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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
Adaptive filtering in wavelet frames: applicationto echo (multiple) suppression in geophysics
S. Ventosa, S. Le Roy, I. Huard, A. Pica, H. Rabeson, P.Ricarte, L. Duval, M.-Q. Pham, C. Chaux, J.-C. Pesquet
IFPENlaurent.duval [ad] ifpen.fr
SIERRA 2014, Saint-Etienne
2014/03/25
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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In just one slide: on echoes and morphingWavelet frame coefficients: data and model
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Figure 1: Morphing and adaptive subtraction required
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Agenda
1. Issues in geophysical signal processing
2. Problem: multiple reflections (echoes)• adaptive filtering with approximate templates
3. Continuous, complex wavelet frames• how they (may) simplify adaptive filtering• and how they are discretized (back to the discrete world)
4. Adaptive filtering (morphing)• without constraint: unary filters (on analytic signals)• with constraints: proximal tools
5. Conclusions
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Issues in geophysical signal processing
Figure 2: Seismic data acquisition and wave fields
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Issues in geophysical signal processing
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Figure 3: Seismic data: aspect & dimensions (time, offset)
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Issues in geophysical signal processingShot number
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Figure 4: Seismic data: aspect & dimensions (time, offset)
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Issues in geophysical signal processing
Reflection seismology:
• seismic waves propagate through the subsurface medium
• seismic traces: seismic wave fields recorded at the surface• primary reflections: geological interfaces• many types of distortions/disturbances
• processing goal: extract relevant information for seismic data
• led to important signal processing tools:• ℓ1-promoted deconvolution (Claerbout, 1973)• wavelets (Morlet, 1975)
• exabytes (106 gigabytes) of incoming data
• need for fast, scalable (and robust) algorithms
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Multiple reflections and templates
Figure 5: Seismic data acquisition: focus on multiple reflections
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Multiple reflections and templates
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Figure 5: Reflection data: shot gather and template
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Multiple reflections and templates
Multiple reflections:
• seismic waves bouncing between layers
• one of the most severe types of interferences
• obscure deep reflection layers
• high cross-correlation between primaries (p) and multiples (m)
• additional incoherent noise (n)
• dptq “ pptq`mptq`nptq• with approximate templates: r1ptq, r2ptq,. . . rJptq
• Issue: how to adapt and subtract approximate templates?
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Multiple reflections and templates
2.8 3 3.2 3.4 3.6 3.8 4 4.2
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0
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Am
plitu
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Time (s)
DataModel
(a)
Figure 6: Multiple reflections: data trace d and template r1
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Multiple reflections and templatesMultiple filtering:
• multiple prediction (correlation, wave equation) has limitations
• templates are not accurate• mptq « ř
j hj ˙ rj?• standard: identify, apply a matching filer, subtract
hopt “ argminhPRl
}d´ h ˙ r}2
• primaries and multiples are not (fully) uncorrelated• same (seismic) source• similarities/dissimilarities in time/frequency
• variations in amplitude, waveform, delay
• issues in matching filter length:• short filters and windows: local details• long filters and windows: large scale effects
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Multiple reflections and templates
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(b)
Figure 7: Multiple reflections: data trace, template and adaptation
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Multiple reflections and templatesShot number
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Figure 8: Multiple reflections: data trace and templates, 2D version
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Multiple reflections and templates
• A long history of multiple filtering methods• general idea: combine adaptive filtering and transforms
• data transforms: Fourier, Radon• enhance the differences between primaries, multiples and noise• reinforce the adaptive filtering capacity
• intrication with adaptive filtering?
• might be complicated (think about inverse transform)
• First simple approach:• exploit the non-stationary in the data• naturally allow both large scale & local detail matching
ñ Redundant wavelet frames
• intermediate complexity in the transform
• simplicity in the (unary/FIR) adaptive filtering
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Hilbert transform and pairsReminders [Gabor-1946][Ville-1948]
{Htfupωq “ ´ı signpωq pfpωq
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Figure 9: Hilbert pair 1
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Hilbert transform and pairsReminders [Gabor-1946][Ville-1948]
{Htfupωq “ ´ı signpωq pfpωq
−4 −3 −2 −1 0 1 2 3−0.5
0
0.5
1
Figure 9: Hilbert pair 2
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Hilbert transform and pairsReminders [Gabor-1946][Ville-1948]
{Htfupωq “ ´ı signpωq pfpωq
−4 −3 −2 −1 0 1 2 3 4−2
−1.5
−1
−0.5
0
0.5
1
1.5
2
Figure 9: Hilbert pair 3
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Hilbert transform and pairsReminders [Gabor-1946][Ville-1948]
{Htfupωq “ ´ı signpωq pfpωq
−4 −3 −2 −1 0 1 2 3
−2
−1
0
1
2
3
Figure 9: Hilbert pair 4
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Continuous & complex wavelets
−3 −2 −1 0 1 2 3
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Figure 10: Complex wavelets at two different scales — 1
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Continuous & complex wavelets
−5 0 5
−0.5
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−0.5
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−8 −6 −4 −2 0 2 4 6 8−0.5
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Figure 11: Complex wavelets at two different scales — 2
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Continuous wavelets
• Transformation group:
affine = translation (τ) + dilation (a)
• Basis functions:
ψτ,aptq “ 1?aψ
ˆt´ τ
a
˙
• a ą 1: dilation• a ă 1: contraction• 1{?
a: energy normalization• multiresolution (vs monoresolution in STFT/Gabor)
ψτ,aptq FTÝÑ?aΨpafqe´ı2πfτ
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Continuous wavelets
• Definition
Cspτ, aq “żsptqψ˚
τ,aptqdt
• Vector interpretation
Cspτ, aq “ xsptq, ψτ,aptqy
projection onto time-scale atoms (vs STFT time-frequency)
• Redundant transform: τ Ñ τ ˆ a “samples”
• Parseval-like formula
Cspτ, aq “ xSpfq,Ψτ,apfqy
ñ sounder time-scale domain operations! (cf. Fourier)
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Continuous waveletsIntroductory example
Data Real part
Imaginary partModulus
Figure 12: Noisy chirp mixture in time-scale & sampling
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Continuous waveletsNoise spread & feature simplification (signal vs wiggle)
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Figure 13: Noisy chirp mixture in time-scale: zoomed scaled wiggles
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Continuous wavelets
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Figure 14: Which morphing is easier: time or time-scale?
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Continuous wavelets
• Inversion with another wavelet φ
sptq “ij
Cspu, aqφu,aptqdudaa2
ñ time-scale domain processing! (back to the trace signal)
• Scalogram|Cspt, aq|2
• Energy conversation
E “ij
|Cspt, aq|2dtdaa2
• Parseval-like formula
xs1, s2y “ij
Cs1pt, aqC˚s2
pt, aqdtdaa2
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Continuous wavelets
• Wavelet existence: admissibility criterion
0 ă Ah “ż `8
0
pΦ˚pνqΨpνqν
dν “ż 0
´8
pΦ˚pνqΨpνqν
dν ă 8
generally normalized to 1
• Easy to satisfy (common freq. support midway 0 & 8)
• With ψ “ φ, induces band-pass property:• necessary condition: |Φp0q| “ 0, or zero-average shape• amplitude spectrum neglectable w.r.t. |ν| at infinity
• Example: Morlet-Gabor (not truly admissible)
ψptq “ 1?2πσ2
e´ t2
2σ2 e´ı2πf0t
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Discretization and redundancy
Being practical again: dealing with discrete signals
• Can one sample in time-scale (CWT) domain:
Cspτ, aq “żsptqψ˚
τ,aptqdt, ψτ,aptq “ 1?aψ
ˆt´ τ
a
˙
with cj,k “ Cspkb0aj0, aj0q, pj, kq P Z and still be able to
recover sptq?• Result 1 (Daubechies, 1984): there exists a wavelet frame ifa0b0 ă C, (depending on ψ). A frame is generally redundant
• Result 2 (Meyer, 1985): there exist an orthonormal basis for aspecific ψ (non trivial, Meyer wavelet) and a0 “ 2 b0 “ 1
Now: how to choose the practical level of redundancy?
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Discretization and redundancy
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3
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7
8
Figure 15: Wavelet frame sampling: J “ 21, b0 “ 1, a0 “ 1.1
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Discretization and redundancy
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2
3
4
5
6
7
8
Figure 15: Wavelet frame sampling: J “ 5, b0 “ 2, a0 “?2
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Discretization and redundancy
0 20 40 60 80 100 1201
2
3
4
5
6
7
8
Figure 15: Wavelet frame sampling: J “ 3, b0 “ 1, a0 “ 2
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Discretization and redundancy
0 0.5 1 1.5 2 2.5 3 3.5 4Time (s)
primarymultiplenoisesum
0 0.5 1 1.5 2 2.5 3 3.5 4−0.1
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0.1
0.15
Time (s)
true multipleadapted multiple
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810
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2010
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RedundancyS/N (dB)
Med
ian
S/N
adap
t (dB
)
10
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Figure 16: Redundancy selection with variable noise experiments
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Discretization and redundancy
• Complex Morlet wavelet:
ψptq “ π´1{4e´iω0te´t2{2, ω0: central frequency
• Discretized time r, octave j, voice v:
ψvr,jrns “ 1?
2j`v{Vψ
ˆnT ´ r2jb0
2j`v{V
˙, b0: sampling at scale zero
• Time-scale analysis:
d “ dvr,j “@drns, ψv
r,jrnsD
“ÿ
n
drnsψvr,jrns
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Discretization and redundancy
Time (s)
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Figure 17: Morlet wavelet scalograms, data and templates
Take advantage from the closest similarity/dissimilarity:
• remember wiggles: on sliding windows, at each scale, a singlecomplex coefficient compensates amplitude and phase
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Unary filters
• Windowed unary adaptation: complex unary filter h (aopt)compensates delay/amplitude mismatches:
aopt “ argmintajupjPJq
›››››d ´ÿ
j
ajrk
›››››
2
• Vector Wiener equations for complex signals:
xd, rmy “ÿ
j
aj xrj , rmy
• Time-scale synthesis:
drns “ÿ
r
ÿ
j,v
dvr,jrψvr,jrns
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Results
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2000
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Figure 18: Wavelet scalograms, data and templates, after unary adaptation
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Results (reminders)
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Figure 19: Wavelet scalograms, data and templates
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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ResultsShot number
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)120014001600180020002200
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Figure 20: Original data
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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ResultsShot number
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)120014001600180020002200
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2
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Figure 21: Filtered data, “best” template
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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ResultsShot number
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e (s
)120014001600180020002200
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2
2.2
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Figure 22: Filtered data, three templates
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Going a little furtherImpose geophysical data related assumptions: e.g. sparsity
1
4/3
3/2
2
3
4
Figure 23: Generalized Gaussian modeling of seismic data wavelet frame
decomposition with different power laws.
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Variational approach
minimizexPH
Jÿ
j“1
fjpLjxq
with lower-semicontinuous proper convex functions fj and bounded linear
operators Lj .
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Variational approach
minimizexPH
Jÿ
j“1
fjpLjxq
with lower-semicontinuous proper convex functions fj and bounded linear
operators Lj .
• fj can be related to noise (e.g. a quadratic term when thenoise is Gaussian),
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Variational approach
minimizexPH
Jÿ
j“1
fjpLjxq
with lower-semicontinuous proper convex functions fj and bounded linear
operators Lj .
• fj can be related to noise (e.g. a quadratic term when thenoise is Gaussian),
• fj can be related to some a priori on the target solution (e.g.an a priori on the wavelet coefficient distribution),
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
37/46
Variational approach
minimizexPH
Jÿ
j“1
fjpLjxq
with lower-semicontinuous proper convex functions fj and bounded linear
operators Lj .
• fj can be related to noise (e.g. a quadratic term when thenoise is Gaussian),
• fj can be related to some a priori on the target solution (e.g.an a priori on the wavelet coefficient distribution),
• fj can be related to a constraint (e.g. a support constraint),
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
37/46
Variational approach
minimizexPH
Jÿ
j“1
fjpLjxq
with lower-semicontinuous proper convex functions fj and bounded linear
operators Lj .
• fj can be related to noise (e.g. a quadratic term when thenoise is Gaussian),
• fj can be related to some a priori on the target solution (e.g.an a priori on the wavelet coefficient distribution),
• fj can be related to a constraint (e.g. a support constraint),
• Lj can model a blur operator,
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
37/46
Variational approach
minimizexPH
Jÿ
j“1
fjpLjxq
with lower-semicontinuous proper convex functions fj and bounded linear
operators Lj .
• fj can be related to noise (e.g. a quadratic term when thenoise is Gaussian),
• fj can be related to some a priori on the target solution (e.g.an a priori on the wavelet coefficient distribution),
• fj can be related to a constraint (e.g. a support constraint),
• Lj can model a blur operator,
• Lj can model a gradient operator (e.g. total variation),
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
37/46
Variational approach
minimizexPH
Jÿ
j“1
fjpLjxq
with lower-semicontinuous proper convex functions fj and bounded linear
operators Lj .
• fj can be related to noise (e.g. a quadratic term when thenoise is Gaussian),
• fj can be related to some a priori on the target solution (e.g.an a priori on the wavelet coefficient distribution),
• fj can be related to a constraint (e.g. a support constraint),
• Lj can model a blur operator,
• Lj can model a gradient operator (e.g. total variation),
• Lj can model a frame operator.37/46
Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Problem re-formulation
dpkqloomoonobserved signal
“ ppkqloomoonprimary
` mpkqloomoonmultiple
` npkqloomoonnoise
Assumption: templates linked to mpkq throughout time-varying(FIR) filters:
mpkq “J´1ÿ
j“0
ÿ
p
hppqj pkqrpk´pq
j
where• h
pkqj : unknown impulse response of the filter corresponding to
template j and time k, then:
dloomoonobserved signal
“ ploomoonprimary
`R hloomoonfilter
` nloomoonnoise
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Results: synthetics (noise: σ “ 0.08)
100 200 300 400 500 600 700 800 900 1000
s
z
s
r2
r1
y
y
Original signal y
Estimated signal pyModel r1
Model r2
Original multiple s
Estimated multiple psObserved signal z
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
40/46
Assumptions
• F is a frame, p is a realization of a random vector P :
fP ppq9 expp´ϕpFpqq,
• h is a realization of a random vector H:
fHphq9 expp´ρphqq,
• n is a realization of a random vector N , of probability density:
fN pnq9 expp´ψpnqq,
• slow variations along time and concentration of the filters
|hpn`1qj ppq ´ h
pnqj ppq| ď εj,p ;
J´1ÿ
j“0
rρjphjq ď τ
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Results: synthetics
minimizeyPRN ,hPRNP
ψ`z ´ Rh ´ y
˘loooooooomoooooooonfidelity: noise-realted
` ϕpFyqloomoona priori on signal
` ρphqloomoona priori on filters
• ϕk “ κk| ¨ | (ℓ1-norm) where κk ą 0
• rρjphjq: }hj}ℓ1 , }hj}2ℓ2 or }hj}ℓ1,2• ψ
`z ´ Rh ´ y
˘: quadratic (Gaussian noise)
350 400 450 500 550 600 650 700 350 400 450 500 550 600 650 700
540 560 580 600
Figure 24: Simulated results with heavy noise.41/46
Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Results: synthetics
SNRy SNRs
σ \ rρ ℓ1 ℓ2 ℓ1,2 ℓ1 ℓ2 ℓ1,20.01 20.90 21.23 23.57 24.36 24.68 26.74
0.02 20.89 21.16 23.51 22.53 23.02 23.76
0.04 19.00 19.90 20.67 20.15 20.14 19.84
0.08 17.55 16.81 17.34 16.96 16.56 15.96
Signal-to-noise ratios (SNR, averaged over 100 noise realisations)
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Results: potential on real data
Figure 25: Portion of a receiver gather: recorded data.
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Results: potential on real data
Figure 25: (a) Unary filters (b) Proximal FIR filters.
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Conclusions
Take-away messages:
• Practical side• Competitive with more standard 2D processing• Very fast (unary part): industrial integration
• Technical side• Lots of choices, insights from 1D or 1.5D• Non-stationary, wavelet-based, adaptive multiple filtering• Take good care in cascaded processing
• Present work• Other applications: pattern matching, (voice) echo
cancellation, ultrasonic/acoustic emissions with home-madetemplates
• Going 2D: crucial choices on redundancy, directionality
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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Conclusions
Now what’s next: curvelets, shearlets, dual-tree complex wavelets?
Figure 26: From T. Lee (TPAMI-1996): 2D Gabor filters (odd and even)
or Weyl-Heisenberg coherent states
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Context Multiple filtering Wavelets Discretization, unary filters Results Going proximal Conclusions
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References
Ventosa, S., S. Le Roy, I. Huard, A. Pica, H. Rabeson, P. Ricarte,and L. Duval, 2012, Adaptive multiple subtraction withwavelet-based complex unary Wiener filters: Geophysics, 77,V183–V192; http://arxiv.org/abs/1108.4674
Pham, M. Q., C. Chaux, L. Duval, L. and J.-C. Pesquet, 2014, APrimal-Dual Proximal Algorithm for Sparse Template-BasedAdaptive Filtering: Application to Seismic Multiple Removal: IEEETrans. Signal Process., accepted;http://tinyurl.com/proximal-multiple
Jacques, L., L. Duval, C. Chaux, and G. Peyre, 2011, A panoramaon multiscale geometric representations, intertwining spatial,directional and frequency selectivity: Signal Process., 91,2699–2730; http://arxiv.org/abs/1101.5320
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