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

Recently, convolutional neural networks (CNNs) have been widely applied in the seismic facies classification. However, even state-of-the-art CNN architectures often encounter classification confusion distinguishing seismic facies at their boundaries. Additionally, the annotation is a highly time-consuming task, especially when dealing with 3D seismic data volumes. While traditional semi-supervised methods reduce dependence on annotation, they are susceptible to interference from unreliable pseudo-labels. To address these challenges, we propose a semi-supervised seismic facies classification method called CONSS, which effectively mitigates classification confusion through contrastive learning. Our proposed method requires only 1% of labeled data, significantly reducing the demand for annotation. To minimize the influence of unreliable pseudo-labels, we also introduce a confidence strategy to select positive and negative sample pairs from reliable regions for contrastive learning. Experimental results on the publicly available seismic datasets, Netherlands F3 and SEAM AI challenge dataset, demonstrate that the proposed method outperforms classic semi-supervised methods, including self-training and consistency regularization, achieving exceptional classification performance. Our all codes and data are available at https://github.com/upcliuwenlong/CONSS_SEISMIC_FACIES .