Review of Coherent Noise Suppression Methods Gerard T. Schuster University of Utah

Problem: Ground Roll Degrades Signal Offset (ft) 2000 3500 Reflections Time (sec) Ground Roll 2.5

Problem: PS Waves Degrade Signal Time (sec) Reflections Converted S Waves 4.0

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

Problem: Out-of-Plane Ground Roll

Outline Coherent Filtering Methods ARCO Field Data Results Multicomponent Data Example Conclusion and Discussion

Traditional Filtering Methods F-K Dip Filtering Filtering in  - p domain linear  - p parabolic  - p hyperbolic  - p Least Squares Migration Filter

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

Tau-P Transform Sum Transform Time Tau Distance P

Tau-P Transform Tau-P Transform Time Tau Distance P

Tau-P Transform Tau-P Transform Mute Noise Transform Time Tau Distance P

Separation Signal/Noise Tau-P Transform Problem: Indistinct Separation Signal/Noise Transform Time Tau Distance P

Hyperbolic Transform Tau-P Transform Time Tau Distinct Separation Signal/Noise Distance P

Breakdown of Hyperbolic Assumption * v v v v v v v v v Irregular Moveout B Time A Distance

Filtering by Parabolic - p Time Time Signal/Noise Overlap A Distance p

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

Filtering by LSMF L -1 s PP Time Z L -1 p PS Distance X M1 M2

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

Multicomponent Filtering by LSMF PS PP PS PP Time Z s d = L m + L m p x z Distance

Summary Traditional coherent filtering based on approximate moveout LSMF filtering operators based on actual physics separating signal & noise Better physics --> Better focusing, more $$$

Outline Coherent Filtering Methods ARCO Surface Wave Data Multicomponent Data Example Conclusion and Discussion

ARCO Field Data Offset (ft) 2000 3500 Time (sec) 2.5

LSM Filtered Data (V. Const.) ARCO Field Data LSM Filtered Data (V. Const.) Offset (ft) 2000 3500 Time (sec) 2.5

F-K Filtered Data (13333ft/s) LSM Filtered Data (V. Const.) Offset (ft) 2000 3500 Time (sec) 2.5

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

Outline Coherent Filtering Methods ARCO Field Data Results Multicomponent Data Example Graben Example Mahogony Example Conclusion and Discussion

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

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

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

True P-P and P-SV Reflection Offset (m) 5000 Offset (m) 5000 Time (s) 1.4 Horizontal Component Vertical Component

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

Outline Coherent Filtering Methods ARCO Field Data Results Multicomponent Data Example Graben Example Mahogony Field Data Conclusion and Discussion

CRG1 Raw Data PS Time (s) 4 CRG1 (Vertical component)

CRG1 Data after Using F-K Filtering PS Time (s) 4 CRG1 (Vertical component)

CRG1 Data after Using LSMF PS Time (s) 4 CRG1 (Vertical component)

CRG2 Raw Data (vertical component) Time (s) 4 CRG2 (Vertical component)

CRG2 Data after Using F-K Filtering (vertical component) Time (s) 4 CRG2 (Vertical component)

CRG2 Data after Using LSMF (vertical component) Time (s) 4 CRG2 (Vertical component)

Outline Coherent Filtering Methods ARCO Field Data Results Multicomponent Data Example Conclusion and Discussion

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.