AI 中文总结
研究提出基于流匹配生成模型的全波形反演后处理策略，利用学习到的地质先验信息，无需重新运行反演，通过FWI结果及测井数据指导生成过程，经实验验证可有效提升分辨率和地质质量，还能校正深度误差。
AI 中文摘要
全波形反演（FWI）是用于地下速度重建的强大工具，但仍然是高度不适定的，对采集限制敏感，通常需要某种形式的正则化来减少伪影并提高分辨率。虽然最近的进展表明生成模型可以将学习到的先验信息直接注入FWI优化过程，但此类方法通常需要额外的、计算成本高昂的反演迭代。在本研究中，我们提出了一种基于流匹配（FM）生成模型的FWI后处理策略，该策略无需重新运行FWI即可利用学习到的地质先验信息。该方法使用FWI结果以及测井数据（如果可用）来指导确定性生成过程。合成数据和现场数据实验表明，我们可以将测井信息和地质预期（先验）注入到提供的FWI结果中，从而有效提高其分辨率和地质质量。实际上，测井先验甚至能够改变模型深度以拟合测井信息，这是一种校正深度误差的形式。
英文摘要
Full Waveform Inversion (FWI) is a powerful tool for subsurface velocity reconstruction but remains highly ill-posed, sensitive to acquisition limitations, often requiring some form of regularization to reduce artifacts and enhance resolution. While recent developments have shown that generative models can inject learned priors directly into the FWI optimization process, such approaches typically require additional, computationally expensive inversion iterations. In this study, we propose a post-FWI refinement strategy based on a Flow Matching (FM) generative model, which leverages learned geological priors without re-running FWI. The method guides the deterministic generative process using the FWI result, as well as well logs, if available. Synthetic and field data experiments demonstrate that we can inject well information and our geological expectations (prior) into the provided FWI result, and thus, we can effectively enhance its resolution and geological quality. In fact, the well prior even managed to alter the model depth to fit the well information, which is a form of correcting for depth misties.
