Enhancing image resolution of soils by stochastic multiscale image fusion

Karsanina, Marina V.; Gerke, Kirill M.; Скворцова, Е. Б.; Иванов, А. Л.; Mallants, Dirk

doi:10.1016/j.geoderma.2017.10.055

Cited by 61 publications

(22 citation statements)

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“…Also, different tillage systems lead to characteristically formed pores (Pires et al, 2017; Pires, Roque, Rosa, & Mooney, 2019). Using a hierarchical sampling scheme and combining the information from images from different sample sizes and resolution to derive a complete description of multiscale heterogeneity (Bacq‐Labreuil et al, 2018; Dal Ferro, Sartori, Simonetti, Berti, & Morari, 2014; Karsanina, Gerke, Skvortsova, Ivanov, & Mallants, 2018; Leuther, Schlüter, Wallach, & Vogel, 2019; Schlüter et al, 2018; Schlüter, Weller, & Vogel, 2011; Vogel, Weller, & Schlüter, 2010). By merging the pore size distribution (PSD) of several sample sizes (Vogel et al, 2010) a joint pore size distribution can be created while increasing the size range of visible porosity (ϕ‐vis).…”

Section: Introductionmentioning

confidence: 99%

Revealing pore connectivity across scales and resolutions with X‐ray CT

Lucas

Vetterlein

Vogel

et al. 2020

European J Soil Science

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Connectivity is one of the most important parameters to quantify pore structure and link it to soil functions. One of the great challenges in quantifying connectivity with X‐ray microtomography (X‐ray μCT) is that high resolution, as required for small pores, can only be achieved in small samples in which the connectivity of larger pores can no longer be quantified in a meaningful way. The objective of this study was to investigate the changes in pore connectivity with changing sample size, covering a range of analysed pore diameters of more than three orders of magnitude. With this approach, we wanted to address whether pore types formed by different processes in an agricultural chronosequence leave characteristic traces in certain connectivity metrics. The Euler number, χ, and the connection probability of two random points within the pore system, that is, the Γ‐indicator, were determined as a function of minimum pore diameter. The results show that characteristic signatures of certain pore types overlap with scale artifacts in the connectivity functions. The Γ‐indicator, gives highly biased information in small samples. Therefore, we developed a new method for a joint‐Γ‐curve that merges information from three samples sizes. However, χ does not require such a scale fusion. It can be used to define characteristic size ranges for pore types and is very sensitive to the occurrence of bottle necks. Our findings suggest a joint evaluation of both connectivity metrics to disentangle different pore types with χ and to identify the contribution of different pore types to the overall pore connectivity with Γ. This evaluation on the chronosequence showed that biopores mainly connect pores of diameters between 0.5 and 0.1 mm. This was not coupled with an increase in pore volume. In contrast, tillage led to a shift of pores of diameter >0.05 mm towards pores of diameter >0.20 mm and thus increased connectivity of pores >0.20 mm. This work underlines the importance of accounting for the scale dependence of connectivity measures and provides a methodological approach for doing so. Highlights Scale dependence of connectivity metrics needs to be accounted for. Connectivity metrics can be used to disentangle different pore types across scales. Roots mainly connect the pore system between 0.1 and 0.5 mm. A joint Γ‐connectivity function can be constructed that is free of scale artifacts.

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Section: Introductionmentioning

confidence: 99%

Revealing pore connectivity across scales and resolutions with X‐ray CT

Lucas

Vetterlein

Vogel

et al. 2020

European J Soil Science

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“…Firstly, such multiscale models can be obtained through the superimposition of low and high resolution 2D images or 3D volumes. These 2D images are then obtained by extrapolating high resolution structures to larger images through two-point correlation functions and simulated annealing [57][58][59][60][61]. 3D volumes can be obtained from 2D images through process-reconstruction [62], two-point statistical relations [63,64], simulated annealing [59,65], phase recovery [66,67], and multiple point geostatistics [68][69][70][71][72].…”

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confidence: 99%

Computed Tomography 3D Super-Resolution with Generative Adversarial Neural Networks: Implications on Unsaturated and Two-Phase Fluid Flow

2020

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Fluid flow characteristics are important to assess reservoir performance. Unfortunately, laboratory techniques are inadequate to know these characteristics, which is why numerical methods were developed. Such methods often use computed tomography (CT) scans as input but this technique is plagued by a resolution versus sample size trade-off. Therefore, a super-resolution method using generative adversarial neural networks (GANs) was used to artificially improve the resolution. Firstly, the influence of resolution on pore network properties and single-phase, unsaturated, and two-phase flow was analysed to verify that pores and pore throats become larger on average and surface area decreases with worsening resolution. These observations are reflected in increasingly overestimated single-phase permeability, less moisture uptake at lower capillary pressures, and high residual oil fraction after waterflooding. Therefore, the super-resolution GANs were developed which take low (12 µm) resolution input and increase the resolution to 4 µm, which is compared to the expected high-resolution output. These results better predicted pore network properties and fluid flow properties despite the overestimation of porosity. Relevant small pores and pore surfaces are better resolved thus providing better estimates of unsaturated and two-phase flow which can be heavily influenced by flow along pore boundaries and through smaller pores. This study presents the second case in which GANs were applied to a super-resolution problem on geological materials, but it is the first one to apply it directly on raw CT images and to determine the actual impact of a super-resolution method on fluid predictions.Materials 2020, 13, 1397 2 of 33 examples requires detailed knowledge on fluid storage and flow properties which could be estimated through experiments, either carried out in specialized laboratories or deduced from digital rocks. Laboratory measurements, however, require expensive and complex set-ups, take a long time to complete, are often destructive, do not always entirely capture the studied properties and on top of that, experiments often fail to produce reproducible results between laboratories [9,[12][13][14][15][16][17][18][19]. Therefore, numerical solutions have been developed allowing to compute fluid storage and flow properties on digital rocks [7,[20][21][22][23]. Such digital rocks are acquired through direct imaging or numerical simulations of rocks.Computed tomography (CT) is a technique to rapidly capture the three-dimensional structure of materials [22,24,25]. However, like any imaging technique, CT is plagued by a sample size versus resolution trade-off, which is adversely affecting fluid flow simulations. Both a high resolution and sufficiently large volume are required for accurate fluid flow estimations at the pore-scale [9,[26][27][28]. Resolution impacts how well pore network topology and pore surfaces are resolved. At lower resolution, i.e., lower resolving power, pore sizes are often overestimated and specific surf...

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“…However, this is challenging in practice because of the inherent limitations of MCT. More specifically, a trade-off should be made between the FOV and resolution of MCT images [5,6]. Actually, rock MCT images with a large FOV are typically of low resolution (LR), which causes increased difficulty of property analysis as many fine structures (e.g., pores) cannot be captured.…”

Section: Introductionmentioning

confidence: 99%

Super-resolution of real-world rock microcomputed tomography images using cycle-consistent generative adversarial networks

Chen

Teng

et al. 2020

Phys. Rev. E

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Digital rock imaging plays an important role in studying the microstructure and macroscopic properties of rocks, where microcomputed tomography (MCT) is widely used. Due to the inherent limitations of MCT, a balance should be made between the field of view (FOV) and resolution of rock MCT images-a large FOV at low resolution (LR) or a small FOV at high resolution (HR). However, large FOV and HR are both expected for reliable analysis results in practice. Super-resolution (SR) is an effective solution to break through the mutual restriction between the FOV and resolution of rock MCT images, for it can reconstruct an HR image from a LR observation. Most of the existing SR methods cannot produce satisfactory HR results on real-world rock MCT images. One of the main reasons for this is that paired images are usually needed to learn the relationship between LR and HR rock images. However, it is challenging to collect such a dataset in a real scenario. Meanwhile, the simulated datasets may be unable to accurately reflect the model in actual applications. To address these problems, we propose a cycle-consistent generative adversarial network (CycleGAN)-based SR approach for real-world rock MCT images, namely, SRCycleGAN. In the off-line training phase, a set of unpaired rock MCT images is used to train the proposed SRCycleGAN, which can model the mapping between rock MCT images at different resolutions. In the on-line testing phase, the resolution of the LR input is enhanced via the learned mapping by SRCycleGAN. Experimental results show that the proposed SRCycleGAN can greatly improve the quality of simulated and real-world rock MCT images. The HR images reconstructed by SRCycleGAN show good agreement with the targets in terms of both the visual quality and the statistical parameters, including the porosity, the local porosity distribution, the two-point correlation function, the lineal-path function, the two-point cluster function, the chord-length distribution function, and the pore size distribution. Large FOV and HR rock MCT images can be obtained with the help of SRCycleGAN. Hence, this work makes it possible to generate HR rock MCT images that exceed the limitations of imaging systems on FOV and resolution.

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Enhancing image resolution of soils by stochastic multiscale image fusion

Cited by 61 publications

References 50 publications

Revealing pore connectivity across scales and resolutions with X‐ray CT

Revealing pore connectivity across scales and resolutions with X‐ray CT

Computed Tomography 3D Super-Resolution with Generative Adversarial Neural Networks: Implications on Unsaturated and Two-Phase Fluid Flow

Super-resolution of real-world rock microcomputed tomography images using cycle-consistent generative adversarial networks

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