Volumetric Supervised Contrastive Learning for Seismic Semantic Segmentation
This work addresses the challenge of expensive pixel-level labeling in seismic interpretation, offering a domain-specific improvement for geoscience applications.
The paper tackled the problem of limited labeled data in seismic interpretation by proposing a novel positive pair selection strategy for contrastive learning that incorporates seismic volumetric context, resulting in learned representations that outperform a state-of-the-art contrastive learning method in semantic segmentation tasks.
In seismic interpretation, pixel-level labels of various rock structures can be time-consuming and expensive to obtain. As a result, there oftentimes exists a non-trivial quantity of unlabeled data that is left unused simply because traditional deep learning methods rely on access to fully labeled volumes. To rectify this problem, contrastive learning approaches have been proposed that use a self-supervised methodology in order to learn useful representations from unlabeled data. However, traditional contrastive learning approaches are based on assumptions from the domain of natural images that do not make use of seismic context. In order to incorporate this context within contrastive learning, we propose a novel positive pair selection strategy based on the position of slices within a seismic volume. We show that the learnt representations from our method out-perform a state of the art contrastive learning methodology in a semantic segmentation task.