Self-Supervised Learning for Seismic Image Segmentation From Few-Labeled Samples

Citation

Monteiro BAA, Oliveira H & dos Santos J (2022) Self-Supervised Learning for Seismic Image Segmentation From Few-Labeled Samples. IEEE Geoscience and Remote Sensing Letters, 19, pp. 1-5. https://doi.org/10.1109/lgrs.2022.3193567

Abstract
Current deep learning methods for interpreting seismic images require large amounts of labeled data, and due to strategic and economic interests, these data are not plenty available. In this scenario, seismic interpretation can benefit from self-supervised learning (SSL) by relying on prior training without manually annotated labels within the target data domain and subsequent fine-tuning with few shots. To demonstrate the potential of such an approach, we conducted experiments with three classic context-based pretext tasks: rotation, jigsaw, and frame order prediction. Our results for 1, 5, 10, and 20 shots showed significant improvement for mean Intersection-over-Union (mIoU) measurements for semantic segmentation in most scenarios, outperforming the baseline method in 38% in the one-shot scenario for the F3 Netherlands Dataset and 16.4% in the New Zealand Parihaka dataset, and this gap grows even higher after performing ensemble modeling. These experiments suggest that applying SSL methods can also bring great benefits in seismic interpretation when few labeled data are available.

Keywords
Electrical and Electronic Engineering; Geotechnical Engineering and Engineering Geology

Journal
IEEE Geoscience and Remote Sensing Letters: Volume 19