用2D图像生成可控孔隙率的3D多孔介质，无需昂贵3D数据。
Property-Constrained 3D Porous Media Reconstruction from 2D Images via Conditional Generative Adversarial Networks
- 用条件GAN结合2D图像生成3D结构，控制孔隙率
- 孔隙率预测相关系数达0.93，误差低于0.02
- 适合地质建模与油藏模拟研究者使用
本研究提出一种基于条件生成对抗网络（cGAN）的框架，仅利用2D薄片图像生成具有可控孔隙率的3D多孔介质体。核心创新在于将属性约束生成与2D到3D重建结合，无需昂贵的3D训练数据即可保持岩石物理属性的可控性。该框架采用3D生成器与2D判别器的混合架构，通过多轴切片提取，从2D数据中学习3D一致性结构。孔隙率标签由增强型U-Net分割模型提取。方法在两种不同岩性的碳酸盐岩样本（白云岩-硬石膏与纯白云岩）上验证，生成结果成功捕捉了硬石膏夹杂物和细晶纹理等岩性特征。孔隙率控制的决定系数达到0.93，异质与均质样本的平均绝对误差分别为0.019和0.010。
原文摘要 · Abstract (English)
This study presents a conditional Generative Adversarial Network (cGAN) framework for generating 3D porous media volumes with controlled porosity, trained exclusively on 2D thin section images. The key innovation lies in combining property-conditioned generation with 2D-to-3D reconstruction, eliminating the need for expensive 3D training data while maintaining control over petrophysical properties. The framework employs a hybrid architecture with a 3D generator and 2D discriminator, where multi-axis slice extraction enables learning 3D-consistent structures from 2D training data. Porosity labels are extracted using an Enhanced U-Net segmentation model. The methodology was demonstrated on two carbonate samples with different lithologies: dolomite-anhydrite and pure dolomite. Results show that the framework successfully generates realistic 3D volumes capturing lithological features such as anhydrite inclusions and fine crystalline textures. Porosity control achieved an $R^2$ of 0.93, with mean absolute errors of 0.019 and 0.010 for the heterogeneous and homogeneous samples, respectively.
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