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Recently, seismic facies classification based on convolutional neuralnetworks (CNN) has garnered significant research interest. However, existingCNN-based supervised learning approaches necessitate massive labeled data.Labeling is laborious and time-consuming, particularly for 3D seismic datavolumes. To overcome this challenge, we propose a semi-supervised method basedon pixel-level contrastive learning, termed CONSS, which can efficientlyidentify seismic facies using only 1% of the original annotations. Furthermore,the absence of a unified data division and standardized metrics hinders thefair comparison of various facies classification approaches. To this end, wedevelop 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 objectivelycompare different approaches. Experimental results demonstrate that ourapproach achieves state-of-the-art performance on the F3 survey.

CONSS: Contrastive Learning Approach for Semi-Supervised Seismic Facies Classification