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Abstract

Summary

In seismic imaging, a long sought after goal has been either full or partial automation of the seismic image segmentation and interpretation processes. In this study, we present a novel supervised learning method for textural classification of seismic image patches, based on a topological tool called persistent homology. The basic workflow starts by taking an image and calculating its persistent homology, which gives us a list of birth-death pairs for different homology dimensions. Polynomial feature vectors are then extracted from these pairs, which are used to train three commonly used classifiers --- support vector machines, random forests, and neural networks, whose performances we compare. In addition, we experiment with different derived textural attributes and test the impact of using them instead of the raw images in the workflow. Our proposed method is tested on the publicly available LANDMASS datasets, which contains two sets of 2D seismic image patches grouped into four classes. The results indicate that persistent homology derived features can be quite powerful for automated textural segmentation of seismic images.

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/content/papers/10.3997/2214-4609.201901608
2019-06-03
2024-03-29
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References

  1. Adams, H., Emerson, T., Kirby, M., Neville, R., Peterson, C., Shipman, P., Chepushtanova, S., Hanson, E., Motta, F. and Ziegelmeier, L.
    [2017] Persistence Images: A Stable Vector Representation of Persistent Homology. Journal of Machine Learning Research, 18, 1–35.
    [Google Scholar]
  2. Adcock, A., Carlsson, E. and Carlsson, G
    . [2016] The ring of algebraic functions on persistence bar codes. Homology, Homotopy and Applications, 18(1), 381–402.
    [Google Scholar]
  3. Alaudah, Y., Wang, Z.
    , Long, Z. and AlRegib, G. [2015] LANDMASS Seismic Dataset.
    [Google Scholar]
  4. Bubenik, P
    . [2015] Statistical Topological Data Analysis using Persistence Landscapes. Journal of Machine Learning Research, 16, 77–102.
    [Google Scholar]
  5. Carlsson, G
    . [2009] Topology and data. Bull. Amer. Math. Soc. (N.S.), 46(2), 255–308.
    [Google Scholar]
  6. Chen, Q. and Sidney, S
    . [1997] Seismic attribute technology for reservoir forecasting and monitoring. The Leading Edge, 16(5), 445–448.
    [Google Scholar]
  7. Chevitarese, D.S., Szwarcman, D., Brazil, E.V. and Zadrozny, B
    . [2018] Efficient Classification of Seismic Textures. In: 2018International Joint Conference on Neural Networks (IJCNN). IEEE, 1–8.
    [Google Scholar]
  8. Chopra, S. and Alexeev, V
    . [2006] Applications of texture attribute analysis to 3D seismic data. The Leading Edge, 25(8), 934–940.
    [Google Scholar]
  9. Chopra, S. and Marfurt, K.J
    . [2005] Seismic attributes – A historical perspective. Geophysics, 70(5), 3SO–28SO.
    [Google Scholar]
  10. Cohen-Steiner, D., Edelsbrunner, H. and Harer, J
    . [2007] Stability of Persistence Diagrams. Discrete & Computational Geometry, 37(1), 103–120.
    [Google Scholar]
  11. Di, H., Shafiq, M. and AlRegib, G.
    [2017] Multi-attribute k-means cluster analysis for salt boundary detection. In: 79th EAGE Conference and Exhibition 2017.
    [Google Scholar]
  12. Eichkitz, C.G., Amtmann, J. and Schreilechner, M.G.
    [2013] Calculation of grey level co-occurrence matrix-based seismic attributes in three dimensions. Computers & Geosciences, 60, 176–183.
    [Google Scholar]
  13. Freudenthal, H
    . [1942] Simplizialzerlegungen von beschrankter Flachheit. Annals of Mathematics, 43(3), 580–582.
    [Google Scholar]
  14. Ghrist, R
    . [2017] Homological algebra and data. In: The Mathematics of Data, IAS/Park City Mathematics Series, 25, 273–325.
    [Google Scholar]
  15. Love, P. and Simaan, M
    . [1984] Segmentation of stacked seismic data by the classification of image texture. In: SEG Technical Program Expanded Abstracts 1984, Society of Exploration Geophysicists, 480–482.
    [Google Scholar]
  16. Maria, C
    . [2015] Filtered Complexes. In: GUDHI User and Reference Manual, GUDHI Editorial Board.
    [Google Scholar]
  17. Waldeland, A.U., Jensen, A.C., Gelius, L.J. and Solberg, A.H.S
    . [2018] Convolutional neural networks for automated seismic interpretation. The Leading Edge, 37(7), 529–537.
    [Google Scholar]
  18. 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]
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