This paper introduces an approach to enhance seismic fault recognition through self-supervised pretraining. Seismic fault interpretation holds great significance in the fields of geophysics and geology. However, conventional methods for seismic fault recognition encounter various issues, including dependence on data quality and quantity, as well as susceptibility to interpreter subjectivity. Currently, automated fault recognition methods proposed based on small synthetic datasets experience performance degradation when applied to actual seismic data. To address these challenges, we have introduced the concept of self-supervised learning, utilizing a substantial amount of relatively easily obtainable unlabeled seismic data for pretraining. Specifically, we have employed the Swin Transformer model as the core network and employed the SimMIM pretraining task to capture unique features related to discontinuities in seismic data. During the fine-tuning phase, inspired by edge detection techniques, we have also refined the structure of the Swin-UNETR model, enabling multiscale decoding and fusion for more effective fault detection. Experimental results demonstrate that our proposed method attains state-of-the-art performance on the Thebe dataset, as measured by the OIS and ODS metrics.

翻译：本文提出了一种通过自监督预训练增强地震断层识别的方法。地震断层解释在地球物理学和地质学领域具有重要意义。然而，传统地震断层识别方法面临诸多问题，包括对数据质量和数量的依赖，以及易受解释者主观性的影响。目前，基于小型合成数据集提出的自动断层识别方法在应用于实际地震数据时会出现性能下降。为解决这些挑战，我们引入了自监督学习的概念，利用大量较易获取的无标签地震数据进行预训练。具体而言，我们采用Swin Transformer模型作为核心网络，并利用SimMIM预训练任务捕获地震数据中与不连续性相关的独特特征。在微调阶段，受边缘检测技术启发，我们还改进了Swin-UNETR模型的结构，实现了多尺度解码与融合，以更有效地检测断层。实验结果表明，在Thebe数据集上，我们提出的方法在OIS和ODS指标上达到了当前最优性能。