review of coherent noise suppression methods gerard t. schuster university of utah
TRANSCRIPT
Review of Coherent Review of Coherent Noise Suppression Noise Suppression
MethodsMethods
Gerard T. SchusterGerard T. SchusterUniversity of UtahUniversity of Utah
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
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
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
Problem: Dune Waves Obscure PPProblem: Dune Waves Obscure PP
Dune WavesDune Waves
• Coherent Filtering MethodsCoherent Filtering Methods• ARCO Field Data ResultsARCO Field Data Results• Multicomponent Data ExampleMulticomponent Data Example• Conclusion and DiscussionConclusion and Discussion
OutlineOutline
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
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
DistanceDistance
Tim
eT
ime
PP
Tau
TauTransformTransform
SumSum
Tau-P TransformTau-P Transform
DistanceDistance
Tim
eT
ime TransformTransform
Tau-P TransformTau-P Transform
PP
Tau
Tau
Tau-P TransformTau-P Transform
DistanceDistance
Tim
eT
ime TransformTransform
Tau-P TransformTau-P Transform
PP
Tau
Tau
Tau-P TransformTau-P Transform
Mute NoiseMute Noise
Tau
Tau
DistanceDistance
Tim
eT
ime TransformTransform
Problem: IndistinctProblem: IndistinctSeparation Signal/NoiseSeparation Signal/Noise
PP
Tau-P TransformTau-P Transform
Tau
Tau
DistanceDistance
Tim
eT
ime TransformTransform
PP
Tau-P TransformTau-P Transform Hyperbolic TransformHyperbolic Transform
Distinct SeparationDistinct Separation Signal/NoiseSignal/Noise
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
DistanceDistance
Tim
eT
ime
AA
BB
pp
Tim
eT
ime
Filtering by ParabolicFiltering by Parabolic - p - p
Signal/NoiseSignal/NoiseOverlap Overlap
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
DistanceDistance
Tim
eT
ime
PSPS
PPPP
Filtering by LSMF Filtering by LSMF
MM11MM22
ZZ
XX
LL-1-1pp
LL-1-1
ss
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
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 $$$Better physics --> Better focusing, more $$$
OutlineOutline• Coherent Filtering MethodsCoherent Filtering Methods• ARCO Surface Wave Data ARCO Surface Wave Data • Multicomponent Data ExampleMulticomponent Data Example• Conclusion and DiscussionConclusion and Discussion
ARCO Field Data ARCO Field Data Offset (ft)Offset (ft)
Tim
e (
sec)
Tim
e (
sec)
003500350020002000
2.52.5
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
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.)
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)
• 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
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
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
LSMF Separation LSMF Separation
1.4
0
Tim
e (s
)
0
Offset (m) 5000
0
Offset (m) 5000
Horizontal ComponentHorizontal Component Vertical ComponentVertical Component
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
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
• 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
CRG1 (Vertical component)
Tim
e (
s)
0
4
CRG1 Data after Using F-K Filtering
CRG1 Raw Data
CRG1 (Vertical component)
Tim
e (
s)
0
4
CRG1 (Vertical component)
Tim
e (
s)
0
4
CRG1 Data after Using LSMF
CRG2 (Vertical component)
Tim
e (
s)
0
4
CRG2 Data after Using F-K Filtering (vertical component)
CRG2 (Vertical component)
Tim
e (
s)
0
4
CRG2 Raw Data (vertical component)
CRG2 (Vertical component)
Tim
e (
s)
0
4
CRG2 Data after Using LSMF (vertical component)
• Coherent Filtering MethodsCoherent Filtering Methods• ARCO Field Data ResultsARCO Field Data Results• Multicomponent Data ExampleMulticomponent Data Example• Conclusion and DiscussionConclusion and Discussion
OutlineOutline
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