通过稳定扩散-对抗模型从二维图像生成异构三维地质微观结构
Generating Heterogeneous 3D Geological Microstructures from 2D Images via a Stable Diffusion-Adversarial Model
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中文总结 AI 辅助
提出一种结合去噪扩散模型与对抗损失的混合方法，从二维图像生成异构三维地质微观结构，实现高保真重建并减少伪影。
中文摘要 AI 辅助
表征粘土和胶凝材料的物理性质在许多领域都很重要，从材料科学到地质废物处置。性质模拟通常需要三维成像，这既昂贵，又不总是可获取，且对某些材料存在技术限制。近期深度生成模型的进展提供了一种替代途径，即从更易获取的二维图像重建三维体积。在基于GAN的三维微观结构生成方法中，SliceGAN在均匀各向同性和各向异性系统中表现出色。然而，它难以捕捉更复杂的异构微观结构的精细细节，这促使了替代生成框架的发展。我们提出了一种混合方法，利用去噪扩散模型的稳定性和生成质量。由于没有三维真实数据可用，我们用对抗损失替代标准的去噪损失，这在我们的实验中产生了稳定的训练过程。我们展示了所生成的模型能够生成不同复杂度的微观结构，且切片伪影极少，与真实相位分数和结构描述符高度一致。
英文摘要
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.
