Introduction to Multiple Attenuation Methodssep Predictive Deconvolution •Remove short-period multiples (most notably from relatively flat, shallow water-bottom) •The periodicity

Introduction to Multiple Attenuation Methods

GP 210 Basic Earth Imaging

Dec 5th, 2012

Prepared by Mandy Wong

  Introduction   Types of multiples

  Multiple removal methods 1.  Predictive Deconvolution 2.  Fk filtering 3.  Hyperbolic Radon filtering 4.  Surface-related multiple elimination (SRME)

  Summary

Overview

Introduction •  Multiples are seismic arrival that have more than one reflections or scattering •  There are many types of multiples with special names

(1) Source ghost

(2) Receiver ghost

(3) Mirror signal • A type of receiver ghost that involves ocean bottom receiver • It can easily be used as signal

(4) Surface-related multiples •  event with at least one reflection off the sea-surface

(4) Surface-related multiples •  event with at least one reflection off the sea-surface

(5) Water-column reverberations

• a class of surface-related multiples • Reflections only between the sea-surface and the seabed.

(6) Internal multiples •  event with no reflection off the sea-surface

(7) Peg-leg multiples •  A multiple reflection involving successive reflection at different interfaces so that its travel path is not symmetric

1.  Predictive Deconvolution 2.  F-k filtering 3.  Parabolic Radon Transform 4.  Surface-related multiple elimination

(SRME)

Multiple removal methods

Predictive Deconvolution • Remove short-period multiples (most notably from relatively flat, shallow water-bottom) • The periodicity of the multiples is exploited to design a filter that removes the predictable part of the wavelet (multiples), leaving only its non-predictable part (signal)

After prediction decon Zero-offset gather

Predictive Deconvolution

After prediction decon Zero-offset gather

To suppress multiples choose a lag corresponding to the two-way travel-time of the multiples

Predictive Deconvolution

Pros Cons

•  Computationally affordable •  Good for shallow water reverberation

•  1D model •  For dipping reflector, multiples are not periodic

Fk-filtering

CMP gather from Lab 5

Primaries and multiples exhibit different hyperbolic moveout in CMPs

Fk-filtering

CMP gather from Lab 5

Primaries and multiples exhibit different hyperbolic moveout in CMPs

Which one is primary? And multiple?

NMO with the Vrms of the primaries

Fk-filtering NMO with the Vrms between the

primaries and the multiples

Fk-filtering Up- and down-going event can be separated in the f-k domain

Fk-filtering

Pros Cons

•  Computationally affordable •  Good for simple subsurface

•  Fail at near offset •  Insufficient for complex subsurface •  Require velocity model

Hyperbolic Radon Transform

Radon Transform

Pros Cons

•  Computationally affordable •  Good for simple subsurface

• Insufficient for complex subsurface •  Require velocity model

Surface Related Multiple Elimination (SRME)

X1

X2


X1

X2

X1*X2 (convolution): predict multiples of path S1-R1-R2

What is Convolution?

Source:http://en.wikipedia.org/wiki/Cross-correlation


X1

X2

X1*X2 (convolution)

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X1

X2

X1*X2 (convolution)

-

t1

t1


X1

X2

X1*X2 (convolution)

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t2

t2


X1

X2

X1*X2 (convolution)

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+

+



A C

Verschuur (2009)

To estimate the multiples coming from point A to C, convolve the following •  common shot gather (shot at A) •  common receiver gather (receiver at B) And then sum all the contributions


Verschuur (2009)

To estimate the multiples coming from point A to C, convolve the following •  common shot gather (shot at A) •  common receiver gather (receiver at B) And then sum all the contributions

Surface Related Multiple Elimination (SRME) To estimate the multiples coming from point A to C, convolve the following •  common shot gather (shot at A) •  common receiver gather (receiver at B) And then sum all the contributions

Verschuur (2009)

Figure 5:Surface bounce lie outside of acquisition geometry.


Pros Cons

•  Require no subsurface info •  Can eliminate complex surface-related multiples

•  Require dense and regular acquisition geometry


Summary Methods Pros Cons Predictive Deconvolution

 Computationally affordable  Good for shallow water reverberation

 1D model  For dipping reflector, not periodic

F‐k Filtering  ‐Computationally affordable  ‐ Good for simple subsurface

 Fail at near offset  Insufficient for complex subsurface  Require velocity model

Radon Transform  ‐Computationally affordable  ‐ Good for simple subsurface

 Insufficient for complex subsurface  Require velocity model


 Require no subsurface info  Effectively converging

  Require dense and regular acquisition geometry.

References

Alvarez G F, Attenuation of Multiples in Image Space, PhD Thesis, Stanford University, 2007 (Figure 5)

Cao Z.H., Analysis and Application of Radon Transform, MSc Thesis, Univ of Calgary, 2006 (Figure 1, 4)

Peacock K L and Treitel S, Predictive Deconvolution: Theory and Practice, Geophysics, 34 (1969) (Figure 3)

Verschuur, D. J., Berkhout, A. J. and Wapenaar, C. P. A., Adaptive surface-related multiple elimination, Geophysics, 57, 1166-1177, 1992

Weglein A B, Multiple attenuation: an overview of recent advances and the road ahead, The Leading Edge, 18 (1999), 40-44

Introduction to Multiple Attenuation Methodssep Predictive Deconvolution •Remove short-period multiples (most notably from relatively flat, shallow water-bottom) •The periodicity

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