RESEARCH PAPER
Saturated water conductivity estimation based on X-ray CT images – evaluation of the impact of thresholding errors
1
Institute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland
Acceptance date: 2018-08-07
Publication date: 2019-02-13
Int. Agrophys. 2019, 33(1): 49-60
KEYWORDS
TOPICS
ABSTRACT
X-ray computed tomography soil studies rely on image analysis procedures that commonly begin with a thresholding step which is prone to errors and leads to uncertainty in the deduced values of soil characteristics, e.g., total porosity, specific surface or simulated saturated water conductivity. In this paper, four 3D images of soil cores were thresholded using two different algorithms. Total porosity and specific surface were determined for binarized images whereas saturated water conductivity was numerically estimated using the Navier-Stokes equation-based modelling. The study shows that an erroneous thresholding step leads to uncertainty in the determination of soil pore system characteristics and saturated water conductivity estimation. The lowest relative error in the total porosity determination, which was obtained in our study, was 15%, and the highest 40%. The results of this study demonstrate that errors related to thresholding may have a huge impact on the estimation of saturated hydraulic conductivity in soils, easily reaching a relative error of 50% of the saturated water conductivity reference value. Even small shifts in the threshold level can cause huge changes in saturated water conductivity estimation (a threshold shift by 6.7% for sample 2 analysed in the study caused more than a two-fold increase in the estimated value of saturated hydraulic conductivity).
REFERENCES (63)
1.
Andrä H., Combaret N., Dvorkin J., Glatt E., Han J., Kabel M., Keehm Y., Krzikalla F., Lee M., Madonna C., Marsh M., Mukerji T., Saenger E.H., Sain R., Saxena N., Ricker S., Wiegmann A., and Zhan X., 2013. Digital rock physics benchmarks-Part I: Imaging and segmentation. Comput. Geosci., 50, 25-32. doi:10.1016/j.cageo.2012.09.005.
2.
Baveye P.C., Laba M., Otten W., Bouckaert L., Dello Sterpaio P., Goswami R.R., Grinev D., Houston A., Hu Y., Liu J., Mooney S., Pajor R., Sleutel S., Tarquis A., Wang W., Wei Q., and Sezgin M., 2010. Observer-dependent variability of the thresholding step in the quantitative analysis of soil images and X-ray microtomography data. Geoderma, 157, 51–63. doi:10.1016/J.GEODERMA.2010.03.015.
3.
Beckers E., Plougonven E., Roisin C., Hapca S., Léonard A., and Degré A., 2014. X-ray microtomography: A porosity-based thresholding method to improve soil pore network characterization? Geoderma, 219-220, 145-154, doi:10.1016/j.geoderma.2014.01.004.
4.
Bultreys T., De Boever W., and Cnudde V., 2016. Imaging and image-based fluid transport modeling at the pore scale in geological materials: A practical introduction to the current state-of-the-art. Earth-Science Rev., 155, 93-128. doi:10.1016/J.EARSCIREV.2016.02.001.
5.
Bultreys T., Van Hoorebeke L., and Cnudde V., 2015. Multi-scale, micro-computed tomography-based pore network models to simulate drainage in heterogeneous rocks. Adv. Water Resour., 78, 36-49. doi:10.1016/J.ADVWATRES.02.003
6.
Chen X., Verma R., Espinoza D.N., and Prodanović M., 2018. Pore-scale determination of gas relative permeability in hydrate-bearing sediments using X-ray computed micro-tomography and lattice Boltzmann method. Water Resour. Res. 54, 600-608. doi:10.1002/2017WR021851.
7.
Daly K.R., Cooper L.J., Koebernick N., Evaristo J., Keyes S.D., van Veelen A., and Roose T., 2017. Modelling water dynamics in the rhizosphere. Rhizosphere, 4, 139-151. doi:10.1016/J.RHISPH.2017.10.004.
8.
Daly K.R., Tracy S.R., Crout N.M.J., Mairhofer S., Pridmore T.P., Mooney S.J., and Roose T., 2018. Quantification of root water uptake in soil using X-ray computed tomography and image-based modelling. Plant. Cell Environ., 41, 121-133. doi:10.1111/pce.12983.
9.
Doube M., Klosowski M.M., Arganda-Carreras I., Cordelières F.P., Dougherty R.P., Jackson J.S., Schmid B., Hutchinson J.R., and Shefelbine S.J., 2010. BoneJ: Free and extensible bone image analysis in Image J. Bone, 47, 1076-1079. doi:10.1016/j.bone.2010.08.023.
10.
Elliot T.R., Reynolds W.D., and Heck R.J., 2010. Use of existing pore models and X-ray computed tomography to predict saturated soil hydraulic conductivity. Geoderma, 156, 133-142. doi:10.1016/j.geoderma.2010.02.010.
11.
Gao J., Xing H., Rudolph V., Li Q., and Golding S.D., 2015. Parallel lattice Boltzmann computing and applications in core sample feature evaluation. Transp. Porous Media, 107, 65-77. doi:10.1007/s11242-014-0425-1.
12.
Gerke H.H., 2012. Macroscopic representation of the interface between flow domains in structured soil. Vadose Zo. J., 11, 0. doi:10.2136/vzj2011.0125.
13.
Hamby D.M., 1994. A review of techniques for parameter sensitivity analysis of environmental models. Environ. Monit. Assess., 32, 135-154. doi:10.1007/BF00547132.
14.
Hapca S.M., Houston A.N., Otten W., and Baveye P.C., 2013. New local thresholding method for soil images by minimizing grayscale intra-class variance. Vadose Zo. J., 12, 0. doi:10.2136/vzj2012.0172.
15.
Helliwell J.R., Sturrock C.J., Grayling K.M., Tracy S.R., Flavel R.J., Young I.M., Whalley W.R., and Mooney S.J., 2013. Applications of X-ray computed tomography for examining biophysical interactions and structural development in soil systems: a review. Eur. J. Soil Sci., 64, 279-297. doi:10.1111/ejss.12028.
16.
Houston A.N., Schmidt S., Tarquis A.M., Otten W., Baveye P.C., and Hapca S.M., 2013. Effect of scanning and image reconstruction settings in X-ray computed microtomography on quality and segmentation of 3D soil images. Geoderma, 207-208, 154-165. doi:10.1016/J.GEODERMA.05.017
17.
Hu X., Li Z.-C., Li X.-Y., and Liu Y., 2015. Influence of shrub encroachment on CT-measured soil macropore characteristics in the Inner Mongolia grassland of northern China. Soil Till. Res., 150, 1–9. doi:10.1016/j.still.2014.12.019.
18.
Iassonov P., Gebrenegus T., and Tuller M., 2009. Segmentation of X-ray computed tomography images of porous materials: A crucial step for characterization and quantitative analysis of pore structures. Water Resour. Res., 45. doi:10.1029/2009WR008087.
19.
Icardi M., Boccardo G., Marchisio D.L., Tosco T., and Sethi R., 2014. Pore-scale simulation of fluid flow and solute dispersion in three-dimensional porous media. Phys. Rev., E 90, 013032. doi:10.1103/PhysRevE.90.013032.
20.
Jarvis N., Larsbo M., and Koestel J., 2017. Connectivity and percolation of structural pore networks in a cultivated silt loam soil quantified by X-ray tomography. Geoderma, 287, 71-79. doi:10.1016/j.geoderma.2016.06.026.
21.
Jarvis N.J., 2007. A review of non-equilibrium water flow and solute transport in soil macropores: principles, controlling factors and consequences for water quality. Eur. J. Soil Sci., 58, 523-546. doi:10.1111/j.1365-2389.2007.00915.x.
22.
Jiang Z., van Dijke M.I.J., Geiger S., Ma J., Couples G.D., and Li X., 2017. Pore network extraction for fractured porous media. Adv. Water Resour., 107, 280-289. doi:10.1016/j.advwatres.2017.06.025.
23.
Jones B.D., and Feng Y.T., 2016. Effect of image scaling and segmentation in digital rock characterisation. Comput. Part. Mech., 3, 201-213. doi:10.1007/s40571-015-0077-0.
24.
Józefaciuk G., Czachor H., Lamorski K., Hajnos M., Świeboda R., and Franus W., 2015. Effect of humic acids, sesquioxides and silica on the pore system of silt aggregates measured by water vapour desorption, mercury intrusion and microtomography. Eur. J. Soil Sci., 66, 992-1001. doi:10.1111/ejss.12299.
25.
Katuwal S., Hermansen C., Knadel M., Moldrup P., Greve M.H., and de Jonge L.W., 2018. Combining X-ray computed tomography and visible near-infrared spectroscopy for prediction of soil structural properties. Vadose Zo. J., 17, 0. doi:10.2136/vzj2016.06.0054.
26.
Lamorski K., 2017. X-ray computational tomography facility - Institute of Agrophysics PAS [WWW Document]. URL http://tomography.ipan.lublin.... (accessed 4.19.18).
27.
Larsbo M., Koestel J., Kätterer T., and Jarvis N., 2016. Preferential transport in macropores is reduced by soil organic carbon. Vadose Zo. J. 15, 0. doi:10.2136/vzj2016.03.0021.
28.
Latief F.D.E., Fauzi U., Irayani Z., and Dougherty G., 2017. The effect of X-ray micro computed tomography image resolution on flow properties of porous rocks. J. Microsc., 266, 69-88. doi:10.1111/jmi.12521.
29.
Lesueur M., Casadiego M.C., Veveakis M., and Poulet T., 2017. Modelling fluid-microstructure interaction on elasto-visco-plastic digital rocks. Geomech. Energy Environ., 12, 1-13. doi:10.1016/J.GETE.2017.08.001.
30.
Liu J., Song R., and Cui M., 2015. Improvement of predictions of petrophysical transport behavior using three-dimensional finite volume element model with micro-CT images. J. Hydrodyn., Ser. B, 27, 234-241. doi:10.1016/S1001-6058(15)60477-2
31.
Liu Y., Wang H., Shen Z., and Song Y., 2013. Estimation of CO2 storage capacity in porous media by using X-ray micro-CT. Energy Procedia, 37, 5201-5208. doi:10.1016/J.EGYPRO.2013.06.436.
32.
Liu Z., and Wu H., 2016. Pore-scale modeling of immiscible two-phase flow in complex porous media. Appl. Therm. Eng., 93, 1394-1402. doi:10.1016/j.applthermaleng.2015.08.099.
33.
Martín-Sotoca J.J., Saa-Requejo A., Grau J.B., and Tarquis A.M., 2018. Local 3D segmentation of soil pore space based on fractal properties using singularity maps. Geoderma, 311, 175-188. doi:10.1016/J.GEODERMA.2016.11.029.
34.
McClure J.E., Prins J.F., and Miller C.T., 2014. A novel heterogeneous algorithm to simulate multiphase flow in porous media on multicore CPU-GPU systems. Comput. Phys. Commun., 185, 1865-1874. doi:10.1016/J.CPC.2014.03.012.
35.
Meakin P., and Tartakovsky A.M., 2009. Modeling and simulation of pore-scale multiphase fluid flow and reactive transport in fractured and porous media. Rev. Geophys., 47, RG3002. doi:10.1029/2008RG000263.
36.
Menke H.P., Bijeljic B., and Blunt M.J., 2017. Dynamic reservoir-condition microtomography of reactive transport in complex carbonates: Effect of initial pore structure and initial brine pH. Geochim. Cosmochim. Acta, 204, 267-285. doi:10.1016/j.gca.2017.01.053.
37.
Moreira A.C., Appoloni C.R., Mantovani I.F., Fernandes J.S., Marques L.C., Nagata R., and Fernandes C.P., 2012. Effects of manual threshold setting on image analysis results of a sandstone sample structural characterization by X-ray microtomography. Appl. Radiat. Isot., 70, 937-941. doi:10.1016/J.APRADISO.2012.03.001.
38.
Muljadi B.P., Blunt M.J., Raeini A.Q., and Bijeljic B., 2016. The impact of porous media heterogeneity on non-Darcy flow behaviour from pore-scale simulation. Adv. Water Resour., 95, 329-340. doi:10.1016/J.ADVWATRES.2015.05.019.
39.
Müller K., Katuwal S., Young I., McLeod M., Moldrup P., de Jonge L.W., and Clothier B., 2018. Characterising and linking X-ray CT derived macroporosity parameters to infiltration in soils with contrasting structures. Geoderma, 313, 82-91. doi:10.1016/j.geoderma.2017.10.020.
40.
Ngom N.F., Garnier P., Monga O., and Peth S., 2011. Extraction of three-dimensional soil pore space from microtomography images using a geometrical approach. Geoderma, 163, 127-134. doi:10.1016/j.geoderma.2011.04.013.
41.
Otsu N., 1979. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man. Cybern., 9, 62-66. doi:10.1109/TSMC.1979.4310076.
42.
Pereira Nunes J.P., Blunt M.J., and Bijeljic B., 2016. Pore-scale simulation of carbonate dissolution in micro-CT images. J. Geophys. Res. Solid Earth, 121, 558-576. doi:10.1002/2015JB012117.
43.
Porter M.L., and Wildenschild D., 2010. Image analysis algorithms for estimating porous media multiphase flow variables from computed microtomography data: a validation study. Comput. Geosci., 14, 15-30. doi:10.1007/s10596-009-9130-5.
44.
Rab M.A., Haling R.E., Aarons S.R., Hannah M., Young I.M., and Gibson D., 2014. Evaluation of X-ray computed tomography for quantifying macroporosity of loamy pasture soils. Geoderma, 213, 460-470. doi:10.1016/J.GEODERMA.2013.08.037.
45.
Ridler T.W. and Calvard S., 1978. Picture thresholding using an iterative selection method. IEEE Trans. Syst. Man Cybern., 8, 630-632. doi:10.1109/TSMC.1978.4310039.
46.
Saltelli A., Tarantola S., Campolongo F., and Ratto M., 2002. Sensitivity analysis in practice. John Wiley and Sons, Ltd, Chichester, UK. doi:10.1002/0470870958.
47.
Sander T., Gerke H.H., and Rogasik H., 2008. Assessment of Chinese paddy-soil structure using X-ray computed tomography. Geoderma, 145, 303-314. doi:10.1016/j.geoderma.2008.03.024.
48.
Schindelin J., Arganda-Carreras I., Frise E., Kaynig V., Longair M., Pietzsch T., Preibisch S., Rueden C., Saalfeld S., Schmid B., Tinevez J.-Y., White D.J., Hartenstein V., Eliceiri K., Tomancak P., and Cardona A., 2012. Fiji: an open-source platform for biological-image analysis. Nat. Methods, 9, 676-682. doi:10.1038/nmeth.2019.
49.
Schläuter S., Sheppard A., Brown K., and Wildenschild D., 2014. Image processing of multiphase images obtained via X-ray microtomography: A review. Water Resour. Res., 50, 3615-3639. doi:10.1002/2014WR015256.Received.
50.
Sezgin M., and Sankur B., 2004. Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging, 13, 146-165. doi:10.1117/1.1631316.
51.
Sleutel S., Cnudde V., Masschaele B., Vlassenbroek J., Dierick M., Van Hoorebeke L., Jacobs P., and De Neve S., 2008. Comparison of different nano- and micro-focus X-ray computed tomography set-ups for the visualization of the soil microstructure and soil organic matter. Comput. Geosci., 34, 931-938. doi:10.1016/j.cageo.2007.10.006.
52.
Smal P., Gouze P., and Rodriguez O., 2018. An automatic segmentation algorithm for retrieving sub-resolution porosity from X-ray tomography images. J. Pet. Sci. Eng., 166, 198-207. doi:10.1016/J.PETROL.2018.02.062.
53.
Smet S., Plougonven E., Leonard A., Degré A., and Beckers E., 2018. X-ray Micro-CT: how soil pore space description can be altered by image processing. Vadose Zo. J., 17, 0. doi:10.2136/vzj2016.06.0049.
54.
Starnoni M., Pokrajac D., and Neilson J.E., 2017. Computation of fluid flow and pore-space properties estimation on micro-CT images of rock samples. Comput. Geosci., 106, 118-129. doi:10.1016/j.cageo.2017.06.009.
55.
Than V. Du, Tang A.M., Roux J.-N., Pereira J.M., Aimedieu P., and Bornert M., 2017. Investigation into macroscopic and microscopic behaviors of wet granular soils using discrete element method and X-ray computed tomography. In: Powders and Grains, 8th Int. Conf. Micromechanics on Granular Media, July 3-7, Montpellier, France, doi:10.1051/epjconf/201714008018.
56.
Vaz C.M.P., de Maria I.C., Lasso P.O., and Tuller M., 2011. Evaluation of an advanced benchtop micro-computed tomography system for quantifying porosities and pore-size distributions of two Brazilian oxisols. Soil Sci. Soc. Am. J. 75, 832. doi:10.2136/sssaj2010.0245.
57.
Voltolini M., Kwon T.-H., and Ajo-Franklin J., 2017a. Visualization and prediction of supercritical CO2 distribution in sandstones during drainage: An in situ synchrotron X-ray micro-computed tomography study. Int. J. Greenh. Gas Control, 66, 230-245. doi:10.1016/J.IJGGC.2017.10.002.
58.
Voltolini M., Taş N., Wang S., Brodie E.L., and Ajo-Franklin J.B., 2017b. Quantitative characterization of soil micro-aggregates: New opportunities from sub-micron resolution synchrotron X-ray microtomography. Geoderma, 305, 382-393. doi:10.1016/J.GEODERMA.2017.06.005.
59.
Wang W., Kravchenko A.N., Smucker A.J.M., and Rivers M.L., 2011. Comparison of image segmentation methods in simulated 2D and 3D microtomographic images of soil aggregates. Geoderma, 162, 231-241. doi:10.1016/J.GEODERMA.2011.01.006.
60.
Wildenschild D., Hopmans J.W., Vaz C.M.P., Rivers M.L., and Rikard D., 2002. Using X-ray computed tomography in hydrology: systems, resolutions, and limitations. J. Hydrol., 267, 285-297.
61.
Wildenschild D., and Sheppard A.P., 2013. X-ray imaging and analysis techniques for quantifying pore-scale structure and processes in subsurface porous medium systems. Adv. Water Resour., 51, 217-246. doi:10.1016/j.advwatres.2012.07.018.
62.
Yang Y., Wu J., Zhao S., Han Q., Pan X., He F., and Chen C., 2018. Assessment of the responses of soil pore properties to combined soil structure amendments using X-ray computed tomography. Sci. Rep., 8, 695. doi:10.1038/s41598-017-18997-1.
63.
Zhang X., Crawford J.W., Flavel R.J., and Young I.M., 2016. A multi-scale Lattice Boltzmann model for simulating solute transport in 3D X-ray micro-tomography images of aggregated porous materials. J. Hydrol., 541, 1020-1029. doi:10.1016/j.jhydrol.2016.08.01.
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