Digital rock analysis has become increasingly crucial in earth sciences and geological engineering. However, the multiscale characteristics of rock pores often exceed the capabilities of single-resolution imaging, which is inadequate for a comprehensive description of their characteristics. To address this issue, we introduce a novel multiscale rock image fusion framework based on a generative adversarial network (GAN). This method employs a 3D super-resolution convolutional neural network-based generator and a 2D discriminator to integrate low-resolution 3D images with high-resolution 2D images. Compared to existing methods, our approach directly generates high-resolution 3D data, which offers better continuity. Once trained, the generator can upscale low-resolution inputs to produce corresponding high-resolution outputs, thus completing the feature fusion of images with different resolutions. Experiments were conducted using two distinct datasets, encompassing both pore structure analysis and permeability simulation. The results indicate that the fused and reconstructed digital rocks closely resemble genuine digital rocks in terms of pore structure and flow properties. We have also expanded its application and achieved the fusion of 3D CT images with 2D SEM images. Furthermore, as the impact of low-resolution data decreases with increasing resolution difference. Therefore, it is recommended to select an appropriate scaling factor for effective fusion.

中文翻译：

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通过新颖的基于 GAN 的 2D-3D 图像融合框架增强数字岩石重建


数字岩石分析在地球科学和地质工程中变得越来越重要。然而，岩石孔隙的多尺度特征往往超过了单分辨率成像的能力，这对于全面描述其特征是不够的。为了解决这个问题，我们引入了一种基于生成对抗网络 （GAN） 的新型多尺度岩石图像融合框架。该方法采用基于 3D 超分辨率卷积神经网络的生成器和 2D 判别器将低分辨率 3D 图像与高分辨率 2D 图像集成在一起。与现有方法相比，我们的方法直接生成高分辨率的 3D 数据，从而提供更好的连续性。训练后，生成器可以放大低分辨率输入以产生相应的高分辨率输出，从而完成不同分辨率图像的特征融合。使用两个不同的数据集进行实验，包括孔隙结构分析和渗透率模拟。结果表明，熔融和重建的数字岩石在孔隙结构和流动特性方面与真正的数字岩石非常相似。我们还扩展了其应用，实现了 3D CT 图像与 2D SEM 图像的融合。此外，由于低分辨率数据的影响随着分辨率差异的增加而减小。因此，建议选择合适的缩放因子以实现有效融合。