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  • 1
    In: Vadose Zone Journal, Wiley, Vol. 11, No. 1 ( 2012-02)
    Abstract: Advancements in noninvasive imaging methods such as X‐ray computed tomography (CT) have led to a recent surge of applications in porous media research with objectives ranging from theoretical aspects of pore‐scale fluid and interfacial dynamics to practical applications such as enhanced oil recovery and advanced contaminant remediation. While substantial efforts and resources have been devoted to advance CT technology, microscale analysis, and fluid dynamics simulations, the development of efficient and stable three‐dimensional multiphase image segmentation methods applicable to large data sets is lacking. To eliminate the need for wet–dry or dual‐energy scans, image alignment, and subtraction analysis, commonly applied in X‐ray micro‐CT, a segmentation method based on a Bayesian Markov random field (MRF) framework amenable to true three‐dimensional multiphase processing was developed and evaluated. Furthermore, several heuristic and deterministic combinatorial optimization schemes required to solve the labeling problem of the MRF image model were implemented and tested for computational efficiency and their impact on segmentation results. Test results for three grayscale data sets consisting of dry glass beads, partially saturated glass beads, and partially saturated crushed tuff obtained with synchrotron X‐ray micro‐CT demonstrate great potential of the MRF image model for three‐dimensional multiphase segmentation. While our results are promising and the developed algorithm is stable and computationally more efficient than other commonly applied porous media segmentation models, further potential improvements exist for fully automated operation.
    Type of Medium: Online Resource
    ISSN: 1539-1663 , 1539-1663
    Language: English
    Publisher: Wiley
    Publication Date: 2012
    detail.hit.zdb_id: 2088189-7
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  • 2
    Online Resource
    Online Resource
    Copernicus GmbH ; 2016
    In:  Hydrology and Earth System Sciences Vol. 20, No. 10 ( 2016-10-06), p. 4017-4030
    In: Hydrology and Earth System Sciences, Copernicus GmbH, Vol. 20, No. 10 ( 2016-10-06), p. 4017-4030
    Abstract: Abstract. Prediction and modeling of localized flow processes in macropores is of crucial importance for sustaining both soil and water quality. However, currently there are no reliable means to predict preferential flow due to its inherently large spatial variability. The aim of this study was to investigate the predictive performance of previously developed empirical models for both water and air flow and to explore the potential applicability of X-ray computed tomography (CT)-derived macropore network characteristics. For this purpose, 65 cylindrical soil columns (6 cm diameter and 3.5 cm height) were extracted from the topsoil (5 cm to 8.5 cm depth) in a 15 m  ×  15 m grid from an agricultural field located in Silstrup, Denmark. All soil columns were scanned with an industrial X-ray CT scanner (129 µm resolution) and later employed for measurement of saturated hydraulic conductivity, air permeability at −30 and −100 cm matric potential, and gas diffusivity at −30 and −100 cm matric potential. Distribution maps for saturated hydraulic conductivity, air permeability, and gas diffusivity reflected no autocorrelation irrespective of soil texture and organic matter content. Existing empirical predictive models for saturated hydraulic conductivity and air permeability showed poor performance, as they were not able to realistically capture macropore flow. The tested empirical model for gas diffusivity predicted measurements at −100 cm matric potential reasonably well, but failed at −30 cm matric potential, particularly for soil columns with biopore-dominated flow. X-ray CT-derived macroporosity matched the measured air-filled porosity at −30 cm matric potential well. Many of the CT-derived macropore network characteristics were strongly interrelated. Most of the macropore network characteristics were also significantly correlated with saturated hydraulic conductivity, air permeability, and gas diffusivity. The predictive Ahuja et al. (1984) model for saturated hydraulic conductivity, air permeability, and gas diffusivity performed reasonably well when parameterized with novel, X-ray CT-derived parameters such as effective percolating macroporosity for biopore-dominated flow and total macroporosity for matrix-dominated flow. The obtained results further indicate that it is crucially important to discern between matrix-dominated and biopore-dominated flow for accurate prediction of macropore flow from X-ray CT-derived macropore network characteristics.
    Type of Medium: Online Resource
    ISSN: 1607-7938
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2016
    detail.hit.zdb_id: 2100610-6
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