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Introduction Sequential acoustic inversion Applications
Conclusion
Coastal acoustic tomography:
a sequential filtering approach
Olivier Carrière
PhD. advisor: Prof. Jean-Pierre Hermand
Environmental hydroacoustics lab.Université libre de Bruxelles
(U.L.B.)
http://[email protected]
ABAV Study day - 23-02-2011
Olivier Carrière Coastal acoustic tomography
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Introduction Sequential acoustic inversion Applications
Conclusion
Outline
1 Introduction
2 Sequential acoustic inversion
3 Applications
4 Conclusion
Olivier Carrière Coastal acoustic tomography
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Introduction Sequential acoustic inversion Applications
Conclusion
Outline
1 Introduction
2 Sequential acoustic inversion
3 Applications
4 Conclusion
Olivier Carrière Coastal acoustic tomography
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Introduction Sequential acoustic inversion Applications
Conclusion
Underwater acoustic tomography
Sound speed c = c(temperature, salinity, depth)
Varying sound speed : waveguide propagation
Surface and bottom reflections
Arrival time inversion (deep-water tomography)
Full-field inversion (matched field processing, MFP)
Impulse response inversion (model-based matched filter,
MBMF)
Olivier Carrière Coastal acoustic tomography
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Introduction Sequential acoustic inversion Applications
Conclusion
Underwater acoustic tomography
0 1 2 3 4 5 6 7 8 9 10
x 104
1000
2000
3000
4000
Range (m)
Depth (m)
BELLHOP ray tracing
1500 1550 1600
0
1000
2000
3000
4000
5000
Sound Speed (m/s)
Depth (m)
Munk profile
Olivier Carrière Coastal acoustic tomography
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Introduction Sequential acoustic inversion Applications
Conclusion
Coastal acoustic tomography environments
Shallow depths (< 300 m)
Range-dependence (bathymetry, temperature and salinity
fields)
Short temporal/spatial scales
Tidal currents
Strong acoustic-bottom interaction
Lack of resolvable arrivals
Currents → effective sound speed
Range-dependent acoustic propagation models
→ continuous monitoring based on ocean observatories
Olivier Carrière Coastal acoustic tomography
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Introduction Sequential acoustic inversion Applications
Conclusion
Coastal acoustic tomography environments
Shallow depths (< 300 m)
Range-dependence (bathymetry, temperature and salinity
fields)
Short temporal/spatial scales
Tidal currents
Strong acoustic-bottom interaction
Lack of resolvable arrivals
Currents → effective sound speed
Range-dependent acoustic propagation models
→ continuous monitoring based on ocean observatories
Olivier Carrière Coastal acoustic tomography
from Rodriguez and Jesus, Physical limitations of travel-time
based
shallow water tomography, J. Acoust. Soc. Am., 2000
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Introduction Sequential acoustic inversion Applications
Conclusion
Coastal acoustic tomography environments
Shallow depths (< 300 m)
Range-dependence (bathymetry, temperature and salinity
fields)
Short temporal/spatial scales
Tidal currents
Strong acoustic-bottom interaction
Lack of resolvable arrivals
Currents → effective sound speed
Range-dependent acoustic propagation models
→ continuous monitoring based on ocean observatories
Olivier Carrière Coastal acoustic tomography
0
20
40
60
80
100
120
0 500 1000 1500
depth (m)
range (m)
−80−30TL (dB)
NORMAL MODEWITHOUT LEAKY MODES
NORMAL MODEWITH LEAKY MODES
PARABOLIC EQUATION
300 Hz
800 Hz
1600 Hz
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Introduction Sequential acoustic inversion Applications
Conclusion
Outline
1 Introduction
2 Sequential acoustic inversion
3 Applications
4 Conclusion
Olivier Carrière Coastal acoustic tomography
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Introduction Sequential acoustic inversion Applications
Conclusion
State-space model
The inverse problem is formulated in a Gauss-Markov model
x(τm) = A[x(τm−1)] + w(τm−1)y(τm) = C[x(τm)] + v(τm)
x : sound-speed parameters
y : acoustic measurements
A[x] : transition model
C[x] : measurement model
w ∼ N (0, Rww ), v ∼ N (0, Rvv )
→ Transition model : random walk of the sound-speed parameters A
= 1
→ Nonlinear measurement model : numerical acoustic propagation
model
Olivier Carrière Coastal acoustic tomography
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Introduction Sequential acoustic inversion Applications
Conclusion
Kalman filter algorithm
GIVEN a set of noisy complex acoustic field measurements on a
vertical array,FIND the best (minimum error variance) estimate of
the sound-speed field ofthe environment.
Prediction
(1) Predict the states
x̂tk|tk−1 = A(x̂tk−1|tk−1)
(2) Predict the error covariance
P̃tk|tk−1 = AkP̃tk−1|tk−1AT
k+ Rww
Correction
(1) Compute the Kalman gain
Kk = P̃tk|tk−1CT
k(CkP̃tk|tk−1C
T
k+ Rvv)
−1
(2) Update the states
xtk|tk = x̂tk|tk−1 + Kk{ytk − C[x̂tk|tk−1 ]}
(3) Update the error covariance
P̃tk|tk = (I − KkCk)P̃tk|tk−1
Olivier Carrière Coastal acoustic tomography
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Introduction Sequential acoustic inversion Applications
Conclusion
Outline
1 Introduction
2 Sequential acoustic inversion
3 Applications
4 Conclusion
Olivier Carrière Coastal acoustic tomography
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Introduction Sequential acoustic inversion Applications
Conclusion
Weakly range-dependent environment
Refine the knowledge of the sound-speed field around the mean
sound-speedprofile
by discretizing the considered vertical slice
Olivier Carrière Coastal acoustic tomography
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Introduction Sequential acoustic inversion Applications
Conclusion
Weakly range-dependent environment
S-depth=60m, R-array=[30–90 m], 16 elements, |S − R| = 15 km
Transmission of 3-frequency multitones (250, 400, 630 Hz) every
hour
Carrière et al., Inversion for time-evolving sound-speed field
in a shallow ocean by ensembleKalman filtering, IEEE J. Ocean.
Eng., 2009
Olivier Carrière Coastal acoustic tomography
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Introduction Sequential acoustic inversion Applications
Conclusion
Weakly range-dependent environment
Spatial and temporal tracking of the sound-speed field
1508 1513 19 April 2007 06:00 L 20 April 2007 06:00 L1508
1513
24 April 2007 06:00 L1508 1513
21 April 2007 06:00 L1508 1513
22 April 2007 06:00 L1508 151323 April 2007 06:00 L1508 1513
Olivier Carrière Coastal acoustic tomography
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Introduction Sequential acoustic inversion Applications
Conclusion
Strongly range-dependent environment
The SSF is mainly determined by a known oceanic feature (front,
upwelling)
SSF parameterization based on a feature modelShallow source
(inshore) and bottom-anchored array (offshore)Lower frequency band
[200–500 Hz], longer range propagation, reducessensitivity to non
parameterized inhomogeneities
depth (m)
100
50
0
16
16
16
17
17
18
18
19
19
20
15 18 21
0
1
range (km)
0 10 20 30
melt value
T (°C)
15 21T (°C)
Inshore Offshore
15 21T (°C)
Olivier Carrière Coastal acoustic tomography
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Introduction Sequential acoustic inversion Applications
Conclusion
Strongly range-dependent environment
Upwelling feature tracking in Cabo Frio (Brazil)
0 10 3020
range (km)
dep
th (
m)
t = 00h t = 12h t = 24h
t = 36h t = 48h t = 60h
1510
1515
1520
1525
0
50
100
(m/s)
Oceanic model predictions vs. acoustically inverted FMCarrière
and Hermand, Feature-oriented acoustic tomography, IEEE J. Ocean.
Eng., submitted
Olivier Carrière Coastal acoustic tomography
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Introduction Sequential acoustic inversion Applications
Conclusion
Strongly range-dependent environment
Upwelling feature tracking in Cabo Frio (Brazil)
10 20 30 40 50 60 70 80 900
0.1
0.2
0.3
0.4
0.5T
RM
SE
(°C
)
time (h)
200 Hz400 Hz200, 250, 315, 400 Hz
Integrated temperature estimation error for different frequency
processing
Olivier Carrière Coastal acoustic tomography
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Introduction Sequential acoustic inversion Applications
Conclusion
Outline
1 Introduction
2 Sequential acoustic inversion
3 Applications
4 Conclusion
Olivier Carrière Coastal acoustic tomography
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Introduction Sequential acoustic inversion Applications
Conclusion
Conclusion
Accurate acoustic propagation modeling is critical for
performing accurateinversions
Sequential filtering approach provides an excellent framework
for coastalcontinuous monitoring and ocean observatories
Range-resolving and feature-oriented parameterization schemes
show goodperformances on realistic oceanic simulations
The simultaneous processing of lower and higher frequencies
enables toincrease the robustness and sensitivity of the inversion
method
Olivier Carrière Coastal acoustic tomography
Introduction
Sequential acoustic inversion
Applications
Conclusion
