VISULATION OF GEOLOGIC FEATURES USING DATA REPRESENTATIONS THEREOF

• US 20110115787A1
• Filed: 04/13/2009
• Published: 05/19/2011
• Est. Priority Date: 04/11/2008
• Status: Abandoned Application

• Current Assignee: CGG Jason (Netherlands) B.V. (CGG)
• Original Assignee: Terrraspark Geosciences LLC (CGG)
• Inventors: Kadlec, Benjamin J.
• Application Number:
  US12/936,532
• Publication Number:
  US 20110115787A1
• Time in Patent Office: Days
• US Class Current: 345/419
• CPC Class Codes: G01V 1/345 Visualisation of seismic data or attributes, e.g. in 3D cubes
• CPC Class Codes: G01V 2210/64 Geostructures, e.g. in 3D data cubes
• CPC Class Codes: G06T 17/05 Geographic models
• CPC Class Codes: G06T 19/00 Manipulating 3D models or images for computer graphics
• CPC Class Codes: G06T 2207/10072 Tomographic images
• CPC Class Codes: G06T 2207/20101 Interactive definition of point of interest, landmark or seed
• CPC Class Codes: G06T 2207/20161 Level set
• CPC Class Codes: G06T 7/12 Edge-based segmentation

Abstract

One exemplary embodiment presents a unified approach in the form of an Interactive “Violation” (simultaneous visualization and simulation) Environment (IVE) designed to efficiently segment geologic features with high accuracy. The IVE unifies image structure analysis and implicit surface modeling as a surface-driven solution that assists analysts, such as geoscientists, in the segmentation and modeling of faults, channels, and other geobodies in 3-D data, such as 3-D seismic data.

Claims

1. A computer implemented method for processing data comprising:

identifying one or more seed points in an input data volume, the input data volume including data representing a portion of an object;

utilizing an implicit surface velocity determination to identify at least one surface in the input data volume; and

one or more of outputting the at least one surface into an interactive visulation environment and storing the at least one surface in a storage device.

2. The method of claim 1, wherein the input data volume comprises seismic data.

3. The method of claim 1, wherein the input data volume includes medical imaging data.

4. The method of claim 1, wherein the input data volume includes 3-D seismic data.

5. The method of claim 1, further comprising determining derivatives and partial derivatives in the input data volume, and analyzing vector representation of magnitude changes of voxel values to determine one or more of gradients, edges, curvature, and image elements.

6. The method of claim 1, wherein level sets are used as an implicit representation of a deformable surface.

7. The method of claim 6, wherein level sets allow manipulation of the at least one surface directly.

8. The method of claim 7, wherein a level set is embedded as a zero level set of a level set function, the level set function is then evolved wherein at any time an evolving surface can be implicitly obtained by extracting the zero level set.

9. The method of claim 8, wherein a velocity of the level set is a representation that describes a motion of the surface in space and time.

10. The method of claim 1, wherein confidence and curvature information is obtained from an image structure analysis, wherein the image structure analysis uses a second order tensor to directly extract confidence and curvature information with no intermediate transformation.

11. The method of claim 1, further comprising utilizing an implicit representation of a level set to define a surface integral and a volume integral of the surface.

12. The method of claim 1, wherein a measure of confidence and curvature in input data volume will correspond to regions of high depositional curvature that present a strong and confident amplitude response.

13. The method of claim 6, wherein the level set implementation comprises data packing, numerical computation and visualization.

14. The method of claim 13, wherein data packing stores a 3-D level set function into GPU memory.

15. The method of claim 13, wherein the numerical computation of the level set optimizes data locality and maximizes compute intensity of a kernel function during each iteration.

16. The method of claim 13, wherein the visualization comprises at least one of:

a marching cubes kernel that extracts and displays an implicit surface at least one iteration; and

directly extracting and displaying an implicit surface at least one iteration.

17. The method of claim 1, further comprising iteratively developing a surface based on a voxel-by-voxel progression of the implicit surface velocity determination until a threshold is met.

18. The method of claim 17, wherein a user can steer a rendering of the surface.

19. The method of claim 1, further comprising enabling interactive steering by modifying the velocity function, wherein user-defined control of a level set surface allows growth and shrinkage of the surface.

20. The method of claim 1, further comprising determining eigenvalues and eigenvectors of a structure tensor.

21. The method of claim 20, further comprising, after determining a representation of the structure tensor and its eigenvalues and eigenvectors, determining kernels for imaging faults and channels.

22. The method of claim 21, wherein the kernels are determined during each block update of a level set domain.

23. The method of claim 22, wherein as every voxel in the level set surface is solved, further comprising determining a feature kernel and then using the resulting values in the level set determination.

24. The method of claim 1, wherein seeding is accomplished via one or more of a cubic paintbrush that can be elongated in any direction and a point set dropper that places points at mouse cursor locations.

25. The method of claim 1, further comprising allowing interactive steering by use of a shaped 3-D paintbrush.

26. The method of claim 1, further comprising providing interactive steering of an evolving surface through modification of a velocity function.

27. The method of claim 1, further comprising utilizing a Hessian matrix to enhance translation invariant second order structures in the data.

28. The method of claim 1, further comprising measuring an orientation of seismic strata using a gradient structure tensor (GST).

29. A computer-readable storage media including instructions that when executed perform the steps in claim 1.

30. One or more means for performing the steps in claim 1.

31. A system comprising:

a seed point module that identifies one or more seed points in an input data volume, the input data volume including data representing a portion of an object;

a data processing system that utilizes an implicit surface velocity determination to identify at least one surface in the input data volume; and

an output device, wherein the at least one surface is output into an interactive visulation environment that can be displayed on the output device, or stored in a storage device, the at least one surface representing a portion of the object.

32. The system of claim 31, wherein the input data volume comprises seismic data.

33. The system of claim 31, wherein the input data volume includes medical imaging data.

34. The system of claim 31, wherein the input data volume includes 3-D seismic data.

35. The system of claim 31, further comprising a structure analysis module that determines derivatives and partial derivatives in the input data volume, and analyzes a vector representation of magnitude changes of voxel values to determine one or more of gradients, edges, curvature and image elements.

36. The system of claim 31, wherein level sets are used as an implicit representation of a deformable surface.

37. The system of claim 36, wherein level sets allow manipulation of the at least one surface directly.

38. The system of claim 37, wherein a level set is embedded as a zero level set of a level set function, the level set function is then evolved wherein at any time an evolving surface can be implicitly obtained by extracting the zero level set.

39. The system of claim 38, wherein a velocity of the level set is a representation that describes a motion of the surface in space and time.

40. The system of claim 31, wherein confidence and curvature information is obtained from an image structure analysis, wherein the image structure analysis uses a second order tensor to directly extract confidence and curvature information with no intermediate transformation.

41. The system of claim 31, further comprising a level set module that uses an implicit representation of a level set to define a surface integral and a volume integral of the surface.

42. The system of claim 31, wherein a measure of confidence and curvature in input data volume will correspond to regions of high depositional curvature that present a strong and confident amplitude response.

43. The system of claim 36, wherein the level set implementation comprises data packing, numerical computation and visualization.

44. The system of claim 43, wherein data packing stores a 3-D level set function into GPU memory.

45. The system of claim 43, wherein the numerical computation of the level set optimizes data locality and maximizes compute intensity of a kernel function during each iteration.

46. The system of claim 43, wherein the visualization comprises at least one of:

a marching cubes kernel that extracts and displays an implicit surface at least one iteration; and

directly extracting and displaying an implicit surface at least one iteration.

47. The system of claim 31, wherein a surface is iteratively developed based on a voxel-by-voxel progression of the implicit surface velocity determination until a threshold is met.

48. The system of claim 47, wherein a user can steer a rendering of the surface via an input device.

49. The system of claim 31, wherein interactive steering is enabled by modifying the velocity function, wherein user-defined control of a level set surface allows growth and shrinkage of the surface.

50. The system of claim 31, further comprising a structure tensor module determines eigenvalues and eigenvectors of a structure tensor.

51. The system of claim 50, wherein, after determining a representation of the structure tensor and its eigenvalues and eigenvectors, the structure tensor module in cooperation with the channel module and fault module determines kernels for imaging faults and channels.

52. The system of claim 51, wherein the kernels are determined during each block update of a level set domain.

53. The system of claim 52, wherein as every voxel in the level set surface is solved, further comprising determining a feature kernel and then using the resulting values in the level set determination.

54. The system of claim 31, wherein seeding is accomplished via one or more of a cubic paintbrush that can be elongated in any direction and a point set dropper that places points at mouse cursor locations.

55. The system of claim 31, further comprising an input device and the interactive visulation environment that allow interactive steering by use of a shaped 3-D paintbrush.

56. The system of claim 31, wherein interactive steering of an evolving surface is provided through the use of modification of a velocity function.

57. The system of claim 31, further comprising a Hessian tensor module that utilizes a Hessian matrix to enhance translation invariant second order structures in the data.

58. The system of claim 31, wherein an orientation of seismic strata is measured using a gradient structure tensor (GST).