Interpreting salt is a complex and time consuming task in seismic interpretation.

Considerable progresses have been made recently by automatic methods which detect deterministic salt tops from the seismic image.

However, uncertainties about salt exist, owing to intrinsic physical limitations of seismic imaging.

Therefore, we propose a new workflow to generate several possible models of salt top surfaces with varying geometries and topologies.

The method we propose is divided into three steps.

We first segment the seismic volume into three regions: salt, sediments and uncertain, depending on the reliability of the automatic interpretation.

We then compute a monotonic scalar field in the uncertain region, ranging from zero at the contact with salt to one at the contact with sediments.

We finally generate a random field bounded between zero and one.

The salt boundary is implicitly defined by the zero isovalue of the scalar field defined as the difference between the distance and the random fields.

Applications of this workflow on a 2D seismic image and a 3D synthetic data set illustrate the potential of the method to efficiently address salt interpretation uncertainties.


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  1. Aqrawi, A.A., Boe, T.H. and Barros, S.
    [2011] Detecting salt domes using a dip guided 3D Sobel seismic attribute. In: SEG Technical Program Expanded Abstracts 2011.Society of Exploration Geophysicists, 1014–1018.
    [Google Scholar]
  2. Berthelot, A., Solberg, A.H.S. and Gelius, L.J.
    [2013] Texture attributes for detection of salt. Journal of Applied Geophysics, 88, 52–69.
    [Google Scholar]
  3. Di, H. and Al Regib, G.
    [2017] Seismic Multi-attribute Classification for Salt Boundary Detection - A Comparison. In: 79th EAGE Conference and Exhibition 2017, June 2017. European Association of Geoscientists and Engineers.
    [Google Scholar]
  4. Haukås, J., Bounaim, A. and Gramstad, O.
    [2017] Automated salt interpretation, Part II: Smooth surface wrapping of volume attribute. In: SEG Technical Program Expanded Abstracts 2017.2076–2080.
    [Google Scholar]
  5. Henrion, V., Caumon, G. and Cherpeau, N.
    [2010] ODSIM: An Object-Distance Simulation Method for Conditioning Complex Natural Structures. Mathematical Geosciences, 42(8), 911–924.
    [Google Scholar]
  6. Jackson, C.A.L. and Lewis, M.M.
    [2012] Origin of an anhydrite sheath encircling a salt diapir and implications for the seismic imaging of steep-sided salt structures, Egersund Basin, Northern North Sea. Journal of the Geological Society, 169(5), 593–599.
    [Google Scholar]
  7. Mallet, J.L.
    [2002] Geomodeling.Oxford University Press.
    [Google Scholar]
  8. Rongier, G., Collon-Drouaillet, P. and Filipponi, M.
    [2014] Simulation of 3D karst conduits with an object-distance based method integrating geological knowledge. Geomorphology, 217, 152–164.
    [Google Scholar]
  9. Waldeland, A.U. and Solberg, A.H.S.
    [2017] Salt classification using deep learning. In: 79th EAGE Conference and Exhibition 2017, June 2017. European Association of Geoscientists and Engineers.
    [Google Scholar]
  10. Wu, X.
    [2016] Methods to compute salt likelihoods and extract salt boundaries from 3D seismic images. GEOPHYSICS, 81(6), IM119–IM126.
    [Google Scholar]

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