Joint Velocity Slope Diffusion Prior for Structurally Constrained Velocity Model Building
This work addresses the problem of enhancing velocity model resolution from sparse well data for geoscientists, offering a practical improvement over existing structurally preconditioned inversion techniques.
The paper presents a diffusion-guided framework that integrates plane-wave PDE regularization, structurally preconditioned inversion, and measurement-guided diffusion posterior sampling to reconstruct high-resolution velocity models from sparse well-log data. Experiments on Volve synthetic and Viking Graben field datasets show improved structural continuity, lateral consistency, and geological realism over conventional methods.
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.