Ahuja Vishal R, Gupta Utkarsh, Rapole Shivani R, Saxena Nishank, Hofmann Ronny, Day-Stirrat Ruarri J, Prakash Jaya, Yalavarthy Phaneendra K
IEEE Trans Image Process. 2022;31:3479-3493. doi: 10.1109/TIP.2022.3172211. Epub 2022 May 18.
Digital Rock Physics leverages advances in digital image acquisition and analysis techniques to create 3D digital images of rock samples, which are used for computational modeling and simulations to predict petrophysical properties of interest. However, the accuracy of the predictions is crucially dependent on the quality of the digital images, which is currently limited by the resolution of the micro-CT scanning technology. We have proposed a novel Deep Learning based Super-Resolution model called Siamese-SR to digitally boost the resolution of Digital Rock images whilst retaining the texture and providing optimal de-noising. The Siamese-SR model consists of a generator which is adversarially trained with a relativistic and a siamese discriminator utilizing Materials In Context (MINC) loss estimator. This model has been demonstrated to improve the resolution of sandstone rock images acquired using micro-CT scanning by a factor of 2. Another key highlight of our work is that for the evaluation of the super-resolution performance, we propose to move away from image-based metrics such as Structural Similarity (SSIM) and Peak Signal to Noise Ratio (PSNR) because they do not correlate well with expert geological and petrophysical evaluations. Instead, we propose to subject the super-resolved images to the next step in the Digital Rock workflow to calculate a crucial petrophysical property of interest, viz. porosity and use it as a metric for evaluation of our proposed Siamese-SR model against several other existing super-resolution methods like SRGAN, ESRGAN, EDSR and SPSR. Furthermore, we also use Local Attribution Maps to show how our proposed Siamese-SR model focuses optimally on edge-semantics, which is what leads to improvement in the image-based porosity prediction, the permeability prediction from Multiple Relaxation Time Lattice Boltzmann Method (MRTLBM) flow simulations as well as the prediction of other petrophysical properties of interest derived from Mercury Injection Capillary Pressure (MICP) simulations.
数字岩石物理利用数字图像采集和分析技术的进步来创建岩石样本的三维数字图像，这些图像用于计算建模和模拟，以预测感兴趣的岩石物理性质。然而，预测的准确性关键取决于数字图像的质量，而目前数字图像的质量受限于微CT扫描技术的分辨率。我们提出了一种基于深度学习的新型超分辨率模型，称为连体超分辨率（Siamese-SR），以在数字上提高数字岩石图像的分辨率，同时保留纹理并提供最佳去噪效果。连体超分辨率模型由一个生成器组成，该生成器通过相对论性判别器和连体判别器进行对抗训练，并利用上下文材料（MINC）损失估计器。该模型已被证明能够将使用微CT扫描获取的砂岩岩石图像的分辨率提高两倍。我们工作的另一个关键亮点是，对于超分辨率性能的评估，我们建议摒弃基于图像的指标，如结构相似性（SSIM）和峰值信噪比（PSNR），因为它们与专业地质和岩石物理评估的相关性不佳。相反，我们建议将超分辨率图像应用于数字岩石工作流程的下一步，以计算感兴趣的关键岩石物理性质，即孔隙率，并将其用作评估我们提出的连体超分辨率模型与其他几种现有超分辨率方法（如SRGAN、ESRGAN、EDSR和SPSR）的指标。此外，我们还使用局部归因图来展示我们提出的连体超分辨率模型如何最佳地聚焦于边缘语义，这就是在基于图像的孔隙率预测、基于多弛豫时间格子玻尔兹曼方法（MRTLBM）流动模拟的渗透率预测以及从压汞毛细管压力（MICP）模拟得出的其他感兴趣岩石物理性质的预测方面实现改进的原因。
