key point: computing posterior probabilities takes the algorithm off the table; to get better answers, either Improve the model, or Add more sensors
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Vertical integration: Detection
Basic idea: analyzing each signal separately throws away information. Multiple weak signals are mutually reinforcing via a higher-
level hypothesis Multiple missing signals indicate that other “detections” may
be coincidental noise Simple example: K sensors record either
Independent noise drawn from N[0,1] Common signal drawn from N[0,1-] + independent N[0,] noise
Separate detectors fail completely!
Joint detection succeeds w.p. 1 as 0 or K Travel time accuracy affects detection capability!
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STA/LTA Threshold
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Outline
Seismic event monitoring as probabilistic inference Vertically integrated probability models …
Connect events to sensor data and everything in between Associate events and detections optimally Automatically take nondetections into account May improve low-amplitude detection and noise rejection
Inference using MCMC (poster) Empirical estimation of model components Preliminary experimental results
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Seismic event
Travel times
Seismic event
Travel times
Station 1picks
Station 2picks
Generative structure
Detected atStation 1?
Detected atStation 2?
Station 1noise
Station 2 noise
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Seismic event
Travel times
Seismic event
Travel times
Station 1picks
Station 2picks
Generative structure
Detected atStation 1?
Detected atStation 2?
Station 1noise
Station 2 noise
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Seismic event
Travel times
Seismic event
Travel times
Station 1picks
Station 2picks
Generative structure
Detected atStation 1?
Detected atStation 2?
Station 1noise
Station 2 noise
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Overall Pick Error
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WRA Pick Error
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Overall IASPEI Error
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WRA - IASPEI Error
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Seismic event
Travel times
Seismic event
Travel times
Station 1picks
Station 2picks
Generative structure
Detected atStation 1?
Detected atStation 2?
Station 1noise
Station 2 noise
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Overall Azimuth Error
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WRA - Azimuth Error
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Seismic event
Travel times
Seismic event
Travel times
Station 1picks
Station 2picks
Generative structure
Detected atStation 1?
Detected atStation 2?
Station 1noise
Station 2 noise
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Analyzing Performance
Min-cost max-cardinality matching where edges exist between prediction and ground truth events within 50 seconds and 5 degrees.
Precision – percentage of predictions that match. Recall – percentage of ground truths that match. F1 – harmonic mean of precision and recall. Error – average distance between matching events.
(Cost of matching / size of matching)
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Evaluation vs LEB (human experts)
F1 Precision/
Recall
Error/S.D.
(km)
Average Log-likelihood
SEL3 (IDC Automated)
55.6 46.2 / 69.7 98 / 119 _
VISA (Best Start) 80.4 70.9 / 92.9 100 / 117 -1784
VISA (SEL3 Start) 55.2 44.3 / 73.4 104 / 124 -1791
VISA (Back projection Start)
50.6 49.1 / 52.0 126 / 139 -1818
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INFERENCE EXAMPLE
model2_71_223.avi
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Summary
Vertically integrated probability models Connect events, transmission, detection,
association Information flows in all directions, reinforcing or
rejecting local hypotheses to form a global solution Better travel time model => better signal detection Nondetections automatically play a role Local sensor models calibrated continuously with no
need for ground truth May give more reliable detection and localization of
lower-magnitude events
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Ongoing Work
More sophisticated MCMC design Add more phases and phase relabeling Extend model all the way down to waveforms Evaluation using data from high-density
networks (Japan Meteorological Agency, some regions within ISC data)