Estimation of Elastic Parameters with Guidance-based Diffusion model

Abstract

Elastic parameters are fundamental rock properties for reservoir characterization, but their reliable estimation from angle-stack seismic data remains challenging due to strong nonlinearity and imperfect physical modeling. Conventional deterministic approaches based on linearized Zoeppritz approximations yield a single point estimate and cannot quantify solution uncertainty, while probabilistic methods are computationally expensive. To address these limitations, we present a workflow for elastic parameter inversion from angle-stack seismic data using a guided diffusion model as an implicit prior over the joint distribution of P-wave velocity, S-wave velocity, and density. The diffusion model is trained in an unsupervised manner on benchmark datasets and well-log-derived synthetic models, learning the non-Gaussian statistical coupling among the three elastic parameters. For guidance, we employ Diffusion Posterior Sampling (DPS), which approximates the likelihood function through a forward operator based on the Aki-Richards approximation and injects data-consistency gradient corrections at each reverse diffusion step, sampling from a posterior conditioned on the misfit between observed and modeled angle-stack data. We evaluate the framework on two datasets: the 2D Otway synthetic elastic model and field data from the Poseidon field, NW Shelf, Browse Basin, Australia, comparing it against two baselines: LSQR least-squares inversion and ADMM-based inversion with total variation regularization. Quantitative comparisons confirm that the diffusion-based framework recovers sharper lithological contrasts and geologically more realistic elastic profiles. Uncertainty quantification is achieved by generating multiple independent posterior realizations through repeated reverse diffusion runs, producing spatially resolved uncertainty maps for each elastic parameter.