1. LEAST SQUARES DATUMING AND SURFACE WAVES PREDICTION WITH INTERFEROMETRY Yanwei Xue Department of Geology & Geophysics University of Utah 1

2. OUTLINE VSP to SWP VSP surface related multiple to SSP Surface waves prediction and subtraction Summary 2

3. OUTLINE VSP to SWP Motivation Theory Numerical Results Conclusion 3

4. Why least squares datuming? Ideal case for datumimg Single ray-path 4

5. Why least squares datuming? Real case for datumimg Multi ray-path 5

6. OUTLINE VSP to SWP Motivation Theory Numerical Results Conclusion 6

7. 7 Two state model State 1State 2 S S1S1 S0S0 8 B x S1S1 S 8 x A

8. 8 State 1State 2 S1S1 S 8 x A S1S1 S0S0 8 B x

9. 9 State 1State 2 S1S1 S 8 x A S1S1 S0S0 8 B x

10. OUTLINE VSP to SWP Motivation Theory Numerical Results Conclusion 10

11. 11 Velocity Model Sources at surface: [-1500:15:1500] Receiver at datum: [0] v=1500 m/s, d=500 m v=2000 m/s, d=800 m v=2500 m/s, d=1200 m v=3000 m/s

12. Interferometric datuming with direct waves 0 2.0 -2.02.0 Time (s) Offset (Km) S.I 0 2.0 -2.02.0 Time (s) Offset (Km) L.S.I 0 2.0 -2.02.0 Time (s) Offset (Km) Ideal 12

13. Interferometric datuming with down-going waves 0 2.0 -2.02.0 Time (s) Offset (Km) S.I 0 2.0 -2.02.0 Time (s) Offset (Km) L.S.I 0 2.0 -2.02.0 Time (s) Offset (Km) Ideal 13

14. Limited acquisition aperture problem 14

15. L.S. Interferometric datuming with direct waves 0 5.2 -4.04.0 Time (s) Offset (Km) 15

16. L.S. Interferometric datuming with down-going waves 0 5.2 -4.04.0 Time (s) Offset (Km) 16

17. Difference between full&partial G’s function 0 5.2 -4.04.0 Time (s) Offset (Km) 17

18. OUTLINE VSP to SWP Motivation Theory Numerical Results Conclusion 18

19. Conclusion Least squares interferometric datuming with up-down going wavefields separation can provides datumed results without reflection from the structures above the datum line. Factors affect results: –Quality of up-down going separation; –Quality of acquisition geometry. 19

20. OUTLINE VSP to SWP VSP surface related multiple to SSP Surface waves prediction and subtraction Summary 20

21. OUTLINE VSP to SSP Motivation Theory Numerical Results Synthetic data test Field data test Conclusion 21

22. Shot radius Z 3D VSP Survey 22

23. OUTLINE VSP to SSP Motivation Theory Numerical Results Synthetic data test Field data test Conclusion 23

24. 24 Two state model State 1State 2 S0S0 S1S1 S1S1 S 8 S 8 A xx B

25. 25 State 1State 2 S0S0 S1S1 S1S1 S 8 S 8 A xx B

26. 26 State 1State 2 S0S0 S1S1 S1S1 S 8 S 8 A xx B

27. 27 Downgoing Multiples sx1x1 x2x2 R1R1 R2R2 s x1x1 x2x2 R1R1 R2R2 d1d1 d2d2 h

28. 28 Upgoing Multiples 28 sx1x1 x2x2 R1R1 R2R2 s x1x1 x2x2 R1R1 R2R2

29. 29 Comments  Increase order of multiples, illumination area goes away from the well  The deeper the reflectors, the narrower the illumination area. The illumination area goes closer to the well.

30. OUTLINE VSP to SSP Motivation Theory Numerical Results Synthetic data test Virtual source close to well. Virtual source far from well. Field data test Conclusion 30

31. 31 0 4000 0 3-layer Velocity Model

32. 32 Real SSPSI SSP 0 10 0 350 Time (s) Offset (Km) 0 10 0 350 Time (s) Offset (Km) Primaries Artifacts

33. 33 Real SSPLSI SSP of Downgoing 0 10 0 350 Time (s) Offset (Km) 0 10 0 350 Time (s) Offset (Km) Primaries

34. 34 Real SSPLSI SSP of Upgoing 0 10 0 350 Time (s) Offset (Km) 0 10 0 350 Time (s) Offset (Km) Primaries

35. 35 Real SSPCorrected LSI SSP 0 10 0 350 Time (s) Offset (Km) 0 10 0 350 Time (s) Offset (Km) Primaries

36. primaries multiples 36

37. primaries multiples 37

38. primaries multiples 38

39. OUTLINE VSP to SSP Motivation Theory Numerical Results Synthetic data test Virtual source close to well. Virtual source far from well. Field data test Conclusion 39

40. 40 Real SSPSI SSP 0 10 0 750 Time (s) Offset (Km) 0 10 0 750 Time (s) Offset (Km) Primaries Artifacts

41. 41 Real SSPLSI SSP of Downgoing 0 10 0 750 Time (s) Offset (Km) 0 10 0 750 Time (s) Offset (Km) Primaries

42. 42 Real SSPLSI SSP of Upgoing 0 10 0 750 Time (s) Offset (Km) 0 10 0 750 Time (s) Offset (Km) Primaries

43. 43 Real SSPCorrected LSI SSP 0 10 0 750 Time (s) Offset (Km) 0 10 0 750 Time (s) Offset (Km) Primaries

44. Left side primaries multiples 44

45. Left side primaries multiples 45

46. Left side primaries multiples 46

47. Middle primaries multiples 47

48. Middle primaries multiples 48

49. Middle primaries multiples 49

50. Right side primaries multiples 50

51. Right side primaries multiples 51

52. Right side primaries multiples 52

53. OUTLINE VSP to SSP Motivation Theory Numerical Results Synthetic data test Virtual source close to well. Virtual source far from well. Field data test Conclusion 53

54. Standard Interferometric SSP 0 0.6 5500 Time (s) offset (ft) S.I (Down) 0 0.6 5500 Time (s) Offset (ft) S.I (up) 0 0.6 6000 Time (s) Offset (ft) Reference 54

55. L.S. Interferometric SSP 0 0.6 5500 Time (s) Offset (ft) L.S.I (Down) 0 0.6 5500 Time (s) Offset (ft) L.S.I (up) 0 0.6 6000 Time (s) Offset (ft) Reference 55

56. Real SSPCorrected LSI SSP 0 0.6 550 0 Time (s) Offset (ft) 0 0.6 600 0 Time (s) Offset (ft) 56

57. OUTLINE VSP to SSP Motivation Theory Numerical Results Conclusion 57

58. Conclusion Least squares interferometric VSP to SSP transform can attenuate the surface related multiples and crosstalk artifacts. A matching filter correction can attenuate more artifacts caused by limited acquisition geometry. Problem: –Non-surface related multiples may cause artifacts. 58

59. OUTLINE VSP to SWP VSP surface related multiple to SSP Surface waves prediction and subtraction Summary 59

60. OUTLINE Surface Wave Prediction 2D problem 3D problem 60

61. OUTLINE Surface Wave Prediction (2D) Motivation Methodology Field data Conclusion 61

62. Problem: Surface waves blur the seismogram. d 0 2.0 7200 Time (s) Receiver (m) A seismogram with surface waves and reflection data = d surf Surface waves +d ref Reflection waves 62

63. Solution: Filter the surface waves by NLF and Interferometric method 63

64. OUTLINE Surface Wave Prediction (2D) Motivation Methodology Field data test Conclusion 64

65. Prediction of Surface Waves Mid-Offset Surf. Wave Near-Offset Surf. Wave 65

66. The work flow Filter the surface waves by NLF Predict the residual and primaries by interferometry Predict the surface waves by NLF or wavelet transform Surface waves are removed completely? Least square subtraction Output data No Input data d Yes Lowpass filter 66

67. OUTLINE Surface Wave Prediction (2D) Motivation Methodology Field data test Conclusion 67

68. 0 2.0 0 3600 Time (s) Receiver (m) d 0 2.0 0 3600 Time (s) Receiver (m) The original data from Saudi Remove surface waves only by NLF 68

69. 0 2.0 0 3600 Time (s) Receiver (m) 0 2.0 0 3600 Time (s) Receiver (m) Remove surface waves only by NLF Remove surface waves Int.+NLF 69

70. 0 2.0 0 3600 Time (s) Receiver (m) d02.0 0 3600 Time (s) Receiver (m) The original data from Saudi Remove surface waves Int.+NLF 70

71. 0 2.0 0 3600 Time (s) Receiver (m) 0 2.0 0 3600 Time (s) Receiver (m) Surface waves predicted by NLF Remaining SW predicted by Int.+NLF 71

72. 0 2.0 0 3600 Time (s) Receiver (m) 0 2.0 0 3600 Time (s) Receiver (m) Remove surface waves by F-K Remove surface waves by Int.+NLF 72

73. 0 2.0 0 3600 Time (s) Receiver (m) 0 2.0 0 3600 Time (s) Receiver (m) Surface waves predicted by F-K Surface waves predicted by Int.+NLF 73

74. OUTLINE Surface Wave Prediction (2D) Motivation Methodology Field data test Conclusion 74

75. The NLF+interferometry method can remove the surface waves successfully. The NLF+interferometry method can keep more details than the fk method. The NLF+interferometry method can remove surface waves with very low energy. 75

76. OUTLINE Surface Wave Prediction 2D problem 3D problem 76

77. OUTLINE Surface Wave Prediction (3D) Motivation Methodology Field data test Conclusion 77

78. Problem: No perfect geometry for 3D interferometry Solution: Convert the 3D problem to 2D problem 78

79. 0 4.0 Time (s) 5000 Receiver (m) 0 A 3D seismic data with surface waves Nonlinear moveout Highly aliased 79

80. OUTLINE Surface Wave Prediction (3D) Motivation Methodology Field data test Conclusion 80

81. d x Sqrt(d^2+x^2) Shift(x)={Sqrt(d^2+x^2)-x}/v nonlinear moveoutlinear moveout time shift 81

82. Original data Time (s) receiver (m) surface wave Shifted data Time (s) receiver (m) Time Shift Linearized moveout Predicted surface waves Time (s) receiver (m) NLF+Interferometry predicted surface wave Predicted reflections Time (s) receiver (m) Match filter Predicted reflections 82

83. The work flow Predict the residual and primaries by interferometry Predict the surface waves by NLF or wavelet transform Surface waves are removed completely? Least square subtraction No Filter the surface waves by NLF Time shift d to make the surface waves linear moveout Input data d Output data Yes Lowpass filter 83

84. OUTLINE Surface Wave Prediction (3D) Motivation Methodology Field data test Conclusion 84

85. Line8 before and after removing surface waves 0 4.0 Time (s) 0 5000Receiver (m) 0 4.0 Time (s) 0 5000Receiver (m) 85

86. Line9 before and after removing surface waves 0 4.0 Time (s) 0 5000Receiver (m) 0 4.0 Time (s) 0 5000Receiver (m) 86

87. Line10 before and after removing surface waves 0 4.0 Time (s) 0 5000Receiver (m) 0 4.0 Time (s) 0 5000Receiver (m) 87

88. Line11 before and after removing surface waves 0 4.0 Time (s) 0 5000Receiver (m) 0 4.0 Time (s) 0 5000Receiver (m) 88

89. Line12 before and after removing surface waves 0 4.0 Time (s) 0 5000Receiver (m) 0 4.0 Time (s) 0 5000Receiver (m) 89

90. OUTLINE Surface Wave Prediction (3D) Motivation Methodology Field data test Conclusion 90

91. The nonlinear time shift can make a nonlinear event a linear event.The nonlinear time shift can make a nonlinear event a linear event. The linear shift can weaken the aliasing problemThe linear shift can weaken the aliasing problem After the nolinear and linear shift, the 2D surface waves elimination technique can be apply on the 3D data.After the nolinear and linear shift, the 2D surface waves elimination technique can be apply on the 3D data. This technique solves 3D interferometry geometry problem.This technique solves 3D interferometry geometry problem. 91

92. problems The angle between the signal events and the noise event are too small. It will be not easy for the nonlinear local filter to choose the noise from signal Both the signal events and the noise event are nearly linear. They share the same stationary phase points and this leads to a low contrast of signal/noise in the interferometry and make it difficult to separate the noise from the data 92

93. OUTLINE VSP to SWP VSP surface related multiple to SSP Surface waves prediction and subtraction Summary 93

94. Summary Wavefield decomposition improves the interferometric results. Least squares interferometric scheme can attenuate surface related multiples and crosstalk artifacts. Matching filter help to improve interferometric prediction results 94

95. Future Work Apply LSD to target oriented RTM Up-down going wavefields separation for complicated medium 3D data test on least squares interferometric techniques 95

96. Acknowledgements My supervisory committee: Ronanld L. Bruhn, Hugues Djikpesse, Richard D. Jarrard, and Michael S. Thorne My wife Jing and my Son Daniel My advisor: Gerard T. Schuster My UTAM colleagues and my other friends 96

97. Thank you 97