In multi-seismic-attribute analysis area, facies classification provides an effective way for lithology discrimination and reservoir characterization. SOM method has been widely used in seismic attributes analysis. But the application of SOM method is usually performed in an empirical manner, such as clusters’ number selection and classification procedure, which introduce artificial bias and unstable result. In this paper, we introduce a visualization-induced SOM method. With distance preservation on mapping grid, seismic multidimensional data is able to be visualized on 2-D plane in a very natural and accurate way, and automatic and stable facies classification could also be implemented. We use a simple classification example to illustrate the advantage and verify its effectiveness on a field data.


Article metrics loading...

Loading full text...

Full text loading...


  1. Coléou, T., Poupon, M. and Azbel, K.
    [2003] Unsupervised seismic facies classification: A review and comparison of techniques and implementation. The Leading Edge, 22(10), 942–953.
    [Google Scholar]
  2. de Matos, M.C., Osorio, P.L. and Johann, P.R.
    [2006] Unsupervised seismic facies analysis using wavelet transform and self-organizing maps. Geophysics, 72(1), P9–P21.
    [Google Scholar]
  3. Kohonen, T.
    [2001] Self-organizing maps (Vol. 30). Springer.
    [Google Scholar]
  4. Marroquín, I.D., Brault, J.J. and Hart, B.S.
    [2008] A visual data-mining methodology for seismic facies analysis: Part 1—Testing and comparison with other unsupervised clustering methods. Geophysics, 74(1), P1–P11.
    [Google Scholar]
  5. Roksandić, M.M.
    [1978] Seismic facies analysis concepts. Geophysical Prospecting, 26(2), 383–398.
    [Google Scholar]
  6. Saggaf, M.M., Toksöz, M.N. and Marhoon, M.I.
    [2003] Seismic facies classification and identification by competitive neural networks. Geophysics, 68(6), 1984–1999.
    [Google Scholar]
  7. West, B.P., May, S.R., Eastwood, J.E. and Rossen, C.
    [2002] Interactive seismic facies classification using textural attributes and neural networks. The Leading Edge, 21(10), 1042–1049.
    [Google Scholar]
  8. Yin, H.
    [2002] Data visualisation and manifold mapping using the ViSOM. Neural Networks, 15(8), 1005–1016.
    [Google Scholar]
  9. [2008] On multidimensional scaling and the embedding of self-organising maps. Neural Networks, 21(2), 160–169.
    [Google Scholar]

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