# Tensor-based subspace learning for tracking salt-dome boundaries

arXiv: https://arxiv.org/abs/1901.02921 · HTML: https://mlsanity.com/paper/1901.02921/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2019.

## Built on by (most cited first, 1 of 1)

- [A novel attention model for salient structure detection in seismic volumes](https://mlsanity.com/paper/2201.06174/) (2022)

## Abstract (arXiv)

The exploration of petroleum reservoirs has a close relationship with the identification of salt domes. To efficiently interpret salt-dome structures, in this paper, we propose a method that tracks salt-dome boundaries through seismic volumes using a tensor-based subspace learning algorithm. We build texture tensors by classifying image patches acquired along the boundary regions of seismic sections and contrast maps. With features extracted from the subspaces of texture tensors, we can identify tracked points in neighboring sections and label salt-dome boundaries by optimally connecting these points. Experimental results show that the proposed method outperforms the state-of-the-art salt-dome detection method by employing texture information and tensor-based analysis.