Contrastive Learning Approach for Semi-Supervised Seismic Facies Identification Using High-Confidence Representations

Abstract

The manual seismic facies annotation relies heavily on the experience of seismic interpreters, and the distribution of seismic facies in adjacent locations is very similar, which means that much of the labeling is costly repetitive work. However, we found that training the model with only a few evenly sampled labeled slices still suffers from severe classification confusion, that is, misidentifying one class of seismic facies as another. To address this issue, we propose a semi-supervised seismic facies identification method using features from unlabeled data for contrastive learning. We sample features in regions with high identification confidence, and use a pixel-level instance discrimination task to narrow the intra-class distance and increase the inter-class distance. Instance discrimination encourages the latent space to produce more distinguishable decision boundaries and reduces the bias in the features of the same class. Our method only needs to extend one branch to compute the contrastive loss without extensive changes to the network structure. We have conducted experiments on two public seismic surveys, SEAM AI and Netherlands F3, and the proposed model achieves an IOU score of more than 90 using only 1% of the annotations in the F3 survey. We have made our codes and pre-trained models publicly available: www.github.com/upcliuwenlong/CONSS.

Seismic Stratigraphic Interpretation Based on Unsupervised Validation and Spectral Clustering Sampling

“…The scarcity of labeled data implies the unavailability of a validation set, which is a common problem in seismic image segmentation with limited samples. Previous studies [19] [23] [28] have also lacked a validation set for model selection during the training process. The absence of a validation set remains a concerning matter, despite the availability of various techniques to mitigate overfitting.…”