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Construction of a Q-compensation operator directly from measured seismic data

Construction of a Q-compensation operator directly from measured seismic data. Kristopher Innanen ** and Arthur Weglein * * University of Houston ** University of Houston & University of British Columbia. M-OSRP Annual Meeting 21 April, 2005 University of Houston. Acknowledgments.

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Construction of a Q-compensation operator directly from measured seismic data

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  1. Construction of a Q-compensation operator directly from measured seismic data Kristopher Innanen** and Arthur Weglein* *University of Houston **University of Houston & University of British Columbia M-OSRP Annual Meeting 21 April, 2005 University of Houston

  2. Acknowledgments Tad Ulrych M-OSRP sponsors and personnel CDSST sponsors and personnel

  3. Overview Review of recent results, and a conjecture about attenuation in the inverse scattering series What does a Q-compensation operator look like? Generalizing the scope of the ISS non-linear imaging mechanisms The form and behavior of a non-linear data driven Q-compensation operator (with simple examples)

  4. Previous results & conjectures 2001—2002 Forward scattering series for attenuative media 2003—2004 Linear inversion for absorptive- dispersive medium parameters “…construction of an appropriate linear inverse, and use of the imaging mechanisms of the ISS, will lead to Q-compensation in the absence of an a priori Q estimate.”

  5. What does a Q-compensation operator look like? Q-compensation (with known Q) belongs to the class of ill-conditioned deconvolution problems.

  6. What does a Q-compensation operator look like? Q-compensation (with known Q) belongs to the class of ill-conditioned deconvolution problems. …i.e., in the conjugate domain we apply the operator 1/MT(k).

  7. What does a Q-compensation operator look like? Operators of this type: 1. Preferentially magnify large k signal components 2. Are often regularized, or stabilized via some approximation 3. Usually contain the relevant medium parameters…

  8. What does a Q-compensation operator look like? Summary: 1. Any/all operators that purport to correct for absorption effects of Q must have this form or similar. 2. Therefore “be on the look-out” for such a form in our current developments.

  9. Review The linear inverse. 1. We have considered 1D normal incidence, pre-stack, and both 1- and 2-parameter linear absorptive- dispersive inversion 2. We have reconstructed (linear) complex impedance contrasts by detecting the phase/amplitude imprint of complex reflection coefficients in measured wave field

  10. Review The linear inverse. 3. All schemes produced complex estimates of spatial distributions of the linear component of the A/D medium. We are particularly interested in the effect on the non-linear terms of the ISS of this new aspect of V1

  11. Review The linear inverse. So, construct the simplest possible of these and investigate the consequent behavior…

  12. Review The linear inverse. So, construct the simplest possible of these and investigate the consequent behavior… 1D causal Green’s fn. for homogeneous non-attenuating reference medium Incident acoustic plane wave

  13. Review The linear inverse. So, construct the simplest possible of these and investigate the consequent behavior… The linear component of the medium parameters, expressed as a perturbation away from a very simple acoustic reference. Data bears the effects of attenuation-dispersion in wave propagation and in interaction with complex impedance contrasts

  14. Review The linear inverse. Key point: linear estimation problem must be posed to interpret phase/amplitude data effects as being due to complex medium contrasts (Innanen and Weglein, 2004).

  15. Review The linear inverse. Key point: linear estimation problem must be posed to interpret phase/amplitude data effects as being due to complex medium contrasts (Innanen and Weglein, 2004).

  16. Review The leading order imaging subseries. Incorporate a large subset of the terms in the ISS that are ONLY concerned with correcting the spatial structure of the medium (misplaced by the linear inverse which contains an inaccurate reference medium).

  17. Review The leading order imaging subseries. In 1D the linear component of the velocity perturbation is computed directly from the data. The ISS may be manipulated so that the order-by-order reconstruction, cast in terms of 1(z),

  18. Review The leading order imaging subseries. …involves a subset of the form where (Shaw, 2005)

  19. Review The leading order imaging subseries. …involves a subset of the form where (Shaw, 2005) We will be particularly interested in this representation…

  20. Construction of a Q-compensation operator This portion of the 1D ISS can be exactly reproduced in the case of the absorptive linear inverse for media with contrasts in Q only, i.e., with a complex linear input: Some points: 1. As in the case of LOIS, we are building an operator, to act on 1 at the output point z, that cares about what 1 itself is at all points above (0—z).

  21. Construction of a Q-compensation operator This portion of the 1D ISS can be exactly reproduced in the case of the absorptive linear inverse for media with contrasts in Q only, i.e., with a complex linear input: Some points: 2. With a complex 1, part of this operator will have the form of the 1/MT(k) that we have discussed.

  22. Construction of a Q-compensation operator This portion of the 1D ISS can be exactly reproduced in the case of the absorptive linear inverse for media with contrasts in Q only, i.e., with a complex linear input: Some points: 2. With a complex 1, part of this operator will have the form of the 1/MT(k) that we have discussed.

  23. Construction of a Q-compensation operator This portion of the 1D ISS can be exactly reproduced in the case of the absorptive linear inverse for media with contrasts in Q only, i.e., with a complex linear input: Some points: 3. This is a non-linear operator that is constructed directly from the measured data (via 1).

  24. An illustrative example Consider a gradual progression from data through this “algorithm”: discover its character and capability.

  25. An illustrative example Consider a gradual progression from data through this “algorithm”: discover its character and capability.

  26. An illustrative example Consider a gradual progression from data through this “algorithm”: discover its character and capability. MODEL & DATA:

  27. An illustrative example Consider a gradual progression from data through this “algorithm”: discover its character and capability. MODEL & DATA: Smoothed, attenuated signal for all portions of the wave field that have propagated within the Q   part of the medium…

  28. An illustrative example Consider a gradual progression from data through this “algorithm”: discover its character and capability. LINEAR INVERSE:

  29. An illustrative example Consider a gradual progression from data through this “algorithm”: discover its character and capability. LINEAR INVERSE: Non-linear reconstruction of  with sharp boundaries from this smooth 1 involves a flavor of Q-compensation. And we think we know where this resides…

  30. An illustrative example Non-linear operator in k domain… …derived from the cumulative effect of 1(z), z <|

  31. An illustrative example Non-linear operator in k domain… …derived from the cumulative effect of 1(z), z <|

  32. An illustrative example Non-linear operator in k domain… …derived from the cumulative effect of 1(z), z <|

  33. An illustrative example Non-linear operator in k domain… …derived from the cumulative effect of 1(z), z <|

  34. An illustrative example Non-linear operator in k domain… …derived from the cumulative effect of 1(z), z <|

  35. An illustrative example Non-linear operator in k domain… …derived from the cumulative effect of 1(z), z <|

  36. An illustrative example Non-linear operator in k domain… …derived from the cumulative effect of 1(z), z <|

  37. An illustrative example Non-linear operator in k domain… …derived from the cumulative effect of 1(z), z <|

  38. An illustrative example Non-linear operator in k domain… …derived from the cumulative effect of 1(z), z <|

  39. An illustrative example Non-linear operator in k domain… …derived from the cumulative effect of 1(z), z <|

  40. An illustrative example Non-linear operator in k domain… …derived from the cumulative effect of 1(z), z <|

  41. An illustrative example Non-linear operator in k domain… …derived from the cumulative effect of 1(z), z <|

  42. An illustrative example Non-linear operator in k domain… …derived from the cumulative effect of 1(z), z <|

  43. An illustrative example Non-linear operator in k domain… …derived from the cumulative effect of 1(z), z <|

  44. An illustrative example Non-linear operator in k domain… …derived from the cumulative effect of 1(z), z <|

  45. An illustrative example Non-linear operator in k domain… …derived from the cumulative effect of 1(z), z <|

  46. An illustrative example Non-linear operator in k domain… …derived from the cumulative effect of 1(z), z <|

  47. An illustrative example Non-linear operator in k domain… …derived from the cumulative effect of 1(z), z <|

  48. An illustrative example Non-linear operator in k domain… …derived from the cumulative effect of 1(z), z <|

  49. An illustrative example Non-linear operator in k domain… …derived from the cumulative effect of 1(z), z <|

  50. An illustrative example Non-linear operator in k domain… …derived from the cumulative effect of 1(z), z <|

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