一种生成异质3D地质微结构的稳定扩散-对抗模型
Generating Heterogeneous 3D Geological Microstructures from 2D Images via a Stable Diffusion-Adversarial Model
这个研究用扩散模型和对抗训练，从2D图片生成3D地质结构，解决了传统方法昂贵的问题，生成的结构细节和真实数据很接近。
本文提出了一种混合方法，结合了稳定扩散模型的稳定性和生成质量，用于从2D图像生成异质3D地质微结构。由于没有可用的3D真实数据，作者将标准去噪损失替换为对抗损失，实现了稳定的训练过程。该方法生成的微结构复杂度可变，切片伪影极少，且与真实相分数和结构描述符高度一致。
Generating Heterogeneous 3D Geological Microstructures from 2D Images via a Stable Diffusion-Adversarial Model
Characterizing the physical properties of clay and cementitious materials matters across many fields, from materials science to geological waste disposal. Property simulation typically calls for 3D imaging, which is expensive, not always accessible, and technically limited for certain materials. Recent progress in deep generative models offers a way around this, reconstructing 3D volumes from the more easily acquired 2D images. Among GAN-based methods for 3D microstructure generation, SliceGAN has shown strong results for homogeneous isotropic and anisotropic systems. It struggles, however, to capture the finer detail of more complex heterogeneous microstructures, which motivates alternative generative frameworks. We introduce a hybrid approach that draws on the stability and generation quality of denoising diffusion models. Since no 3D ground truth is available, we replace the standard denoising loss with an adversarial loss, which yields a stable training process in our experiments. We show that the resulting model generates microstructures of varying complexity with minimal slice artefacts and close agreement with ground-truth phase fractions and structural descriptors.