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Abstract

Summary

While seismic data is used to interpret the structural framework, the uncertainties associated with the seismic data itself are often neglected for computational reasons. Structural uncertainty studies are often limited to perturbing horizons and faults around a single interpretation. We propose a method to assess the uncertainty in the seismic image itself through geostatistical randomization of the 3D seismic velocity model. A novel fractal algorithm is used to generate multiple 3D velocity models. To mitigate the computational costs of migrating all generated models, a model selection procedure is applied to select a representative subset of the models. Image registration techniques are applied to the resulting set of migrated images, providing uncertainty maps and as well as a distribution of structural elements, thereby yielding more realistic assessments of structural uncertainty. An application to 3D sub-salt imaging is provided.

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/content/papers/10.3997/2214-4609.201413556
2015-06-01
2024-04-25

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References

  1. 1.Hurley, J. R., & Cattell, R. B. (1962). The Procrustes program: Producing direct rotation to test a hypothesized factor structure. Behavioral Science, 7(2), 258–262.
     [Google Scholar]
  2. 2.Li, L., Caers, J., & Sava, P. (2014, October). Uncertainty maps for seismic images through geostatistical model randomization. In Expanded abstracts, 84th Annual International Meeting, SEG (pp. 1496–1500).
     [Google Scholar]
  3. 3.Mallet, J.L. (2014). Elements of mathematical sedimentary geology: The GeoChron model. Houten: EAGE Publications Bv.
     [Google Scholar]
  4. 4.Perlin, K. (2002, July). Improving noise. In ACM Transactions on Graphics (TOG) (Vol. 21, No. 3, pp. 681–682). ACM.
     [Google Scholar]
  5. 5.Pennec, X., Cachier, P., & Ayache, N. (1999, January). Understanding the “demon’s algorithm”: 3D non-rigid registration by gradient descent. In Medical Image Computing and ComputerAssisted Intervention–MICCAI’99 (pp. 597–605). Springer Berlin Heidelberg.
     [Google Scholar]
  6. 6.Scheidt, C., & Caers, J. (2009). Representing spatial uncertainty using distances and kernels. Mathematical Geosciences, 41(4), 397–419.
     [Google Scholar]
  7. 7.Stone, H. S., Orchard, M. T., Chang, E. C., & Martucci, S. A. (2001). A fast direct Fourier-based algorithm for subpixel registration of images. Geoscience and Remote Sensing, IEEE Transactions on, 39(10), 2235–2243.
     [Google Scholar]
  8. 8.Thirion, J. P. (1998). Image matching as a diffusion process: an analogy with Maxwell's demons. Medical image analysis, 2(3), 243–260.
     [Google Scholar]
  9. 9.Thore, P., Shtuka, A., Lecour, M., Ait-Ettajer, T., & Cognot, R. (2002). Structural uncertainties: Determination, management, and applications. Geophysics, 67(3), 840–852.
     [Google Scholar]
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Geostatistical Velocity Models for Assessing Seismic Uncertainty - An Application to Sub-salt Imaging
Conference Proceedings 2015, 1 (2015); https://doi.org/10.3997/2214-4609.201413556
/content/papers/10.3997/2214-4609.201413556
/content/papers/10.3997/2214-4609.201413556
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