Home » Salt-Dome Tracking Based on Multilinear Subspace Learning

Salt-Dome Tracking Based on Multilinear Subspace Learning

Subspace Learning1Subspace Learning2

We propose a method to delineate salt-dome structures by tracking manually labeled boundaries through seismic volumes. We first extract texture features from boundary regions using the tensor-based subspace learning method. Then, we utilize one seismic attribute, the gradient of texture (GoT), as a constraint on the tracking process. Using texture features and GoT maps, we can identify tracked points and optimally connect them to synthesize the boundaries. The proposed method is evaluated using real-world seismic data and experimental results show that it outperforms the state of the art in accuracy, robustness, and computational efficiency.

[Related Publications]

  1. Z. Wang, T. Hegazy, Z. Long and G. AlRegib, “Noise-robust Detection and Tracking of Salt Domes in Post-migrated Volumes Using Texture, Tensors, and Subspace Learning,” Geophysics, 80(6), WD101-WD116. [PDF] [Poster/Lec] [Bib] [Code]
  2. Z. Wang, Z. Long, and G. AlRegib, “Tensor-based Subspace Learning for Tracking Salt-dome Boundaries Constrained by Seismic Attributes,”to be presented in IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, Mar. 20-25, 2016. [PDF] [Poster/Lec] [Bib] [Code]
  3. Z. Wang, Z. Long and G. AlRegib, “Tensor-based subspace learning for tracking salt-dome boundaries” IEEE ICIP 2015, Québec City, Canada, Sept. 27-30, 2015. [PDF] [Poster/Lec] [Bib] [Code]
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