论文标题
缺点：半监督地震相分类的对比度学习方法
CONSS: Contrastive Learning Approach for Semi-Supervised Seismic Facies Classification
论文作者
论文摘要
最近，基于卷积神经网络（CNN）的地震相分类引起了重大的研究兴趣。但是，现有的基于CNN的监督学习方法需要大量标记的数据。标签是费力且耗时的，特别是对于3D地震数据量。为了克服这一挑战，我们提出了一种基于像素级对比度学习的半监督方法，称为CONS，可以有效地使用1％的原始注释来有效地识别地震相。此外，缺乏统一的数据部门和标准化指标阻碍了各种相分类方法的公平比较。为此，我们为评估半监督方法的评估（包括自我训练，一致性正则化和提议的缺点）开发了一个客观的基准。我们的基准可以公开使用，以使研究人员能够客观地比较不同的方法。实验结果表明，我们的方法在F3调查中实现了最先进的表现。
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.
