Transcript No Slide Title

Overview of Some Coherent
Noise Filtering Methods
Jianhua Yue, Yue Wang, Gerard Schuster
University of Utah
Problem: Ground Roll Degrades Signal
2000
Offset (ft)
3500
0
Time (sec)
Reflections
2.5
Ground
Roll
Problem: PS Waves Degrade Signal
Time (sec)
0
4.0
PP Reflections
Converted S Waves
Problem: Tubes Waves Obscure PP
Time (sec)
2000
4.0
Depth (ft)
0
Reflections
Time
(s)
Reflections
Aliased tubeConverted
waves S Waves
0.14
3100
Outline
•
•
•
•
•
Radon Filtering Methods
ARCO Field Data Results
Saudi Land Data
Multicomponent Data Example
Conclusion and Discussion
Two Classes of Coherent
Noise Filtering
Model Noise and Adaptive Subtraction
Filter that Exploit Moveout Differences
Filtering Methods:
Moveout Separation
F-K Dip Filtering
Filtering in  - p domain
linear  - p
parabolic  - p
hyperbolic  - p
local+adaptive subtraction
Least Squares Migration Filter
Separation Principle: Exploit Differences in
Moveout & Part. Velocity Directions
Time
SIGNAL
Transform
NOISE
Distance
Frequency
SIGNAL
NOISE
Overlap
Signal & Noise
Wavenumber
Tau-P Transform
Transform
Distance
Tau
Time
Sum
V=1/P
Transform
Distance
Tau
Time
Tau-P Transform
V=1/P
Transform
Distance
Tau
Time
Tau-P Transform
Mute Noise
V=1/P
Transform
Tau
Time
Tau-P Transform
Problem: Indistinct
Separation Signal/Noise
Distance
V=1/P
Transform
Tau
Time
Hyperbolic
Transform
Tau-P Transform
Distinct Separation
Signal/Noise Hi res.
Distance
V=1/P
Breakdown of Hyperbolic
Assumption
Irregular Moveout
Time
*
v v v v v v v v v
Distance
Filtering by LSMF
Invert
for m p & m s
Kirchhoff
P-reflectivity
Modeler
Time
PP
d = Lp mp + L m
s
PS
Distance
s
S-Refl. Kirchhoff
Modeler
LSMF Method
1.
d=L m + L m
p
p
s
s
data
2.
unknowns
Find m by conjugate gradient
p
3. Model Coherent Signal
d = Lp mp
Outline
• Radon Filtering Methods
• ARCO Surface Wave Data
• Saudi Land Data: Local Adapt.+Subt.
• Multicomponent Data Example
• Conclusion and Discussion
RAW DATA OF ARCO
1.8
X (kft)
Time (s)
0
2.5
Raw Data
3.6
ARCO DATA
1.8
FK
3.6
1.8
LSMF
3.6
0
A
Time (s)
A
B
B
2.5
X (kft)
X (kft)
ZOOM VIEW OF WINDOW “ A”
2.0
X (kft)
3.0 2.0
X (kft)
Time (s)
0.5
1.5
FK
LSMF
3.0
ZOOM VIEW OF WINDOW “ B”
2.0
X (kft)
3.45 2.0
X (kft)
Time (s)
1.5
2.5
FK
LSMF
3.45
Outline
• Radon Filtering Methods
• ARCO Surface Wave Data
• Saudi Land Data: Local Adapt.+Subt.
• Multicomponent Data Example
• Conclusion and Discussion
Aramco Saudi Land Data
0.0s
Local tau-p
4.0s
N
S
+
Tau-p
~
N
-
S
+
Tau-p
Adaptive Subtraction
N
~
N
+
S
-1
=
S
0.0s
INPUT
Input
LOCAL TAU-P
After Noise Reduction
4.0s
(courtesy Yi Luo @ Aramco)
Input FK
Signal FK
F
F
K
K
Outline
• Radon Filtering Methods
• ARCO/Saudi Field Data Results
• Multicomponent Data Example
Graben Example
Mahagony Example
• Conclusion and Discussion
Graben Velocity Model
0
Depth (m)
0
X (m)
V1=2000 m/s
V2=2700 m/s
V3=3800 m/s
V4=4000 m/s
V5=4500 m/s
3000
5000
Synthetic Data
0
Time (s)
5000
0
0
Offset (m)
Offset (m)
PP1 Leak
PP1
PP2
Leak
PP2
PP3
Leak
PP4
Leak
5000
PP3
PP4
1.4
Horizontal Component
Vertical Component
LSMF Separation
5000
0
0
0
Offset (m)
5000
Offset (m)
PP1
Time (s)
PP2
PP3
PP4
1.4
Horizontal Component
Vertical Component
True P-P and P-SV Reflection
5000
0
0
0
Offset (m)
5000
Offset (m)
PP1
Time (s)
PP2
PP3
PP4
1.4
Horizontal Component
Vertical Component
F-K Filtering Separation
0
Time (s)
0
0
5000
Offset (m)
PP1 Leak
PP1
PP2
Leak
PP2
PP3
Leak
PP4
Leak
5000
Offset (m)
PP3
PP4
1.4
Horizontal Component
Vertical Component
Outline
• Radon Filtering Methods
• ARCO/Saudi Field Data Results
• Multicomponent Data Example
Graben Example
Mahagony Field Data
• Conclusion and Discussion
CRG1 Raw Data
Time (s)
0
PS
PS
4
CRG1 (Vertical component)
PS
CRG1 Data after Using F-K Filtering
Time (s)
0
PS
PS
4
CRG1 (Vertical component)
PS
CRG1 Data after Using LSMF
Time (s)
0
PS
PS
4
CRG1 (Vertical component)
PS
Conclusions
Filtering signal/noise using: moveout
difference & particle velocity direction
Don’t use a shotgun to kill a fly
Local tau-p and adaptive subtraction
LSMF computes moveout and particle
velocity direction based on true physics.
SUMMARY
FK
Simple Filtering
YES
Complex Filtering
No
User Intervention
Mild
Cost
Proven
Linear Parabolic LSMF
Tau-P
Tau-P
YES
YES
YES/No YES/no
YES
YES
Yes
Yes
Yes
c
$
$
$$$$
YES
YES
YES
Yes/No
SAUDI DATA
88
X(m)
Time (s)
0
4.0
Raw Data
2988
SAUDI DATA AFTER FK & LSMF
88
X(m)
2988 88
X (m)
2988
0
A
A
B
Time (s)
B
4.0
FK
LSMF
CRG2 Data after Using F-K Filtering (vertical component)
Time (s)
0
4
CRG2 (Vertical component)
CRG2 Data after Using LSMF (vertical component)
Time (s)
0
4
CRG2 (Vertical component)
ZOOM VIEW OF WINDOW A
890
X (m)
2088 890
X (m)
Time (s)
1.0
2.0
FK
LSMF
2088
ZOOM VIEW OF WINDOW B
186
X (m)
1189 186
X (m)
Time (s)
0.7
2.0
FK
LSMF
1189
SAUDI DATA
88
X(m)
Time (s)
0
4.0
Raw Data
2089
SAUDI DATA AFTER FK & LSMF
88
X(m)
2089 88
X (m)
0
A
B
Time (s)
B
A
4.0
FK
LSMF
2089
ZOOM VIEW OF WINDOW “A”
327
X (m)
1370 327
X (m)
Time (s)
0.6
2.0
FK
LSMF
1370
ZOOM VIEW OF WINDOW “B”
186
X (m)
621
186
X (m)
Time (s)
0.4
1.4
FK
LSMF
621
Overview of Some Coherent 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.
Multicomponent Filtering by LSMF
PS
PP
Time
PP
Z
PS
Distance
dx = L pmp + L m
s
d z = L pmp + L m
s
s
s
Signal FK
Problem: Out-of-Plane Ground Roll
Ground Roll
Filtering by LSMF
-1
Time
PP
Ls
Z
-1
Lp
PS
Distance
M21
X
CRG2 Raw Data (vertical component)
Time (s)
0
4
CRG2 (Vertical component)
Time
B
A
Distance
Tau
Filtering by Parabolic  - p
Signal/Noise
Overlap
V=1/P
Frequency (Hz)
0
50
Spectrum
of
ARCO
Data
S.F-X
of S.
LSM
Filtered
Data
(V.
of F-K Filtered DataConst)
Offset (ft)
2000
3500
(13333ft/s)
Summary
Traditional coherent filtering based on
approximate moveout
LSMF filtering operators based on
actual physics separating signal & noise
Better physics --> Better focusing, more $$$