Review of Coherent Noise Suppression Methods
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Review of Coherent Noise Suppression Methods. Gerard T. Schuster University of Utah. Problem: Ground Roll Degrades Signal. Offset (ft). 2000. 3500. 0. Reflections. Time (sec). Ground Roll. 2.5. Problem: PS Waves Degrade Signal. 0. Reflections. Time (sec). Converted S Waves.

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Review of Coherent Noise Suppression Methods

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Review of coherent noise suppression methods

Review of Coherent Noise Suppression Methods

Gerard T. Schuster

University of Utah


Review of coherent noise suppression methods

Problem: Ground Roll Degrades Signal

Offset (ft)

2000

3500

0

Reflections

Time (sec)

Ground

Roll

2.5


Review of coherent noise suppression methods

Problem: PS Waves Degrade Signal

0

Reflections

Time (sec)

Converted S Waves

4.0


Review of coherent noise suppression methods

Problem: Tubes Waves Obscure PP

2000

Depth (ft)

3100

0

Reflections

Reflections

Time (sec)

Time

(s)

Aliased tube waves

Converted S Waves

0.14

4.0


Review of coherent noise suppression methods

Problem: Dune Waves Obscure PP

Dune Waves


Review of coherent noise suppression methods

Outline

  • Coherent Filtering Methods

  • ARCO Field Data Results

  • Multicomponent Data Example

  • Conclusion and Discussion


Review of coherent noise suppression methods

Traditional Filtering Methods

F-K Dip Filtering

Filtering in  - p domain

linear  - p

parabolic  - p

hyperbolic  - p

Least Squares Migration Filter


Review of coherent noise suppression methods

Overlap

Signal & Noise

Separation Principle: Exploit Differences in

Moveout & Part. Velocity Directions

SIGNAL

SIGNAL

NOISE

Transform

Frequency

Time

NOISE

Wavenumber

Distance


Review of coherent noise suppression methods

Tau-P Transform

Sum

Transform

Tau

Time

P

Distance


Review of coherent noise suppression methods

Tau-P Transform

Tau-P Transform

Transform

Tau

Time

P

Distance


Review of coherent noise suppression methods

Mute Noise

Tau-P Transform

Tau-P Transform

Transform

Tau

Time

P

Distance


Review of coherent noise suppression methods

Problem: Indistinct

Separation Signal/Noise

Tau-P Transform

Transform

Tau

Time

P

Distance


Review of coherent noise suppression methods

Distinct Separation

Signal/Noise

Hyperbolic Transform

Tau-P Transform

Transform

Tau

Time

P

Distance


Review of coherent noise suppression methods

Breakdown of Hyperbolic

Assumption

Irregular Moveout

B

*

v

v

v

v

v

v

v

v

v

Time

A

Distance


Review of coherent noise suppression methods

Filtering by Parabolic - p

B

Time

Time

Signal/Noise

Overlap

A

p

Distance


Review of coherent noise suppression methods

d = L m +L m

Invert for m & m

Kirchhoff

Modeler

s

p

s

s

P-reflectivity

d = L m

p

p

Filtering by LSMF

s

PP

Time

PS

Distance


Review of coherent noise suppression methods

-1

L

s

-1

L

p

Filtering by LSMF

PP

Time

Z

PS

X

Distance

M1

M2


Review of coherent noise suppression methods

PP

d = L m +L m

PS

p

p

x

s

s

d = L m +L m

X

M1

M2

p

p

z

s

s

Filtering by LSMF

Time

Z

Distance


Review of coherent noise suppression methods

Summary

Traditionalcoherent filtering based on

approximate moveout

LSMF filtering operators based on

actual physics separating signal & noise

Better physics --> Better focusing, more $$$


Review of coherent noise suppression methods

Outline

  • Coherent Filtering Methods

  • ARCO Surface Wave Data

  • Multicomponent Data Example

  • Conclusion and Discussion


Review of coherent noise suppression methods

ARCO Field Data

Offset (ft)

2000

3500

0

Time (sec)

2.5


Review of coherent noise suppression methods

LSM Filtered Data (V. Const.)

ARCO Field Data

Offset (ft)

2000

3500

0

Time (sec)

2.5


Review of coherent noise suppression methods

F-K Filtered Data (13333ft/s)

LSM Filtered Data (V. Const.)

Offset (ft)

2000

3500

0

Time (sec)

2.5


Review of coherent noise suppression methods

F-X Spectrum of ARCO Data

S. of LSM Filtered Data (V. Const)

S. of F-K Filtered Data (13333ft/s)

Offset (ft)

2000

3500

0

Frequency (Hz)

50


Review of coherent noise suppression methods

Outline

  • Coherent Filtering Methods

  • ARCO Field Data Results

  • Multicomponent Data Example

  • Graben Example

  • Mahogony Example

  • Conclusion and Discussion


Graben velocity model

Graben Velocity Model

X (m)

0

5000

0

V1=2000 m/s

V2=2700 m/s

V3=3800 m/s

Depth (m)

V4=4000 m/s

V5=4500 m/s

3000


Synthetic data

Synthetic Data

Offset (m)

Offset (m)

5000

0

5000

0

0

PP1

PP2

Time (s)

PP3

PP4

1.4

Horizontal Component

Vertical Component


Lsmf separation

LSMF Separation

5000

0

Offset (m)

5000

0

Offset (m)

0

Time (s)

1.4

Horizontal Component

Vertical Component


True p p and p sv reflection

True P-P and P-SV Reflection

5000

0

Offset (m)

5000

0

Offset (m)

0

Time (s)

1.4

Horizontal Component

Vertical Component


F k filtering separation

F-K Filtering Separation

5000

0

Offset (m)

5000

0

Offset (m)

0

PP1

PP2

Time (s)

PP3

PP4

1.4

Horizontal Component

Vertical Component


Review of coherent noise suppression methods

Outline

  • Coherent Filtering Methods

  • ARCO Field Data Results

  • Multicomponent Data Example

  • Graben Example

  • Mahogony Field Data

  • Conclusion and Discussion


Crg1 data after using f k filtering

CRG1 Data after Using F-K Filtering

0

Time (s)

4

CRG1 (Vertical component)


Crg1 raw data

CRG1 Raw Data

0

Time (s)

4

CRG1 (Vertical component)


Crg1 data after using lsmf

CRG1 Data after Using LSMF

0

Time (s)

4

CRG1 (Vertical component)


Crg2 data after using f k filtering vertical component

CRG2 Data after Using F-K Filtering (vertical component)

0

Time (s)

4

CRG2 (Vertical component)


Crg2 raw data vertical component

CRG2 Raw Data (vertical component)

0

Time (s)

4

CRG2 (Vertical component)


Crg2 data after using lsmf vertical component

CRG2 Data after Using LSMF (vertical component)

0

Time (s)

4

CRG2 (Vertical component)


Review of coherent noise suppression methods

Outline

  • Coherent Filtering Methods

  • ARCO Field Data Results

  • Multicomponent Data Example

  • Conclusion and Discussion


Review of coherent noise suppression methods

Conclusions

Filtering signal/noise using: moveout

difference & particle velocity direction

- Traditional filtering $ vs $$$$ LSMF

LSMF computes moveout and particle

velocity direction based on true physics.


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