Infrasonic Signal Detection Using The Hough Transform

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Infrasonic Signal Detection UsingTheHough
Transform

• D. J. Brown, B.L.N. Kennett, C. Tarlowski
• Research School of Earth Sciences,
• Australian National University,
• Canberra, 0200,
• Australia

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Overview

• Motivation
• The Hough Transform
• Three-Dimensional array processing
• INFER detector design
• Examples
• Summary

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Motivation

• Based on the premise that signal duration may be
  a significant discriminant
• a signal with an extended duration is at least
  indicative that something is happening
  somewhere
• Infrasonic detection algorithms with an
  instantaneous detection philosophy will likely
  never operate at sufficiently low thresholds to
  detect all significant signals
• e.g., a pure Fstat-detector cannot reasonably
  operate with the detection threshold set much
  below 6.
• traditionally a threshold is set and signal
  duration is defined by how long the signal stays
  above threshold.
• It remains to obtain a correct measure of signal
  duration

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Motivation

• Acoustic signal from Etna volcano recorded at
  I26DE
• a significant signal
• Distinct lack of
• correlation
• amplitude
• Average Fstat around 4.3
• Only two integration intervals with Fstats gt 6
• Using a pure Fstat detector, measuring signal
  duration based on above-threshold-time-durations
  will yield very short signal durations
• Signal presence is obvious to the human eye by
  the dominant azimuth and trace velocity

Etna Volcano
I26DE, Freyung, Germany
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Motivation

• Acoustic signal from shuttle launch STS-96
  recorded at DLIAR, Los Alamos
• a significant signal
• Distinct lack of
• correlation
• amplitude
• Average Fstat around 4.3
• Maximum Fstat 6
• Using a pure Fstat detector, determining signal
  duration based on above threshold time durations
  will yield very short signal durations
• Signal presence is obvious to the human eye due
  to the dominant azimuth and trace velocity

STS-96
DLIAR, Los Alamos
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Motivation

• Need to design a detection strategy based on
  the persistence of the measured backazimuth
  during the passage of the signal
• Certain tools in Pattern Recognition theory may
  be useful
• Envisage a detection algorithm that
• has two parts
• parameter extraction over short time intervals.
• seeks regions of persistent backazimuth,
  regardless of any threshold parameter.
• Thresholding
• since the short duration industrial-type signals
  are more prevalent than the longer duration
  signals of interest, and generally not as
  important, define a set of signal duration
  thresholds that may be frequency band-dependent.
• Uses 3D array geometry

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The Hough Transform

• method for doing pattern recognition
• extracts parametric curve information from binary
  pixelated data in the presence of noise
• consider binary image data
• apply the transformation
• maps points in S into lines in parameter space, P
• all points on the same line in S have the same
  intersection point in P
• Pairs of points (xiyi), (xj,yj) in S are mapped
  to the point (m,t)in P where
• Problem of detecting spatially extended lines
  becomes one of finding a local maximum

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The Hough Transform
Image Space, S
Parameter Space, P
Hough Transform
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The Hough Transform
Image Space, S
Parameter Space, P
STS-96
Hough Transform
DLIAR, Los Alamos
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INFER Detector Design (I)

• Detection philosophy
• measure accurately
• signal duration
• arrival time
• backazimuth
• one single detection per phase
• relieves the burden on down-stream processing
• does away with the infrasonic coda phase

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INFER Detector Design (I)

• Two stage detection process
• signal parameter estimation in 3D
• delay-and-sum correlation (in use)
• global minimization of the mis-match between
  theoretical and stacked trace beam powers
  (presently testing -see Poster)
• maximum beam-power by contracting grid (presently
  testing -see Poster)
• Signal detection via the Hough Transform
• to prevent spurious associations between pairs of
  points, define a maximum time separation that can
  exist between any pair of points.
• define an accumulator mesh whose granularity is
  commensurate with the uncertainty in the measured
  azimuth.
• keep track of the features of all integration
  intervals that contributed to any one accumulator
  cell..

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INFER Detector Design (II)

• Phase Identification
• may need to decide phase during source location
• Basic nomenclature
• Thresholding for detection
• signal duration dependent thresholds
• Fstat
• STA/LTA
• take the STA interval to be the entire
  integration interval
• take the LTA interval to be the entire time-block
  requested
• various norms L1, L2 integrated power
• signal duration

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INFER Detector Design (III)

• Thresholds
• signal duration dependent
• processing frequency-band dependent

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3D Array Processing

• Jin Wang (1999) shows that significant vertical
  sensor separations can strongly influence
• measured backazimuth
• magnitude slowness
• beam power
• We extend slowness plane to 3rd dimension
  (sx,sy,sz)
• Create a mesh in the 3D half-space
• Consider only those points that lie in an e
  thick dome centered on the acoustic slowness.
• Recently Use e 0 !
• Use Cs determined by
• Value of Temperature recorded at the array
• climatological model
• Constant 330 m/s

e
s 1/Cs
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Results
Shuttle launch signal -DLIAR
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Results
Gas pipeline explosion -DLIAR
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Results
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Results
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Results
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Results
Seattle Earthquake -I10CA
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Results
Acapulco Bolide -I59US
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Results
Etna volcano -I26DE
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Summary

• Detecting infrasound signals from IMS array data
  by seeking regions of constant backazimuth seems
  to be a robust procedure
• backazimuth can be accurately determined
• signal duration can be accurately inferred
• The Hough Transform can be used quite effectively
  to look for regions of constant backazimuth
• signals with moderate to small Fstat can be
  detected
• An automatic signal detection algorithm called
  INFER based on the Hough Transform has been
  created
• uses 3D array geometry
• uses correlator to do feature extraction
• testing other procedures
• incorporates basic phase id
• interfaces dynamically with the CSS database
  tables