Generating Heterogeneous 3D Geological Microstructures from 2D Images via a Stable Diffusion-Adversarial Model

The paper presents a hybrid generative model that combines denoising diffusion and adversarial training to produce 3D geological microstructures from 2D images. It addresses limitations of previous GAN-based methods like SliceGAN, particularly for heterogeneous materials, by replacing the denoising loss with an adversarial loss to achieve stable training. The resulting model generates microstructures with minimal slice artefacts and accurate phase fractions and structural descriptors.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• diffusion
• safety