用自然图像大模型解决地震数据去噪、插值和消多次问题
Foundation Models For Seismic Data Processing: An Extensive Review
- 直接迁移自然图像领域的大模型到地震数据处理
- 验证不同预训练方法与网络结构对性能的影响
- 为未来地震大模型研究提供候选方案参考
地震处理在将原始数据转化为高质量地下成像方面至关重要，广泛应用于地质科学。尽管重要，传统方法面临数据噪声、损坏以及依赖人工、耗时的工作流程等问题。深度学习虽提供了高效替代方案，但多数基于合成数据和专用神经网络。近年来，自然图像领域的大模型在地震领域兴起。本文研究自然图像大模型在地震处理三大任务（去多次、插值、去噪）中的应用，评估不同预训练技术与网络架构对性能与效率的影响。不提出单一地震大模型，而是批判性分析现有自然图像大模型，推荐若干适合未来探索的候选者。
原文摘要 · Abstract (English)
Seismic processing plays a crucial role in transforming raw data into high-quality subsurface images, pivotal for various geoscience applications. Despite its importance, traditional seismic processing techniques face challenges such as noisy and damaged data and the reliance on manual, time-consuming workflows. The emergence of deep learning approaches has introduced effective and user-friendly alternatives, yet many of these deep learning approaches rely on synthetic datasets and specialized neural networks. Recently, foundation models have gained traction in the seismic domain, due to their success in the natural image domain. Therefore, we investigate the application of natural image foundation models on the three seismic processing tasks: demultiple, interpolation, and denoising. We evaluate the impact of different model characteristics, such as pre-training technique and neural network architecture, on performance and efficiency. Rather than proposing a single seismic foundation model, we critically examine various natural image foundation models and suggest some promising candidates for future exploration.
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