1887

Abstract

Summary

Salt segmentation and characterization can be viewed as a classification problem where two groups or classes (SALT and SEDIMENT) are to be assigned to each seismic data element at a given coordinate. In a previous work we propose to construct a classification machinery based on the theory of sparse representation in order to carry out salt characterization in an automatic fashion. Nonetheless, the complexity of the seismic data as well as the limitations of the migration algorithms makes this dual distinction a difficult challenge. In order to overcome this obstacle, we propose to extend our original work from the characterization of two classes to a multiclass segmentation framework. In this manner, we allow the sparse representation method to extend the number of possible outcomes when classifying a given test sample thus reducing the number of misclassifications.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.20140839
2014-06-16
2020-09-28
Loading full text...

Full text loading...

References

  1. LomaskJ., Clapp, G. and Biondi, B.
    [2007] Application of Image Segmentation to Tracking 3D Salt Boundaries, Geophysics, 72(4), 47–56.
    [Google Scholar]
  2. Ramirez, C., Larrazabal, G. and Gonzalez, G.
    [2013] Towards Automatic Geobody Detection. Repsol E & P Technical Congress, Madrid, Spain.
    [Google Scholar]
  3. Argaez, M., Sanchez, R. and Ramirez, C.
    [2012] Face Recognition from Incomplete Measurements via L1 minimization. American Journal of Computational Mathematics AJCM, 2(4), 287–294.
    [Google Scholar]
  4. WrightJ., YangA., GaneshA., SastryS. and MaY.
    [2009] Robust Face Recognition via Sparse Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(2), 210–227.
    [Google Scholar]
  5. Ramirez, C. and Argaez, M.
    [2013] An L1 minimization algorithm for non-smooth regularization in image processing. Signal, Image and Video Processing. DOI: 10.1007s11760‑013‑0454‑1.
    https://doi.org/10.1007s11760-013-0454-1 [Google Scholar]
  6. Elad, M., Figueiredo, M.A.T. and Ma, Y.
    [2010] On the Role of Sparse and Redundant Representations in Image Processing. IEEE Proceedings - Special Issue on Applications of Sparse Representation & Compressive Sensing, 98(6), 972–982.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.20140839
Loading
/content/papers/10.3997/2214-4609.20140839
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