Abstract
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.
Abstract (translated)
这篇论文提出了一种通过自监督预训练来增强地震断层识别的方法。地震断层识别在地质和地球物理学领域具有重要的意义。然而，传统的地震断层识别方法遇到了各种问题，包括对数据质量和数量的不确定性，以及受到解读者主观性的影响。目前，基于小合成数据集的自动断层识别方法在实际地震数据上应用时，表现出了性能下降的趋势。为了应对这些挑战，我们引入了自监督学习的概念，利用大量相对容易获得的未标记地震数据进行预训练。具体来说，我们采用了Swin Transformer模型作为核心网络，并使用SimMIM预训练任务来捕捉地震数据中与断层相关的独特特征。在微调阶段，受到边缘检测技术启发的，我们还对Swin-UNETR模型的结构进行了优化，实现了多尺度解码和融合，以提高故障检测的有效性。实验结果表明，我们提出的方法在Thebe数据集上取得了与最先进方法相同的性能，这是通过OIS和ODS指标来衡量的。
URL
https://arxiv.org/abs/2310.17974
