With the growing demand of high-resolution subsurface characterization from 3D seismic surveying, the size of 3D seismic datasets has been dramatically increasing, and correspondingly, the process of interpreting a seismic dataset is becoming more time consuming and labor intensive. In addition, supervised machine learning has proved to be very successful for many applications in computational seismic interpretation. However obtaining training labels for large volumes of seismic data is a very demanding task. Furthermore, while the amount of data is continuously growing, the ability of human experts to label data remains limited. In this work, we propose a weakly-supervised framework for labeling seismic structures using Non-Negative Matrix Factorization (NMF) with additional sparsity and orthogonality constraints. We show that weakly-supervised learning requires a much smaller number of labels. Furthermore, we show that “rough” image-level labels of specific seismic structures can be mapped into finer more localized locations within the seismic volume. Results obtained by labeling fault regions and salt dome boundaries from the Netherlands F3 block prove to be very promising.


Article metrics loading...

Loading full text...

Full text loading...


  1. Alaudah, Y. and AlRegib, G.
    [2016] Weakly-supervised labeling of seismic volumes using reference exemplars. In: 2016 IEEE International Conference on Image Processing (ICIP). 4373–1377.
    [Google Scholar]
  2. Alfarraj, M.,, Alaudah, Y. and AlRegib, G.
    [2016] Content-adaptive Non-parametric Texture Similarity Measure. 2016 IEEE Workshop on Multimedia Signal Processing (MMSP).
    [Google Scholar]
  3. Barnes, A.E. and Laughlin, K.J.
    [2005] Investigation of methods for unsupervised classification of seismic data. 2221–2224.
    [Google Scholar]
  4. CeGP
    [2015] LANDMASS: Large North-Sea Dataset of Migrated Aggregated Seismic Structures. http://cegp.ece.gatech.edu/codedata/LANDMASS/default.htm.
    [Google Scholar]
  5. 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]
  6. Ding, C., He, X. and Simon, H.D.
    [2005] On the Equivalence of Nonnegative Matrix Factorization and Spectral Clustering. Proceedings of the fifth SIAM International Conference on Data Mining (SDM), (4), 606–610.
    [Google Scholar]
  7. 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]
  8. Figueiredo, A., Silva, F.B., Silva, P., de O. Martins, L., Milidiú, R.L. and Gattass, M.
    [2015] A Clustering-based Approach to Map 3D Seismic Horizons. 1166–1170.
    [Google Scholar]
  9. Guillen, P., Larrazabal, G., González, G., Boumber, D. and Vilalta, R.
    [2015] Supervised Learning to Detect Salt Body. Society of Exploration Geophysicists.
    [Google Scholar]
  10. Hoyer, P.O.
    [2004] Non-negative Matrix Factorization with Sparseness Constraints. The Journal of Machine Learning Research, 5, 1457–1469.
    [Google Scholar]
  11. Lee, D. and Seung, H.
    [2001] Algorithms for non-negative matrix factorization. Advances in neural information processing systems, (1), 556–562.
    [Google Scholar]
  12. Lee, D.D. and Seung, H.S.
    [1999] Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–91.
    [Google Scholar]
  13. Qi, J., Lin, T., Zhao, T., Li, F. and Marfurt, K.
    [2016] Semisupervised multiattribute seismic facies analysis. Interpretation, 4(1), SB91–SB106.
    [Google Scholar]
  14. Ramirez, C., Larrazabal, G. and Gonzalez, G.
    [2016] Salt body detection from seismic data via sparse representation. Geophysical Prospecting, 64(2), 335–347.
    [Google Scholar]
  15. 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), WD101–WD116.
    [Google Scholar]
  16. Zhao, T., Jayaram, V., Roy, A. and Marfurt, K.J.
    [2015] A comparison of classification techniques for seismic facies recognition. Interpretation, 3(4), SAE29–SAE58
    [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