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Review of Coherent Noise Suppression Methods
Gerard T. Schuster University of Utah
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Problem: Ground Roll Degrades Signal
Offset (ft) 2000 3500 Reflections Time (sec) Ground Roll 2.5
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Problem: PS Waves Degrade Signal
Time (sec) Reflections Converted S Waves 4.0
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Problem: Tubes Waves Obscure PP
2000 Depth (ft) 3100 Reflections Time (sec) Reflections Time (s) Aliased tube waves Converted S Waves 0.14 4.0
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Problem: Out-of-Plane Ground Roll
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Outline Coherent Filtering Methods ARCO Field Data Results
Multicomponent Data Example Conclusion and Discussion
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Traditional Filtering Methods
F-K Dip Filtering Filtering in - p domain linear - p parabolic - p hyperbolic - p Least Squares Migration Filter
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SIGNAL SIGNAL NOISE NOISE Separation Principle: Exploit Differences in
Moveout & Part. Velocity Directions SIGNAL Overlap Signal & Noise SIGNAL NOISE Transform Time Frequency NOISE Distance Wavenumber
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Tau-P Transform Sum Transform Time Tau Distance P
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Tau-P Transform Tau-P Transform
Time Tau Distance P
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Tau-P Transform Tau-P Transform
Mute Noise Transform Time Tau Distance P
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Separation Signal/Noise
Tau-P Transform Problem: Indistinct Separation Signal/Noise Transform Time Tau Distance P
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Hyperbolic Transform Tau-P Transform
Time Tau Distinct Separation Signal/Noise Distance P
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Breakdown of Hyperbolic
Assumption * v v v v v v v v v Irregular Moveout B Time A Distance
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Filtering by Parabolic - p
Time Time Signal/Noise Overlap A Distance p
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d = L m d d = L m + L m Filtering by LSMF Invert for m & m s Kirchhoff
p s Kirchhoff Modeler P-reflectivity d = L m p d d = L m + L m s PP Time PS Distance
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Filtering by LSMF L -1 s PP Time Z L -1 p PS Distance X M1 M2
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Find m by conjugate gradient
LSMF Method d = L m + L m s p 1. data unknowns 2. Find m by conjugate gradient p d = L m p 3. Model Coherent Signal
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Multicomponent Filtering by LSMF
PS PP PS PP Time Z s d = L m + L m p x z Distance
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Summary Traditional coherent filtering based on approximate moveout
LSMF filtering operators based on actual physics separating signal & noise Better physics --> Better focusing, more $$$
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Outline Coherent Filtering Methods ARCO Surface Wave Data
Multicomponent Data Example Conclusion and Discussion
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ARCO Field Data Offset (ft) 2000 3500 Time (sec) 2.5
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LSM Filtered Data (V. Const.)
ARCO Field Data LSM Filtered Data (V. Const.) Offset (ft) 2000 3500 Time (sec) 2.5
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F-K Filtered Data (13333ft/s)
LSM Filtered Data (V. Const.) Offset (ft) 2000 3500 Time (sec) 2.5
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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 Frequency (Hz) 50
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Outline Coherent Filtering Methods ARCO Field Data Results
Multicomponent Data Example Graben Example Mahogony Example Conclusion and Discussion
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Graben Velocity Model X (m) 5000 V1=2000 m/s V2=2700 m/s V3=3800 m/s
5000 V1=2000 m/s V2=2700 m/s V3=3800 m/s Depth (m) V4=4000 m/s V5=4500 m/s 3000
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Synthetic Data Horizontal Component Vertical Component Offset (m)
5000 5000 PP1 PP2 PP3 PP4 PP1 PP2 PP3 PP4 Time (s) 1.4 Horizontal Component Vertical Component
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LSMF Separation Horizontal Component Vertical Component Time (s)
Offset (m) 5000 Offset (m) 5000 PP1 PP2 PP3 PP4 Time (s) 1.4 Horizontal Component Vertical Component
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True P-P and P-SV Reflection
Offset (m) 5000 Offset (m) 5000 Time (s) 1.4 Horizontal Component Vertical Component
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F-K Filtering Separation
Offset (m) 5000 Offset (m) 5000 PP1 PP2 PP3 PP4 PP1 PP2 PP3 PP4 Time (s) 1.4 Horizontal Component Vertical Component
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Outline Coherent Filtering Methods ARCO Field Data Results
Multicomponent Data Example Graben Example Mahogony Field Data Conclusion and Discussion
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CRG1 Raw Data PS Time (s) 4 CRG1 (Vertical component)
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CRG1 Data after Using F-K Filtering
PS Time (s) 4 CRG1 (Vertical component)
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CRG1 Data after Using LSMF
PS Time (s) 4 CRG1 (Vertical component)
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CRG2 Raw Data (vertical component)
Time (s) 4 CRG2 (Vertical component)
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CRG2 Data after Using F-K Filtering (vertical component)
Time (s) 4 CRG2 (Vertical component)
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CRG2 Data after Using LSMF (vertical component)
Time (s) 4 CRG2 (Vertical component)
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Outline Coherent Filtering Methods ARCO Field Data Results
Multicomponent Data Example Conclusion and Discussion
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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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