Tensor-based subspace learning for tracking salt-dome boundaries constrained by seismic attributes

Abstract

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.