1887

Abstract

Summary

In this paper, we propose and evaluate the performance of five different perceptual and non-perceptual dissimilarity measures, which are used to measure the texture dissimilarity between the two neighboring cubes that share a square face centered around the given voxel. The proposed measures are the building blocks of three dimensional Gradient of Texture (3D-GoT), which can quantify texture variations in three-dimensional space. The proposed dissimilarity measures exploit the strong coherence between neighboring seismic sections and compute cubes dissimilarity by incorporating all inline, crossline and time directions that make it effective as compared to those of 2D dissimilarity measures. The perceptual dissimilarity are consistent with human perception and yield better dissimilarity as compared to non-perceptual measures. The experimental results on the real dataset from the North Sea, F3 block illustrate that the perceptual dissimilarity measures are not only computationally more efficient but also yield better salt dome delineation results as compared to the non-perceptual dissimilarity measures.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201601022
2016-05-30
2024-03-29
Loading full text...

Full text loading...

References

  1. Amin, A. and Deriche, M.
    [2015a] A hybrid approach for salt dome detection in 2D and 3D seismic data. In: Image Processing (ICIP), 2015 IEEE International Conference on. 2537–2541.
    [Google Scholar]
  2. [2015b] A new approach for salt dome detection using a 3D multidirectional edge detector. Applied Geophysics, 12(3), 334–342.
    [Google Scholar]
  3. Aqrawi, A.A., Boe, T.H. and Barros, S.
    [2011] Detecting salt domes using a dip guided 3D Sobel seismic attribute. In: Expanded Abstracts of the SEG 81st Annual Meeting. Society of Exploration Geophysicists, 1014–1018.
    [Google Scholar]
  4. Berthelot, A., Solberg, A.H. and Gelius, L.J.
    [2013] Texture attributes for detection of salt. Journal of Applied Geophysics, 88, 52–69.
    [Google Scholar]
  5. dGB Earth Sciences, B.
    [1987] The Netherlands Offshore, The North Sea, F3 Block - Complete. https://opendtect.org/osr/pmwiki.php/Main/Netherlands/OffshoreF3BlockComplete4GB.
    [Google Scholar]
  6. Felzenszwalb, P.F. and Huttenlocher, D.P.
    [2004] Efficient graph-based image segmentation. International Journal of Computer Vision, 59(2), 167–181.
    [Google Scholar]
  7. Guillen, P., Larrazabal, G., Gonzalez, G., Boumber, D. and Vilalta, R.
    [2015] Supervised learning to detect salt body, chap. 351. 1826–1829.
    [Google Scholar]
  8. Hegazy, T., Wang, Z. and AlRegib, G.
    [2015] The Role of Perceptual Texture Dissimilarity in Automating Seismic Data Interpretation. Proc. IEEE Global Conf. on Signal and Information Processing (GlobalSIP), Orlando, Florida, Dec. 14–16.
    [Google Scholar]
  9. Qi, J., Cahoj, M., AlAli, A., Li, L. and Marfurt, K.
    [2015] Segmentation of salt domes, mass transport complexes on 3D seismic data volumes using Kuwahara windows and multiattribute cluster analysis, chap. 350. 1821–1825.
    [Google Scholar]
  10. Shafiq, M.A., Wang, Z. and Alregib, G.
    [2015a] Seismic interpretation of migrated data Using edge-based geodesic active contours. In: Proc. IEEE Global Conf. on Signal and Information Processing (GlobalSIP), Orlando, Florida, Dec. 14–16.
    [Google Scholar]
  11. Shafiq, M.A., Wang, Z., Amin, A., Hegazy, T., Deriche, M. and AlRegib, G.
    [2015b] Detection of Salt-dome Boundary Surfaces in Migrated Seismic Volumes Using Gradient of Textures. In: Expanded Abstracts of the SEG 85th Annual Meeting, New Orleans, Louisiana. 1811–1815.
    [Google Scholar]
  12. Shi, J. and Malik, J.
    [2000] Normalized cuts and image segmentation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 22(8), 888–905.
    [Google Scholar]
  13. Wang, Z., Hegazy, T., Long, Z. and AlRegib, G.
    [2015] Noise-robust detection and tracking of salt domes in postmigrated volumes using texture, tensors, and subspace learning. Geophysics, 80(6).
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201601022
Loading
/content/papers/10.3997/2214-4609.201601022
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error