标题： SeisCoDE：基于对比自蒸馏学习的三维地震解释基础模型 显示英文标题

标题： SeisCoDE: 3D Seismic Interpretation Foundation Model with Contrastive Self-Distillation Learning

摘要： 地震解释对于理解地下结构至关重要，但仍然耗时费力、主观性强且计算需求高。虽然深度学习（DL）提供了希望，但其成功依赖于大量高质量的数据集，而这类数据集在地球物理学中往往稀缺。基础模型（FMs），已经在自然语言处理和计算机视觉等领域取得了显著成功，在地震解释中通过实现跨解释任务的知识迁移和泛化，提供了一个变革性的机会。然而，由于缺乏特定领域的预训练工作流程，基础模型在这方面的应用仍受到限制，特别是在三维尺度上。本研究旨在通过引入基于视觉变换器的地震对比自蒸馏编码器（SeisCoDE），即一种新颖的自监督学习（SSL）框架，该框架利用地震信号处理和属性分析，同时在预训练过程中保留地震结构的完整性，从而开发三维地震解释的预训练策略。通过利用对比学习和自蒸馏，SeisCoDE能够在无需标注数据的情况下（零样本方法）学习有意义的潜在表示。结果显示，SeisCoDE能够有效捕捉关键的地震特征和特性，生成稳健的潜在特征表示，推动下游地震解释。它在不同的地震解释任务中表现出增强的泛化能力，优于传统的有监督学习UNet方法。总体而言，这项研究强调了由地震图像处理和属性分析原则指导的基础模型的潜力，为继续创新整合基础模型以用于地震解释铺平了道路，具有革新地下特征描述和地球物理地震勘探的潜力。 显示英文摘要

摘要： Seismic interpretation is vital for understanding subsurface structures but remains labor-intensive, subjective, and computationally demanding. While deep learning (DL) offers promise, its success hinges on large, high-quality datasets, often scarce in geophysics. Foundation Models (FMs), which have shown significant success in fields like natural language processing and computer vision, offer a transformative opportunity for seismic interpretation by enabling knowledge transfer and generalization across interpretation tasks. However, the application of FMs in this domain remains limited, especially at the 3D scale, due to the absence of a domain-specific pretraining workflow. Here, our study sought to develop a pretraining strategy for 3D seismic interpretation by introducing a vision transformer-based Seismic Contrastive Self-Distillation Encoder (SeisCoDE), a novel self-supervised learning (SSL) framework that leverages seismic signal processing and attribute analysis, preserving seismic structural integrity during pretraining. By leveraging contrastive learning and self-distillation, SeisCoDE learns meaningful latent representations without the need for labeled data (zero-shot approach). Results indicate that SeisCoDE effectively captures critical seismic features and characteristics, producing robust latent feature representations that drive downstream seismic interpretation. It demonstrates enhanced generalization abilities across different seismic interpretation tasks, outperforming the conventional supervised learning UNet method. Overall, this research emphasizes the potential of FMs informed by seismic image processing and attribute analysis principles, paving the way for continued innovation integrating FMs for seismic interpretation, with the potential to revolutionize subsurface characterization and geophysical seismic exploration.