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Multiple attenuation in the image space Paul Sava & Antoine Guitton Stanford University SEP.

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Presentation on theme: "Multiple attenuation in the image space Paul Sava & Antoine Guitton Stanford University SEP."— Presentation transcript:

1 Multiple attenuation in the image space Paul Sava & Antoine Guitton Stanford University SEP

2 Goal Method feasible in 3-D Less expensive Dense data requirement Exploit the data/imaging mismatch Data: two-way propagation Migration: one-way extrapolation

3 Key technology Migration by wavefield extrapolation (WEM) Angle-domain common-image gathers High resolution Radon Transforms

4 The big picture WE prediction S/N separation Data RT & Mute Image Data NMO RT & Mute WE migration & ADCIG Image

5 Multiple attenuation by RTs –Moveout analysis NMO –Moveout analysis WE migration –S/N separation RT + Mute –S/N separation RT + Mute

6 3-D depth imaging WE migration –Multi-arrival Angle-gathers –Single-valued Kirchhoff migration –Single-arrival Offset-gathers –Multi-valued Biondi et al. (2003) Stolk & Symes (2002)   z x y

7 Synthetic example: data vs. image CMP CIG

8 Which Radon transform? Generic Radon Transform Parabolic Biondi & Symes (2003) Tangent q g(  )  z

9 Synthetic example: RTs Tangent Parabolic

10 Synthetic example: S/N separation primaries & multiples primariesmultiplesART + muteART

11 BP synthetic example

12 primaries & multiples primariesmultiplesART BP synthetic example

13 primaries & multiples primariesmultiples BP synthetic example: stacks

14 GOM example

15 primaries & multiples primariesmultiplesART + muteART GOM example: CIG 1

16 GOM example

17 primaries & multiples primariesmultiplesART + muteART GOM example: CIG 2

18 GOM example

19 GOM example: zoom 1 primaries & multiples

20 GOM example: zoom 1 primaries

21 GOM example: zoom 1 primaries & multiples

22 GOM example: zoom 1 multiples

23 GOM example

24 GOM example: zoom 2 primaries & multiples

25 GOM example: zoom 2 primaries

26 GOM example: zoom 2 primaries & multiples

27 GOM example: zoom 2 multiples

28 RT comparison Image space RTData space RT

29 Discussion PROs –Cheap & robust –3-D –Simple primaries –Migration artifacts CONs –Velocity model? –Moveout function? –Interactive mute –Inner angles –RT artifacts

30 Summary WE prediction S/N separation Data RT & Mute Image Data NMO RT & Mute WE migration & ADCIG Image

31 Summary Multiple attenuation after migration WE migration Angle gathers Cost/accuracy Complex propagation Cheap separation RT limitations filtering approach


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