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Overview of Some Coherent Noise Filtering Methods. Jianhua Yue, Yue Wang, Gerard Schuster University of Utah. Problem: Ground Roll Degrades Signal. Reflections. Ground Roll. Offset (ft). 2000. 3500. 0. Time (sec). 2.5. PP Reflections. Converted S Waves.

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slide1

Overview of SomeCoherent Noise Filtering Methods

Jianhua Yue, Yue Wang, Gerard Schuster

University of Utah

slide2

Problem: Ground Roll Degrades Signal

Reflections

Ground

Roll

Offset (ft)

2000

3500

0

Time (sec)

2.5

slide3

PP Reflections

Converted S Waves

Problem: PS Waves Degrade Signal

0

Time (sec)

4.0

slide4

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

slide5

Outline

  • Radon Filtering Methods
  • ARCO Field Data Results
  • Saudi Land Data
  • Multicomponent Data Example
  • Conclusion and Discussion
slide6

Model Noise and Adaptive Subtraction

Filter that Exploit Moveout Differences

Two Classes of Coherent

Noise Filtering

slide7

F-K Dip Filtering

Filtering in  - p domain

linear  - p

parabolic  - p

hyperbolic  - p

local+adaptive subtraction

Least Squares Migration Filter

Filtering Methods:

Moveout Separation

slide8

SIGNAL

SIGNAL

NOISE

Overlap

Signal & Noise

NOISE

Separation Principle: Exploit Differences in

Moveout & Part. Velocity Directions

Transform

Frequency

Time

Wavenumber

Distance

slide9

Tau-P Transform

Sum

Transform

Tau

Time

V=1/P

Distance

slide10

Tau-P Transform

Transform

Tau

Time

V=1/P

Distance

slide11

Mute Noise

Transform

Tau-P Transform

Tau

Time

V=1/P

Distance

slide12

Problem: Indistinct

Separation Signal/Noise

Tau-P Transform

Transform

Tau

Time

V=1/P

Distance

slide13

Distinct Separation

Signal/Noise Hi res.

Hyperbolic Transform

Tau-P Transform

Transform

Tau

Time

V=1/P

Distance

slide14

Breakdown of Hyperbolic

Assumption

Irregular Moveout

*

v

v

v

v

v

v

v

v

v

Time

Distance

slide15

d = L m +L m

Invert for m & m

Kirchhoff

Modeler

s

p

s

s

P-reflectivity

d = L m

p

p

S-Refl. Kirchhoff

Modeler

Filtering by LSMF

d

PP

Time

PS

Distance

slide16

d = L m +L m

s

s

1.

p

p

data

unknowns

2.

Find m by conjugate gradient

p

d = L m

3. Model Coherent Signal

p

p

LSMF Method

slide17

Outline

  • Radon Filtering Methods
  • ARCO Surface Wave Data
  • Saudi Land Data: Local Adapt.+Subt.
  • Multicomponent Data Example
  • Conclusion and Discussion
slide18

RAW DATA OF ARCO

X (kft)

1.8

3.6

0

Time (s)

2.5

Raw Data

slide19

X (kft)

X (kft)

FK

LSMF

ARCO DATA

1.8

3.6

1.8

3.6

0

A

A

Time (s)

B

B

2.5

slide20

ZOOM VIEW OF WINDOW “ A”

X (kft)

X (kft)

2.0

3.0

2.0

3.0

0.5

Time (s)

1.5

FK

LSMF

slide21

ZOOM VIEW OF WINDOW “ B”

X (kft)

X (kft)

2.0

3.45

2.0

3.45

1.5

Time (s)

2.5

FK

LSMF

slide22

Outline

  • Radon Filtering Methods
  • ARCO Surface Wave Data
  • Saudi Land Data: Local Adapt.+Subt.
  • Multicomponent Data Example
  • Conclusion and Discussion
slide23

Local tau-p

Aramco Saudi Land Data

0.0s

4.0s

slide24

S

N

+

Tau-p

~

~

S

N

+

-1

Tau-p

Adaptive Subtraction

=

S

N

N

S

+

-

slide25

INPUT LOCAL TAU-P

0.0s

Input After Noise Reduction

4.0s

(courtesy Yi Luo @ Aramco)

slide26

Input FK

Signal FK

F

F

K

K

slide27

Outline

  • Radon Filtering Methods
  • ARCO/Saudi Field Data Results
  • Multicomponent Data Example
  • Graben Example
  • Mahagony 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

PP1 Leak

PP1

PP2

PP2 Leak

PP3 Leak

PP3

PP4

PP4 Leak

Synthetic Data

Offset (m)

Offset (m)

5000

0

5000

0

0

Time (s)

1.4

Horizontal Component

Vertical Component

lsmf separation

PP1

PP2

PP3

PP4

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

PP1

PP2

PP3

PP4

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

PP1 Leak

PP1

PP2 Leak

PP2

PP3

PP3 Leak

PP4 Leak

PP4

F-K Filtering Separation

5000

0

Offset (m)

5000

0

Offset (m)

0

Time (s)

1.4

Horizontal Component

Vertical Component

slide33

Outline

  • Radon Filtering Methods
  • ARCO/Saudi Field Data Results
  • Multicomponent Data Example
  • Graben Example
  • Mahagony Field Data
  • Conclusion and Discussion
crg1 raw data

PS

PS

PS

CRG1 Raw Data

0

Time (s)

4

CRG1 (Vertical component)

crg1 data after using f k filtering

PS

PS

PS

CRG1 Data after Using F-K Filtering

0

Time (s)

4

CRG1 (Vertical component)

crg1 data after using lsmf

PS

PS

PS

CRG1 Data after Using LSMF

0

Time (s)

4

CRG1 (Vertical component)

slide37

Filtering signal/noise using: moveout

difference & particle velocity direction

Don’t use a shotgun to kill a fly

Conclusions

Local tau-p and adaptive subtraction

LSMF computes moveout and particle

velocity direction based on true physics.

slide38

Simple Filtering

YES

YES

YES

YES

Complex Filtering

No

YES/No

YES/no

YES

User Intervention

Mild

Yes

Yes

Yes

Cost

c

$

$

$$$$

Proven

YES

YES

YES

Yes/No

SUMMARY

FK

Linear

Tau-P

Parabolic

Tau-P

LSMF

slide39

SAUDI DATA

X(m)

88

2988

0

Time (s)

4.0

Raw Data

slide40

SAUDI DATA AFTER FK & LSMF

X(m)

X (m)

88

2988

88

2988

0

A

A

B

B

Time (s)

4.0

FK

LSMF

crg2 data after using lsmf vertical component
CRG2 Data after Using LSMF (vertical component)

0

Time (s)

4

CRG2 (Vertical component)

slide43

ZOOM VIEW OF WINDOW A

X (m)

X (m)

890

2088

890

2088

1.0

Time (s)

2.0

FK

LSMF

slide44

ZOOM VIEW OF WINDOW B

X (m)

X (m)

186

1189

186

1189

0.7

Time (s)

2.0

FK

LSMF

slide45

SAUDI DATA

X(m)

88

2089

0

Time (s)

4.0

Raw Data

slide46

SAUDI DATA AFTER FK & LSMF

X(m)

X (m)

88

2089

88

2089

0

A

A

B

B

Time (s)

4.0

FK

LSMF

slide47

ZOOM VIEW OF WINDOW “A”

X (m)

X (m)

327

1370

327

1370

0.6

Time (s)

2.0

FK

LSMF

slide48

ZOOM VIEW OF WINDOW “B”

X (m)

X (m)

186

621

186

621

0.4

Time (s)

1.4

FK

LSMF

slide49

Overview of SomeCoherent Noise Filtering Merthods

Overview

There are a number of different coherent noise filtering methods, including FK dip filter, Radon transform, hyperbolic transform, and parabolic transform methods. All of these methods rely upon transforming the signal into a new domain where the signal and noise are more separable. We will show that LSM filtering is another coherent filtering method, but is more precise in defining a transform that separates signal and coherent noise according to the physics of wave propagation. Examples show that this is sometimes a more effective ilter, but it is more costly.

slide50

Multicomponent Filtering by LSMF

PP

d = L m +L m

PS

p

p

x

s

s

d = L m +L m

p

p

z

s

s

PS

PP

Time

Z

Distance

slide53

-1

L

s

-1

L

p

Filtering by LSMF

PP

Time

Z

PS

X

Distance

M1

M2

crg2 raw data vertical component
CRG2 Raw Data (vertical component)

0

Time (s)

4

CRG2 (Vertical component)

slide55

Filtering by Parabolic  - p

B

Tau

Time

Signal/Noise

Overlap

A

V=1/P

Distance

slide56

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

slide57

Summary

Traditionalcoherent filtering based on

approximate moveout

LSMF filtering operators based on

actual physics separating signal & noise

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