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

Problem: Ground Roll Degrades Signal Offset (ft) Time (sec) Reflections GroundRoll

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

Time (sec) 4.0 Reflections Converted S Waves 3100 Depth (ft) Time(s) 0.14 Problem: Tubes Waves Obscure PP Reflections Aliased tube waves

Problem: Dune Waves Obscure PP Dune Waves

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

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

Distance Time NOISE SIGNAL Wavenumber Frequency Separation Principle: Exploit Differences in Separation Principle: Exploit Differences in Moveout & Part. Velocity Directions SIGNAL NOISE Transform Overlap Signal & Noise

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

Distance Time Transform P Tau

Distance Time Transform P Tau Mute Noise

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

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

Distance Time Breakdown of Hyperbolic Assumptionvvvvvvvvv * A B Irregular Moveout

Distance Time A B p Time Filtering by Parabolic  - p Signal/NoiseOverlap

Distance Time PP Filtering by LSMF PS d = L m pp d = L m + L m ss sP-reflectivity KirchhoffModeler Invert for m & m p s

Distance Time PS PP Filtering by LSMF M1M1M1M1 M2M2M2M2 Z X Lp Ls

Distance Time PS PP Z ss d = L m + L m pp x ss M1M1M1M1 M2M2M2M2 X pp z

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

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

ARCO Field Data Offset (ft) Time (sec)

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

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

F-X Spectrum of ARCO Data Offset (ft) Frequency (Hz) S. of LSM Filtered Data (V. Const) S. of F-K Filtered Data (13333ft/s)

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

Graben Velocity Model Depth (m) X (m) V1=2000 m/s V2=2700 m/s V3=3800 m/s V4=4000 m/s V5=4500 m/s

Synthetic Data Time (s) 0 Offset (m) Offset (m) 5000 Horizontal Component Vertical Component PP1 PP2 PP3 PP4

LSMF Separation Time (s) 0 Offset (m) Offset (m) 5000 Horizontal Component Vertical Component

True P-P and P-SV Reflection Time (s) 0 Offset (m) Offset (m) 5000 Horizontal Component Vertical Component

F-K Filtering Separation Time (s) 0 Offset (m) Offset (m) 5000 Horizontal Component Vertical Component PP1 PP2 PP3 PP4

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

CRG1 (Vertical component) Time (s) 0 4 CRG1 Data after Using F-K Filtering

CRG1 Raw Data CRG1 (Vertical component) Time (s) 0 4

CRG1 (Vertical component) Time (s) 0 4 CRG1 Data after Using LSMF

CRG2 (Vertical component) Time (s) 0 4 CRG2 Data after Using F-K Filtering (vertical component)

CRG2 (Vertical component) Time (s) 0 4 CRG2 Raw Data (vertical component)

CRG2 (Vertical component) Time (s) 0 4 CRG2 Data after Using LSMF (vertical component)

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

Filtering signal/noise using: moveout Filtering signal/noise using: moveout difference & particle velocity direction difference & particle velocity direction - Traditional filtering $ vs $$$$ LSMF LSMF computes moveout and particle LSMF computes moveout and particle velocity direction based on true physics. velocity direction based on true physics. Conclusions