1887

Abstract

Summary

Traditional methods for salt classification consist of choosing a set of attributes that are sensitive to the characteristics of salt bodies and training a classification algorithm to discriminate between salt and other geological structures. Convolutional neural networks have the advantage of combining attribute extraction and classification in one network. This allows both the attributes and classification to be trainable for the given application. In this work we show how this technique can be applied to salt classification in seismic datasets. The results shows that training a classifier on one labelled inline slice is sufficient to classify other slices in the same dataset.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201700918
2017-06-12
2024-11-05
Loading full text...

Full text loading...

References

  1. Amin, A., Deriche, M., Hegazy, T., Wang, Z. and Alregib, G.
    [2015] A Novel Approach for Salt Dome Detection using A Dictionary-based Classifier. In: SEG 2015 Annual Meeting, Expanded Abstracts. 1816–1820.
    [Google Scholar]
  2. Aqrawi, A.A., Boe, T.H. and Barros, S.
    [2011] Detecting Salt Domes Using a Dip Guided 3D Sobel Seismic Attribute. In: SEG 2011 Annual Meeting, Expanded Abstracts. Society of Exploration Geophysicists, 1014–1018.
    [Google Scholar]
  3. 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]
  4. Clevert, D.A., Unterthiner, T. and Hochreiter, S.
    [2016] Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs). In: ICLR2016, 1997. 1–13.
    [Google Scholar]
  5. Halpert, A. and Clapp, R.G.
    [2008] Salt body segmentation with dip and frequency attributes. SEP-Report, 136, 113–124.
    [Google Scholar]
  6. Hegazy, T. and AlRegib, G.
    [2014] Texture attributes for detecting salt bodies in seismic data. In: SEG 2014 Annual Meeting, Expanded Abstracts. 1455–1459.
    [Google Scholar]
  7. Ioffe, S. and Szegedy, C.
    [2015] Batch Normalization: Accelerating Deep Network Training by Re-ducing Internal Covariate Shift. In: Proceedings of The 32nd International Conference on Machine Learning. 2015. 1–11.
    [Google Scholar]
  8. Kingma, D. and Ba, J.
    [2015] Adam: A method for stochastic optimization. In: ICLR 2015. 1–15.
    [Google Scholar]
  9. Lomask, J., Clapp, R.G. and Biondi, B.
    [2007] Image segmentation for tracking 3D salt boundaries. Geophysics, 72(4), 47–56.
    [Google Scholar]
  10. Moraleda, L.
    [2015] Interpretation, modelling, and halokinetic evolution of salt diapirs in the Nordkapp Basin. Master thesis, Unviersity of Stavanger.
    [Google Scholar]
  11. Rumelhart, D.E., Hinton, G.E. and Williams, R.J.
    [1986] Learning representations by back-propagating errors. Nature, 323(6088), 533–536.
    [Google Scholar]
  12. Simard, P.Y., Steinkraus, D. and Platt, J.C.
    [2003] Best practices for convolutional neural networks applied to visual document analysis. Document Analysis and Recognition, 2003. Proceedings. Seventh International Conference on, 958–963.
    [Google Scholar]
/content/papers/10.3997/2214-4609.201700918
Loading
/content/papers/10.3997/2214-4609.201700918
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