Siamese-SR: A Siamese Super-Resolution Model for Boosting Resolution of Digital Rock Images for Improved Petrophysical Property Estimation

Ahuja, VR and Gupta, U and Rapole, SR and Saxena, N and Hofmann, R and Day-Stirrat, RJ and Prakash, J and Yalavarthy, PK (2022) Siamese-SR: A Siamese Super-Resolution Model for Boosting Resolution of Digital Rock Images for Improved Petrophysical Property Estimation. In: IEEE Transactions on Image Processing, 31 . pp. 3479-3493.

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Abstract

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.

Item Type:	Journal Article
Publication:	IEEE Transactions on Image Processing
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to the Institute of Electrical and Electronics Engineers Inc.
Keywords:	Computational fluid dynamics; Computerized tomography; Deep learning; E-learning; Forecasting; Generative adversarial networks; Geology; Image acquisition; Optical resolving power; Porosity; Rocks; Semantics; Signal to noise ratio, Deep learning; Digital rock physic; Generator; Image super resolutions; Local attribution map; Petrophysics; Predictive models; Rock physics; Siamese network; Signal resolution; Superresolution, Image enhancement
Department/Centre:	Division of Interdisciplinary Sciences > Computational and Data Sciences Division of Physical & Mathematical Sciences > Instrumentation Appiled Physics
Date Deposited:	05 Jul 2022 11:43
Last Modified:	05 Jul 2022 11:43
URI:	https://eprints.iisc.ac.in/id/eprint/74150

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