  Recently, seismic facies classification based on convolutional neural
networks (CNN) has garnered significant research interest. However, existing
CNN-based supervised learning approaches necessitate massive labeled data.
Labeling is laborious and time-consuming, particularly for 3D seismic data
volumes. To overcome this challenge, we propose a semi-supervised method based
on pixel-level contrastive learning, termed CONSS, which can efficiently
identify seismic facies using only 1% of the original annotations. Furthermore,
the absence of a unified data division and standardized metrics hinders the
fair comparison of various facies classification approaches. To this end, we
develop an objective benchmark for the evaluation of semi-supervised methods,
including self-training, consistency regularization, and the proposed CONSS.
Our benchmark is publicly available to enable researchers to objectively
compare different approaches. Experimental results demonstrate that our
approach achieves state-of-the-art performance on the F3 survey.
