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
35. the same shape
36. the fattening
37. the fattening
38. the fattening
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
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