3D magnetic inversion by planting anomalous densities

Transcript

1. Leonardo Uieda Valéria C. F. Barbosa Observatório Nacional - Brazil

   3D magnetic inversion by planting anomalous densities 2013 AGU Meeting of the Americas

2. Leonardo Uieda Valéria C. F. Barbosa Observatório Nacional - Brazil

   3D magnetic inversion by planting anomalous densities 2013 AGU Meeting of the Americas

3. Leonardo Uieda Valéria C. F. Barbosa Observatório Nacional - Brazil

   3D magnetic inversion by planting anomalous magnetization 2013 AGU Meeting of the Americas

4. (Short) History of planting inversion • Uieda and Barbosa (early

   2012) based on René (1986) • For gravity and gradients • Deal with computational difficulties – A lot of data – Large meshes • A way to input geologic/geophysical information • Improvements at SEG 2012

5. In a nutshell the data

6. In a nutshell the data

7. In a nutshell the data the seeds (known physical properties)

8. In a nutshell inversion

9. In a nutshell Estimate geometry!

10. In a nutshell (~ 1 min) Estimate geometry!

11. In a nutshell fits! (~ 1 min) Estimate geometry!

12. Behind the scenes (aka, Methodology)

13. the data the “truth”

14. the seed

15. the predicted data

16. the neighbors

17. add the best

18. the new predicted add the best

19. the new predicted the new neighbors add the best

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35. the same shape

36. the fattening

37. the fattening

38. the fattening

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43. the final solution

44. the final solution fits!

45. Why it grows that way • Choice of the best:

    1. Not random 2. 3. Smallest goal function φ=[∑ i (d i o−d i )2 ]1 2 Γ=ψ+μθ

46. Γ=ψ+μθ θ=∑ k l k regularizing function compactness distance of

    added cells to seed = scalar μ

47. Γ=ψ+μθ θ=∑ k l k regularizing function compactness distance of

    added cells to seed ψ=[∑ i (α d i o−d i )2]1 2 shape-of-anomaly function (René, 1986) scale factor between observed and predicted = scalar μ

48. Real data (Morro do Engenho, Brazil)

49. Previous interpretation ME for short

50. Geologic profile Forward modeling After Dutra and Marangoni (2009) Layered

    complex Magnetization Dunite center Know the magnetization

51. The data

52. The data ME

53. The data ME A2

54. The data ME A2 ?

55. The data ME A2 ? same as ME?

56. Test this hypothesis

57. The seeds

58. N

59. N

60. N Outcropping

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64. Poor fit!

65. Get rid of “tentacles”

66. Use data weights

67. Use data weights φ=[∑ i w i (d i o−d

    i )2]1 2

68. Use data weights φ=[∑ i w i (d i o−d

    i )2]1 2 w i =exp (−[(x i −x s )2+( y i −y s )2]2 σ4 )

69. Use data weights φ=[∑ i w i (d i o−d

    i )2]1 2 w i =exp (−[(x i −x s )2+( y i −y s )2]2 σ4 ) s = closest seed

70. Use data weights φ=[∑ i w i (d i o−d

    i )2]1 2 w i =exp (−[(x i −x s )2+( y i −y s )2]2 σ4 ) s = closest seed

71. with weights N

72. N

73. with weights without weights

74. N still outcropping

75. N still outcropping still poor fit

76. hypothesis

77. Conclusion • Fast geometry estimation • Known magnetization • Seed

    position • Data weights = more robust • Magnetization of A2 ≠ ME – Probably higher

78. Developed open-source fatiando.org

79. What we're working on (seed positioning)

80. the model the data

81. Single seed at the top

82. the not very good estimate

83. the not very good estimate

84. Extract new seeds from estimate

85. the much better estimate

86. the much better estimate