This systematic literature review provides a comprehensive overview of the current state of deep learning (DL) specifically targeted at semantic segmentation in seismic data, with a particular focus on facies segmentation. We begin by comparing the contributions of DL to traditional techniques used in seismic image interpretation. The review then explores the learning paradigms, architectures, loss functions, public datasets, and evaluation metrics employed in seismic data segmentation. While supervised learning remains the dominant approach, recent years have seen a growing interest in semi-supervised and unsupervised methods to address the challenge of limited labeled data. Additionally, we found that the U-Net architecture is the most prevalent backbone for semantic segmentation, appearing in one-third of the articles reviewed. We also present a comprehensive compilation of the results obtained by 24 methods and discuss the challenges and research opportunities in this field. Notably, the lack of standardized protocols for performance comparison, combined with variability in datasets and evaluation metrics across studies, raises questions about what truly constitutes the current state of the art in semantic segmentation of seismic data.

中文翻译：

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深度学习在地震图像分割中的应用研究综述


本系统文献综述全面概述了深度学习 （DL） 的现状，专门针对地震数据中的语义分割，特别关注相分割。我们首先比较了 DL 与地震图像解释中使用的传统技术的贡献。然后，该综述探讨了地震数据分割中采用的学习范式、架构、损失函数、公共数据集和评估指标。虽然监督学习仍然是主要方法，但近年来，人们对半监督和无监督方法越来越感兴趣，以应对有限标记数据的挑战。此外，我们发现 U-Net 架构是语义分割最普遍的主干，出现在所审查的文章中。我们还提供了通过 24 种方法获得的结果的全面汇编，并讨论了该领域的挑战和研究机会。值得注意的是，缺乏用于性能比较的标准化协议，再加上数据集和研究之间评估指标的可变性，这引发了人们对地震数据语义分割当前技术水平的真正构成的问题。