Seismic Acoustic Impedance Inversion Framework Based on Conditional Latent Generative Diffusion Model

TL;DR

This paper introduces a novel seismic impedance inversion framework using a conditional latent diffusion model that operates in latent space, improving accuracy and efficiency for field data interpretation.

Contribution

It presents a new latent space diffusion approach with a lightweight wavelet module and a model-driven sampling strategy, enhancing inversion accuracy and reducing computational steps.

Findings

High inversion accuracy on synthetic data

Strong generalization to field data

Enhanced geological detail and consistency with well-logs

Abstract

Seismic acoustic impedance plays a crucial role in lithological identification and subsurface structure interpretation. However, due to the inherently ill-posed nature of the inversion problem, directly estimating impedance from post-stack seismic data remains highly challenging. Recently, diffusion models have shown great potential in addressing such inverse problems due to their strong prior learning and generative capabilities. Nevertheless, most existing methods operate in the pixel domain and require multiple iterations, limiting their applicability to field data. To alleviate these limitations, we propose a novel seismic acoustic impedance inversion framework based on a conditional latent generative diffusion model, where the inversion process is made in latent space. To avoid introducing additional training overhead when embedding conditional inputs, we design a lightweight wavelet-based module into the framework to project seismic data and reuse an encoder trained on impedance to embed low-frequency impedance into the latent space. Furthermore, we propose a model-driven sampling strategy during the inversion process of this framework to enhance accuracy and reduce the number of required diffusion steps. Numerical experiments on a synthetic model demonstrate that the proposed method achieves high inversion accuracy and strong generalization capability within only a few diffusion steps. Moreover, application to field data reveals enhanced geological detail and higher consistency with well-log measurements, validating the effectiveness and practicality of the proposed approach.