  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.
