用地震数据与井数据联合建模，提升地下速度图的精度和地质合理性。
Joint Velocity Slope Diffusion Prior for Structurally Constrained Velocity Model Building
- 通过波面破坏法估计局部构造斜率，引导速度模型构建方向。
- 在Volve和Viking Graben数据上，结构连续性和横向一致性显著提升。
- 适合需要高精度地下建模的油气勘探工程师使用。
高分辨率速度模型对储层刻画和地下结构界定至关重要。然而，地表记录数据的带限特性限制了分辨率。利用井下测量数据提升地下模型分辨率是重要目标。为此，我们提出一种基于扩散引导的框架，从稀疏井资料出发进行结构约束的速度模型重建。该方法统一整合平面波偏微分方程正则化、结构预处理反演以及测量引导的扩散后验采样。通过平面波破坏法估计的局部结构斜率，既用于沿地质倾角方向传播井信息，也通过联合速度-斜率生成先验指导扩散采样过程。在Volve合成模型和Viking Graben实测数据集上的数值实验表明，相比传统结构预处理反演方法，该框架显著提升了结构连续性、横向一致性和地质合理性，同时通过DDIM采样保持计算效率。
原文摘要 · Abstract (English)
High-resolution velocity models are crucial for reservoir characterization and subsurface delineation. However, the band limited nature of our surface recorded data limits resolution. Utilizing well measurements to enhance the resolution of our subsurface models is an important objective. To this end, we present a diffusion-guided framework for structurally preconditioned velocity-model reconstruction from sparse well-log information. The proposed approach combines plane-wave PDE regularization, structurally preconditioned inversion, and measurement-guided diffusion posterior sampling within a unified formulation. Local structural slopes estimated through plane-wave destruction are used both to propagate well information along geological dip directions and to guide the diffusion sampling process through a joint velocity--slope generative prior. Numerical experiments on the Volve synthetic model and the Viking Graben field dataset demonstrate that the proposed framework improves structural continuity, lateral consistency, and geological realism compared with conventional structurally preconditioned inversion approaches while maintaining computationally practical inference through DDIM sampling.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。