Theory of Multisource Crosstalk Reduction by Phase-Encoded Statics
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Theory of Multisource CrosstalkTheory of Multisource Crosstalk
Reduction by Phase-Encoded StaticsReduction by Phase-Encoded Statics
G. Schuster, X. Wang, Y. Huang, C. BoonyasiriwatG. Schuster, X. Wang, Y. Huang, C. BoonyasiriwatKing Abdullah University Science & TechnologyKing Abdullah University Science & Technology
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OutlineOutline1.1. Seismic Experiment:Seismic Experiment:
L m = d
L m = d1 1
L m = d2 2...N N
2. Standard vs Phase Encoded Least Squares Soln.2. Standard vs Phase Encoded Least Squares Soln.
LL11
LL22
dd 11
dd 22m =m = vs N L + N LN L + N L11 2211 22[ ]mm = [N = [N dd + N + N dd ] ]11 2211 22
3. Theory Noise Reduction3. Theory Noise Reduction 4. Summmary and Road Ahead4. Summmary and Road Ahead
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Gulf of Mexico Seismic SurveyGulf of Mexico Seismic Survey
mm
L m = d
L m = d1 1
L m = d2 2...N N
Time (s)
6 X (km)
4
0
d
Goal:Goal: Solve overdetermined Solve overdeterminedSystem of equations for mSystem of equations for m
Predicted data Observed data
Problem:Problem: Expensive, one Expensive, one migration/shot gathermigration/shot gather
Solution:Solution: Supergather Supergather migrationmigration
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Brief History Multisource Brief History Multisource Phase Encoded ImagingPhase Encoded Imaging
Romero, Ghiglia, Ober, & Morton, Geophysics, (2000)
Krebs, Anderson, Hinkley, Neelamani, Lee, Baumstein, Lacasse, SEG, (2009)
Virieux and Operto, EAGE, (2009)
Dai and Schuster, SEG, (2009)
Migration
Waveform Inversion and Least Squares Migration
Biondi et al., SEG, (2009)
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OutlineOutline1.1. Seismic Experiment:Seismic Experiment:
L m = d
L m = d1 1
L m = d2 2...N N
2. Standard vs Phase Encoded Least Squares Soln.2. Standard vs Phase Encoded Least Squares Soln.
LL11
LL22
dd 11
dd 22m =m = vs N L + N LN L + N L11 2211 22[ ]mm = [N = [N dd + N + N dd ] ]11 2211 22
3. Theory Noise Reduction3. Theory Noise Reduction 4. Summmary and Road Ahead4. Summmary and Road Ahead
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(k)(k)
Conventional Least Squares Conventional Least Squares Solution: Solution: L=L= & & d = d =
GivenGiven: : LLm=dm=d
FindFind: m : m s.t.s.t. min|| min||LLm-d||m-d||22
SolutionSolution: m = [: m = [L LL L] ] LL d d TT TT-1-1
m = m – m = m – LL ( (LLm - d) m - d) TT(k+1)(k+1) (k)(k) (k)(k)(k)(k)
or if or if LL is too big is too big
ProblemProblem::
LL11
LL22
dd 11
dd 22
= m – = m – LL ( (L L m - d ) m - d ) (k)(k)
+ L+ L ( (L L m - d ) m - d ) 11 11 22 22 2211
TTTT[ ]
In general, hugedimension matrix
Note: subscripts agree
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Conventional Least Squares Conventional Least Squares Solution: Solution: L=L= & & d = d =
GivenGiven: : LLm=dm=d
FindFind: m : m s.t.s.t. min|| min||LLm-d||m-d||22
SolutionSolution: m = [: m = [L LL L] ] LL d d TT TT-1-1
m = m – m = m – LL ( (LLm - d) m - d) TT(k+1)(k+1) (k)(k) (k)(k)(k)(k)
ProblemProblem::
LL11
LL22
dd 11
dd 22
= m – = m – LL ( (L L m - d ) m - d ) (k)(k)
+ L+ L ( (L L m - d ) m - d ) 11 11 22 22 2211
TTTT[ ]
In general, hugedimension matrix
Problem: Expensive, FD solve/CSGSolution: Blend+encode Data
(k)(k)
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Blending+Phase EncodingBlending+Phase Encoding
22 dd = = N d + N d + N dN d + N d + N d221111 3333
PhasePhasePhasePhaseBlendingBlending
Encoding MatrixEncodedsupergather
LL = = NN L + L + NN L L + + NN L L33 3322 2211 11m [ ]m
dd 11LL mm==11
Encoded supergather modeler
dd 33LL mm==33dd 22LL mm==22
BlendingBlending11
e i in in domain domain
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(k)(k)(k)(k)(k) (k) (k) (k)
++
Blended Phase-Encoded Least Squares Solution Blended Phase-Encoded Least Squares Solution
LL = = && dd = = N d + N dN d + N d
GivenGiven: : LLm=m=ddFindFind: m s.t. min||: m s.t. min||LLm-m-dd||||22
SolutionSolution: m = [: m = [LL LL] ] LL dd TT TT-1-1
TT(k+1)(k+1) (k)(k) (k)(k)(k)(k)
or if or if LL is too big is too big
11N L + N LN L + N L22 11
= m – = m – LL ( (L L m - d ) m - d ) (k)(k)
+ L+ L ( (L L m - d ) m - d ) 11 11 22 22 2211
TTTT[ ]
11 22 11 22 22
+ Crosstalk+ L+ L N N ( N N (L L m - d ) m - d ) 22
TT
11 L L N N (N N (L L m - d ) m - d ) 11
TT
11 2222 11 11 22 22
** **
(k)(k)
In general, SMALLdimension matrix
(k)(k) (k)(k)
Iterations are proxyFor ensemble averaging
(k+1)(k+1) (k)(k) (k)(k)
(k)(k) (k)(k)
(k)(k)(k)(k)(k)(k)(k)(k) (k)(k) (k)(k)
(k)(k) (k)(k)
m = m – m = m – LL ( (LLm - m - dd) )
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OutlineOutline1.1. Seismic Experiment:Seismic Experiment:
L m = d
L m = d1 1
L m = d2 2...N N
2. Standard vs Phase Encoded Least Squares Soln.2. Standard vs Phase Encoded Least Squares Soln.
LL11
LL22
dd 11
dd 22m =m = vs N L + N LN L + N L11 2211 22[ ]mm = [N = [N dd + N + N dd ] ]11 2211 22
3. Theory Noise Reduction3. Theory Noise Reduction
4. Summary 4. Summary
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Ensemble Average of Crosstalk TermEnsemble Average of Crosstalk Term
With Random Time ShiftsWith Random Time Shifts
++Crosstalk: LL N N ( N N (L L m - d ) m - d ) 22
TT
1111 L L N N (N N (L L m - d ) m - d ) 11
TT
11 2222 11 11 22 22
** **
N NN N1122
** = e e -i 2i 1 e i
1 2< > < > >= <
e i1 2= e
1 2
-2
dd1 2
~ e 2 2
e -i 2
Gaussian PDF
Noise
Crosstalk term decreaseswith increasing and
<Noise>:
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Crosstalk Prediction FormulaLL ( (L L m - d ) m - d ) 22
TT
1111 + L+ L ( (L L m - d ) m - d ) 11
TT
2222 e-2 2
O( )~X =
Pt. Scatt. Stand. Mig. Pt. Scatt.. Mig. of Supergathers.
Pt. Scatt.. Mig. of Supergathers.Pt. Scatt.. Mig. of Supergathers.
= .05 s = .1 s
= .01 s
X Offset
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Ensemble Average of Crosstalk TermEnsemble Average of Crosstalk Term
With Random PolarityWith Random Polarity
++Crosstalk: LL N N ( N N (L L m - d ) m - d ) 22
TT
1111 L L N N (N N (L L m - d ) m - d ) 11
TT
11 2222 11 11 22 22
** **
N NN N1122
** 1 2<Noise>: < > = < > = 0
sgn(
Noise
sgn(sgn(
Conclusion: Conclusion: Random polarity better than Random polarity better than random time shiftsrandom time shifts
Further Analysis: Further Analysis: Variance of the crosstalk noise Variance of the crosstalk noise
says that random polarity & random time shifts says that random polarity & random time shifts can be almost twice better than polarity alone can be almost twice better than polarity alone
< >N NN N1122
**( )22
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0 6.75X (km)
a) Standard migration (320 CSG) b) Time static σ = 0.1 s
f) SNR
c) Noise
0 6.75X (km) 0 6.75X (km)
0 6.75X (km) 0 6.75X (km)
e) Noise
0.01 0.1Time static σ (s)
polarity
Time static
Polarity and time static
Polarity+Time Statics+Location StaticsPolarity+Time StaticsPolarity
< < +/-+/- < < +/-+/-, , < < xx Time Statics
a) Polarity b) Noise d) Source polarity & static
Key Theory+Num. Results for 320 CSG SupergatherKey Theory+Num. Results for 320 CSG Supergather(Xin Wang, Yunsong Huang)(Xin Wang, Yunsong Huang)
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Key Results Theory of Multisource Imaging Key Results Theory of Multisource Imaging of Encoded Supergathers of Encoded Supergathers (Xin Wang)(Xin Wang)
Sig/Noise = GI < GINSig/Noise = GI < GIN
# geophones/supergather
# subsupergatherss
0 6.7X (km)
a) Image of 1 stack
Iteration Number
c) Image of 50 stacks
0 6.75
X (km)
0 6.7X (km)
b) Image of 5 stacks
1 115
d) SNR vs Iterations
Observed
Prediction
# iterations
Bulk shift
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Standard Migration SNR
GS# geophones/CSG# geophones/CSG
# CSGs# CSGs
SNR= ...
migrate
SNR=
d(t) =d(t) =Zero-mean white noise
[s(t) +n(t) ][s(t) +n(t) ] Neglect geometric spreading
Standard Migration SNR
Standard Migration SNR
Assume:
migrate+++
stack
S1
SGS G~~
iterate
GI
Iterative Multisrc. Mig. SNR
# iterations# iterations
SNR=
Cost ~ O(S)
Cost ~ O(I)
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OutlineOutline1.1. Seismic Experiment:Seismic Experiment:
L m = d
L m = d1 1
L m = d2 2...N N
2. Standard vs Phase Encoded Least Squares Soln.2. Standard vs Phase Encoded Least Squares Soln.
LL11
LL22
dd 11
dd 22m =m = vs N L + N LN L + N L11 2211 22[ ]mm = [N = [N dd + N + N dd ] ]11 2211 22
3. Theory Noise Reduction3. Theory Noise Reduction
4. Summary 4. Summary
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SNR: VS3. 3. GS GI
2. 2.
11. .
Polarity+Time Statics+Location StaticsPolarity+Time StaticsPolarity
< < +/- +/- < < +/-+/-, , < < xx Time Statics
LL11
LL22
dd 11
dd 22m =m = N L + N LN L + N L
11 2211 22[ ]mm = [N = [N dd + N + N dd ] ]
11 2211 22
Summary
vs
LL ( (L L m - d ) m - d ) 22
TT
1111 + L+ L ( (L L m - d ) m - d ) 11
TT
2222 e-2 2
O( )~< >
4. Passive Seismic Interferometry = Multisrc4. Passive Seismic Interferometry = Multisrc Imaging Imaging
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IO 1 1/320
Cost ~
Resolution dx 1 1
SNR~
Stnd. Mig Multsrc. LSMStnd. Mig Multsrc. LSM
Less 1
1 <1/10
Cost vs QualityCost vs Quality
Summary
1
LL11
LL22
dd 11
dd 22m =m = N L + N LN L + N L
11 2211 22[ ]mm = [N = [N dd + N + N dd ] ]
11 2211 22
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SN
R0
1 Number of Iterations 300
7The SNR of MLSM image grows as the square root of the number of iterations.
SNR = GI
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0 6.75X (km)
a) Standard migration (320 CSG) b) Time static σ = 0.1 s
f) SNR
c) Noise
0 6.75X (km) 0 6.75X (km)
0 6.75X (km) 0 6.75X (km)
e) Noise
0.01 0.1Time static σ (s)
polarity
Time static
Polarity and time static
Polarity+Time Statics+Location StaticsPolarity+Time StaticsPolarity
< +/- < +/-, < +/- < +/-, < < xx Time Statics
a) Polarity b) Noise d) Source polarity & static
Key Theory+Num. Results for 320 CSG SupergatherKey Theory+Num. Results for 320 CSG Supergather(Xin Wang, Yunsong Huang)(Xin Wang, Yunsong Huang)
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Key Results Theory of Multisource Imaging Key Results Theory of Multisource Imaging of Encoded Supergathers of Encoded Supergathers (Xin Wang)(Xin Wang)
Sig/Noise = GI < GINSig/Noise = GI < GIN
# geophones/supergather
# subsupergatherss
0 6.7X (km)
a) Image of 1 stack
Iteration Number
c) Image of 50 stacks
0 6.75
X (km)
0 6.7X (km)
b) Image of 5 stacks
1 115
d) SNR vs Iterations
Observed
Prediction
# iterations
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Key Results Theory of Multisource Imaging Key Results Theory of Multisource Imaging of Encoded Supergathers of Encoded Supergathers (Boonyasiriwat)(Boonyasiriwat)
Sig/Noise = GI < GINSig/Noise = GI < GIN
3.5 km
Dynamic QMC TomogramDynamic QMC Tomogram (99 CSGs/supergather)(99 CSGs/supergather)
Dynamic Polarity TomogramDynamic Polarity Tomogram(1089 CSGs/supergather)(1089 CSGs/supergather)
1/1000 1/300
# geophones/supergather
# iterations
# subsupergatherss
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Multisource Migration:Multisource Migration: mmmigmig=L=LTTdd
Forward Model:Forward Model:
Multisource Phase Encoded ImagingMultisource Phase Encoded Imaging
d +d +dd =[ =[L +L +LL ]m ]m11 222211
LL{dd{
=[=[L +L +LL ]( ](dd + + dd ) ) 11 222211
TT TT
= = L d +L d +L dL d + + 11 222211
TT TT
LL dd + +L L dd22 112211
Crosstalk noiseCrosstalk noiseStandard migrationStandard migration
TT TT
m = m +(k+1) (k)
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Polarity+Time Statics+Location StaticsPolarity+Time StaticsPolarity
Relative Merits of 4 Encoding StrategiesRelative Merits of 4 Encoding Strategies < < +/- +/- < < +/-+/-, , < < xx
dd 11LL mm==11 dd 33LL mm==33dd 22LL mm==22
Time Statics
supergather
Supergather #2
Supergather #3
Supergather #4
Supergather #1
< < +/- +/- < < +/-+/-, , < < xx
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Multisource Migration:Multisource Migration: mmmigmig=L=LTTdd
Forward Model:Forward Model:
Phase Encoded Multisource Migration Phase Encoded Multisource Migration
d +d +dd =[ =[L +L +LL ]m ]m11 222211
LL{dd{
=[=[L +L +LL ]( ](dd + + dd ) ) 11 222211
TT TT
= = L d +L d +L dL d + + 11 222211
TT TT
LL dd + +L L dd22 112211
Crosstalk noiseCrosstalk noiseStandard migrationStandard migration
TT TTmmmigmig
= = L d +L d +L dL d + + 11 222211
TT TT
LL dd + +LL dd22 112211
TT TTmmmigmig
mmmigmig
= = L d +L d +L dL d11 222211
mmmigmig
+ +
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Multisource Migration:Multisource Migration: mmmigmig=L=LTTdd
Forward Model:Forward Model:
Phase Encoded Multisrce Phase Encoded Multisrce Least Squares Least Squares Migration Migration
d +d +dd =[ =[L +L +LL ]m ]m11 222211
LL{dd{
=[=[L +L +LL ]( ](dd + + dd ) ) 11 222211
TT TTmmmigmig
= = L d +L d +L dL d + + 11 222211
TT TT
LL dd + +L L dd22 112211
Crosstalk noiseCrosstalk noiseStandard migrationStandard migration
TT TT
m = m +(k+1) (k)
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OutlineOutline1.1. Seismic Experiment:Seismic Experiment:
L m = d
L m = d1 1
L m = d2 2...N N
2. Standard vs Phase Encoded Least Squares Soln.2. Standard vs Phase Encoded Least Squares Soln.
LL11
LL22
dd 11
dd 22m =m = vs N L + N LN L + N L11 2211 22[ ]mm = [N = [N dd + N + N dd ] ]11 2211 22
3. Theory Noise Reduction3. Theory Noise Reduction
4. Numerical 4. Numerical
TestsTests
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RTM & FWI Problem & Possible Soln.RTM & FWI Problem & Possible Soln.
• ProblemProblem:: RTM & FWI computationally costly RTM & FWI computationally costly
• Solution:Solution: Multisource LSM & FWI Multisource LSM & FWI
Preconditioning speeds up by factor 2-3Preconditioning speeds up by factor 2-3
LSM reduces crosstalkLSM reduces crosstalk
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Multisource Migration:Multisource Migration: mmmigmig=L=LTTdd
Forward Model:Forward Model:
Multisource Least Squares Migration Multisource Least Squares Migration
d +d =[d +d =[L +L ]mL +L ]m11 222211
LL{dd{Standard migration
Crosstalk term
Phase encodingPhase encoding
Kirchhoff kernelKirchhoff kernel
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Multisource Least Squares Migration Multisource Least Squares Migration Crosstalk term
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Conventional Least Squares Conventional Least Squares Solution: Solution: L=L= & & d = d =
GivenGiven: : LLm=dm=d
FindFind: m s.t. min||: m s.t. min||LLm-d||m-d|| 22
SolutionSolution: m = [: m = [L LL L] ] LL d d TT TT-1-1
m = m – m = m – LL ( (LLm - d) m - d) TT(k+1)(k+1) (k)(k) (k)(k)(k)(k)
or if or if LL is too big is too big
ProblemProblem::
LL11
LL22
dd 11
dd 22
= m – = m – LL ( (L L m - d ) m - d ) (k)(k)
+ L+ L ( (L L m - d ) m - d ) 11 11 22 22 2211
TTTT[ ]
In general, hugedimension matrix
Note: subscripts agree
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Key Results Theory of Multisource Imaging Key Results Theory of Multisource Imaging of Encoded Supergathers of Encoded Supergathers (Boonyasiriwat)(Boonyasiriwat)
Sig/Noise = GI < GINSig/Noise = GI < GIN
3.5 km
Dynamic QMC TomogramDynamic QMC Tomogram (99 CSGs/supergather)(99 CSGs/supergather)
Dynamic Polarity TomogramDynamic Polarity Tomogram(1089 CSGs/supergather)(1089 CSGs/supergather)
1/1000 1/300
# geophones/supergather
# iterations
# subsupergatherss