review of coherent noise suppression methods gerard t. schuster university of utah

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