🤖 AI Summary
This study addresses the limited resolution of seismic velocity models caused by bandwidth constraints in seismic data. To overcome this challenge, the authors propose a diffusion-guided high-resolution velocity modeling framework that innovatively embeds local geological dip information into the diffusion prior. By jointly generating velocity and dip priors, the method propagates sparse well data along structural orientations and integrates plane-wave PDE regularization, structure-preconditioned inversion, and measurement-guided DDIM posterior sampling to enable efficient, structurally constrained reconstruction. Experiments on both the Volve synthetic model and real-field Viking Graben data demonstrate that the proposed approach significantly enhances structural continuity, lateral consistency, and geological plausibility of the reconstructed velocity models while maintaining high computational efficiency.
📝 Abstract
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.