Review of Coherent Noise Suppression Methods Gerard T. Schuster University of Utah.
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Transcript of Review of Coherent Noise Suppression Methods Gerard T. Schuster University of Utah.
![Page 1: Review of Coherent Noise Suppression Methods Gerard T. Schuster University of Utah.](https://reader036.fdocuments.net/reader036/viewer/2022062423/5697bffd1a28abf838cc1e93/html5/thumbnails/1.jpg)
Review of Coherent Review of Coherent Noise Suppression Noise Suppression
MethodsMethods
Gerard T. SchusterGerard T. SchusterUniversity of UtahUniversity of Utah
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Problem: Ground Roll Degrades SignalProblem: Ground Roll Degrades SignalOffset (ft)Offset (ft)
Tim
e (
sec)
Tim
e (
sec)
003500350020002000
2.52.5
ReflectionsReflections
Ground Ground RollRoll
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Problem: PS Waves Degrade SignalProblem: PS Waves Degrade SignalT
ime
(se
c)T
ime
(se
c)
00
4.04.0
ReflectionsReflections
Converted S WavesConverted S Waves
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Tim
e (
sec)
Tim
e (
sec)
4.04.0
ReflectionsReflections
Converted S WavesConverted S Waves
31003100Depth (ft)Depth (ft)2000200000
TimeTime(s)(s)
0.140.14
Problem: Tubes Waves Obscure PPProblem: Tubes Waves Obscure PP
ReflectionsReflections
Aliased tube wavesAliased tube waves
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Problem: Dune Waves Obscure PPProblem: Dune Waves Obscure PP
Dune WavesDune Waves
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• Coherent Filtering MethodsCoherent Filtering Methods• ARCO Field Data ResultsARCO Field Data Results• Multicomponent Data ExampleMulticomponent Data Example• Conclusion and DiscussionConclusion and Discussion
OutlineOutline
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F-K Dip FilteringF-K Dip Filtering Filtering in Filtering in - p - p domaindomain linear linear - p - p parabolic parabolic - p - p hyperbolic hyperbolic - p - p Least Squares Migration FilterLeast Squares Migration Filter
Traditional Filtering Traditional Filtering MethodsMethods
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DistanceDistance
Tim
eT
ime
NOISENOISE
SIGNALSIGNAL
WavenumberWavenumber
Fre
qu
ency
Fre
qu
ency
Separation Principle: Exploit Differences in Separation Principle: Exploit Differences in Moveout & Part. Velocity DirectionsMoveout & Part. Velocity Directions
SIGNALSIGNAL
NOISENOISETransformTransform
Overlap Overlap Signal & NoiseSignal & Noise
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DistanceDistance
Tim
eT
ime
PP
Tau
TauTransformTransform
SumSum
Tau-P TransformTau-P Transform
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DistanceDistance
Tim
eT
ime TransformTransform
Tau-P TransformTau-P Transform
PP
Tau
Tau
Tau-P TransformTau-P Transform
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DistanceDistance
Tim
eT
ime TransformTransform
Tau-P TransformTau-P Transform
PP
Tau
Tau
Tau-P TransformTau-P Transform
Mute NoiseMute Noise
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Tau
Tau
DistanceDistance
Tim
eT
ime TransformTransform
Problem: IndistinctProblem: IndistinctSeparation Signal/NoiseSeparation Signal/Noise
PP
Tau-P TransformTau-P Transform
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Tau
Tau
DistanceDistance
Tim
eT
ime TransformTransform
PP
Tau-P TransformTau-P Transform Hyperbolic TransformHyperbolic Transform
Distinct SeparationDistinct Separation Signal/NoiseSignal/Noise
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DistanceDistanceT
ime
Tim
e
Breakdown of Hyperbolic Breakdown of Hyperbolic AssumptionAssumption
vv vv vv vv vv vv vv vv vv**
AA
BB
Irregular MoveoutIrregular Moveout
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DistanceDistance
Tim
eT
ime
AA
BB
pp
Tim
eT
ime
Filtering by ParabolicFiltering by Parabolic - p - p
Signal/NoiseSignal/NoiseOverlap Overlap
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DistanceDistance
Tim
eT
ime PPPP
Filtering by LSMF Filtering by LSMF
PSPS
d =d = L m L m pp pp
d =d = L m L m ++ L mL mssss
ssP-reflectivityP-reflectivity
KirchhoffKirchhoffModelerModeler
Invert for Invert for mm & & mmpp ss
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DistanceDistance
Tim
eT
ime
PSPS
PPPP
Filtering by LSMF Filtering by LSMF
MM11MM22
ZZ
XX
LL-1-1pp
LL-1-1
ss
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DistanceDistance
Tim
eT
ime
PSPS
PPPP
Filtering by LSMF Filtering by LSMF
ZZ
ssssd =d = L m L m ++ L mL mpp ppxx
ssssMM11MM22XXd =d = L m L m ++ L mL mpp ppzz
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SummarySummary
TraditionalTraditional coherent filtering based on coherent filtering based on approximate moveoutapproximate moveout
LSMF filtering operators based onLSMF filtering operators based on actual physics separating actual physics separating signalsignal & & noisenoise
Better physics --> Better focusing, more $$$Better physics --> Better focusing, more $$$
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OutlineOutline• Coherent Filtering MethodsCoherent Filtering Methods• ARCO Surface Wave Data ARCO Surface Wave Data • Multicomponent Data ExampleMulticomponent Data Example• Conclusion and DiscussionConclusion and Discussion
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ARCO Field Data ARCO Field Data Offset (ft)Offset (ft)
Tim
e (
sec)
Tim
e (
sec)
003500350020002000
2.52.5
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LSM Filtered Data (V. Const.)LSM Filtered Data (V. Const.)Offset (ft)Offset (ft)
Tim
e (
sec)
Tim
e (
sec)
003500350020002000
2.52.5
ARCO Field Data ARCO Field Data
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F-K Filtered Data (13333ft/s)F-K Filtered Data (13333ft/s)Offset (ft)Offset (ft)
Tim
e (
sec)
Tim
e (
sec)
003500350020002000
2.52.5
LSM Filtered Data (V. Const.)LSM Filtered Data (V. Const.)
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F-X Spectrum of ARCO DataF-X Spectrum of ARCO DataOffset (ft)Offset (ft)
Fre
qu
ency
(H
z)F
req
uen
cy (
Hz)
003500350020002000
5050
S. of LSM Filtered Data (V. S. of LSM Filtered Data (V. Const)Const)
S. of F-K Filtered Data (13333ft/s)S. of F-K Filtered Data (13333ft/s)
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• Coherent Filtering MethodsCoherent Filtering Methods• ARCO Field Data ResultsARCO Field Data Results• Multicomponent Data ExampleMulticomponent Data Example Graben ExampleGraben Example
Mahogony ExampleMahogony Example
• Conclusion and DiscussionConclusion and Discussion
OutlineOutline
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Graben Velocity ModelGraben Velocity Model
05000
Dep
th (
m)
3000
0 X (m)
V1=2000 m/s
V2=2700 m/s
V3=3800 m/s
V4=4000 m/s
V5=4500 m/s
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Synthetic DataSynthetic Data
1.4
0
Tim
e (s
)
0 Offset (m) 5000
0 Offset (m)5000
Horizontal ComponentHorizontal Component Vertical ComponentVertical Component
PP1PP1
PP2PP2
PP3PP3
PP4PP4
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LSMF Separation LSMF Separation
1.4
0
Tim
e (s
)
0
Offset (m) 5000
0
Offset (m) 5000
Horizontal ComponentHorizontal Component Vertical ComponentVertical Component
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True P-P and P-SV ReflectionTrue P-P and P-SV Reflection
1.4
0
Tim
e (s
)
0
Offset (m) 5000
0
Offset (m) 5000
Horizontal ComponentHorizontal Component Vertical ComponentVertical Component
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F-K Filtering Separation F-K Filtering Separation
1.4
0
Tim
e (s
)
0
Offset (m) 5000
0
Offset (m) 5000
Horizontal ComponentHorizontal Component Vertical ComponentVertical Component
PP1PP1
PP2PP2
PP3PP3
PP4PP4
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• Coherent Filtering MethodsCoherent Filtering Methods• ARCO Field Data ResultsARCO Field Data Results• Multicomponent Data ExampleMulticomponent Data Example Graben ExampleGraben Example
Mahogony Field DataMahogony Field Data
• Conclusion and DiscussionConclusion and Discussion
OutlineOutline
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CRG1 (Vertical component)
Tim
e (
s)
0
4
CRG1 Data after Using F-K Filtering
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CRG1 Raw Data
CRG1 (Vertical component)
Tim
e (
s)
0
4
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CRG1 (Vertical component)
Tim
e (
s)
0
4
CRG1 Data after Using LSMF
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CRG2 (Vertical component)
Tim
e (
s)
0
4
CRG2 Data after Using F-K Filtering (vertical component)
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CRG2 (Vertical component)
Tim
e (
s)
0
4
CRG2 Raw Data (vertical component)
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CRG2 (Vertical component)
Tim
e (
s)
0
4
CRG2 Data after Using LSMF (vertical component)
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• Coherent Filtering MethodsCoherent Filtering Methods• ARCO Field Data ResultsARCO Field Data Results• Multicomponent Data ExampleMulticomponent Data Example• Conclusion and DiscussionConclusion and Discussion
OutlineOutline
![Page 39: Review of Coherent Noise Suppression Methods Gerard T. Schuster University of Utah.](https://reader036.fdocuments.net/reader036/viewer/2022062423/5697bffd1a28abf838cc1e93/html5/thumbnails/39.jpg)
Filtering signal/noise using: moveoutFiltering signal/noise using: moveout difference & particle velocity directiondifference & particle velocity direction
- Traditional filtering $ vs $$$$ LSMF- Traditional filtering $ vs $$$$ LSMF LSMF computes moveout and particleLSMF computes moveout and particle velocity direction based on true physics.velocity direction based on true physics.
ConclusionsConclusions