Joint Velocity Slope Diffusion Prior for Structurally Constrained Velocity Model Building
On the map: Diffusion models → Flow matching, time series and science
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 7 of its 45 references that are in the map. First posted on arXiv 6 Jul 2026.
Builds on
7 of 45 references.
References · 7
- 2020Denoising Diffusion Probabilistic Modelscited by 21,076
- 2020Denoising Diffusion Implicit Modelscited by 9,781
- 2022Diffusion Posterior Sampling for General Noisy Inverse Problemscited by 1,416
- 2019Deep-learning inversion: a next generation seismic velocity-model building methodcited by 50
- 2018Stochastic seismic waveform inversion using generative adversarial networks as a geological priorcited by 48
- 2025Geophysical inverse problems with measurement-guided diffusion modelscited by 6
- 2023PINNslope: seismic data interpolation and local slope estimation with physics informed neural networkscited by 1
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- 2024Controllable seismic velocity synthesis using generative diffusion models
- 2026Structure-aware Bayesian full waveform inversion via diffusion-regularized Stein variational gradient descent
- 2023A prior regularized full waveform inversion using generative diffusion models
- 2024Geological and Well prior assisted full waveform inversion using conditional diffusion models
- 2025Diffusion Model-Based Posterior Sampling in Full Waveform Inversion
- 2026Decoupled Latent Optimization of Diffusion Models for Full Waveform Inversion