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

## TL;DR

This paper introduces a tensor-based subspace learning method for tracking salt-dome boundaries in seismic volumes, improving detection accuracy by leveraging texture information and tensor analysis.

## Contribution

It presents a novel tensor-based approach that enhances salt-dome boundary tracking in seismic data, outperforming existing methods.

## Key findings

- Outperforms state-of-the-art salt-dome detection methods
- Utilizes texture tensors for improved boundary identification
- Achieves higher accuracy in seismic volume analysis

## Abstract

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.