Articles | Volume 13, issue 9
https://doi.org/10.5194/se-13-1475-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-13-1475-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Detecting micro fractures: a comprehensive comparison of conventional and machine-learning-based segmentation methods
Dongwon Lee
CORRESPONDING AUTHOR
Faculty 2, Civil and Environmental Engineering,
Institute of Applied Mechanics (CE), Pfaffenwaldring 7, University of Stuttgart, 70569 Stuttgart, Germany
Nikolaos Karadimitriou
Faculty 2, Civil and Environmental Engineering,
Institute of Applied Mechanics (CE), Pfaffenwaldring 7, University of Stuttgart, 70569 Stuttgart, Germany
Matthias Ruf
Faculty 2, Civil and Environmental Engineering,
Institute of Applied Mechanics (CE), Pfaffenwaldring 7, University of Stuttgart, 70569 Stuttgart, Germany
Holger Steeb
Faculty 2, Civil and Environmental Engineering,
Institute of Applied Mechanics (CE), Pfaffenwaldring 7, University of Stuttgart, 70569 Stuttgart, Germany
SimTech, Stuttgart Center for Simulation Science, Pfaffenwaldring 5a, University of Stuttgart, 70569 Stuttgart, Germany
Related authors
No articles found.
Angelika Humbert, Veit Helm, Ole Zeising, Niklas Neckel, Matthias H. Braun, Shfaqat Abbas Khan, Martin Rückamp, Holger Steeb, Julia Sohn, Matthias Bohnen, and Ralf Müller
The Cryosphere, 19, 3009–3032, https://doi.org/10.5194/tc-19-3009-2025, https://doi.org/10.5194/tc-19-3009-2025, 2025
Short summary
Short summary
We study the evolution of a massive lake on the Greenland Ice Sheet using satellite and airborne data and some modelling. The lake is emptying rapidly. Water flows to the glacier's base through cracks and triangular-shaped moulins that remain visible over the years. Some of them become reactivated. We find features inside the glacier that stem from drainage events with a width of even 1 km. These features are persistent over the years, although they are changing in shape.
Cited articles
Acharya, T. and Ray, A. K.: Image Processing: Principles and Applications, John Wiley & Sons,
https://doi.org/10.1002/0471745790, 2005. a
Ahamed, B. B., Yuvaraj, D., and Priya, S. S.: Image Denoising with Linear and
Non-linear Filters, Proceedings of 2019 International Conference on
Computational Intelligence and Knowledge Economy, ICCIKE 2019, 10, 806–810,
https://doi.org/10.1109/ICCIKE47802.2019.9004429, 2019. a
Al-amri, S. S., Kalyankar, N. V., and Khamitkar, S. D.: Image segmentation by using
edge detection, Int. J. Comput. Sci. Eng.,
2, 804–807, 2010. a
Albawi, S., Mohammed, T. A., and Al-Zawi, S.: Understanding of a convolutional
neural network, in: Proceedings of 2017 International Conference on
Engineering and Technology, ICET 2017, IEEE, 2018,
1–6, https://doi.org/10.1109/ICEngTechnol.2017.8308186, 2018. a
Alqahtani, N., Alzubaidi, F., Armstrong, R. T., Swietojanski, P., and
Mostaghimi, P.: Machine learning for predicting properties of porous media
from 2d X-ray images, J. Petrol. Sci. Eng., 184,
106514, https://doi.org/10.1016/j.petrol.2019.106514, 2020. a
Alzubaidi, F., Makuluni, P., Clark, S. R., Lie, J. E., Mostaghimi, P., and
Armstrong, R. T.: Automatic fracture detection and characterization from
unwrapped drill-core images using mask R–CNN, J. Petrol. Sci. Eng., 208, 109471,
https://doi.org/10.1016/j.petrol.2021.109471, 2022. a
Amit, Y. and Geman, D.: Shape Quantization and Recognition with Randomized
Trees, Neural Comput., 9, 1545–1588, https://doi.org/10.1162/neco.1997.9.7.1545,
1997. a, b
Arena, A., Delle Piane, C., and Sarout, J.: A new computational approach to
cracks quantification from 2D image analysis: Application to micro-cracks
description in rocks, Comput. Geosci., 66, 106–120,
https://doi.org/10.1016/j.cageo.2014.01.007, 2014. a
Arganda-Carreras, I., Kaynig, V., Rueden, C., Eliceiri, K. W., Schindelin, J.,
Cardona, A., and Seung, H. S.: Trainable Weka Segmentation: A machine
learning tool for microscopy pixel classification, Bioinformatics, 33,
2424–2426, https://doi.org/10.1093/bioinformatics/btx180, 2017. a, b, c, d
Beanshell210: BeanShell 2.1.0, [code], https://beanshell.github.io/ (last access: 4 September 2022), 2020. a
Behrenbruch, C. P., Petroudi, S., Bond, S., Declerck, J. D., Leong, F. J., and
Brady, J. M.: Image filtering techniques for medical image post-processing:
An overview, Brit. J. Radiol., 77, 126–132, https://doi.org/10.1259/bjr/17464219,
2004. a, b
Berkowitz, B.: Analysis of Fracture Network Connectivity Using Percolation
Theory, Math. Geol., 27, 467–483, https://doi.org/10.1007/BF02084422, 1995. a
Beucher, S. and Meyer, F.: The Morphological Approach to Segmentation: The
Watershed Transformation, in: Mathematical morphology in image processing,
Vol. 18, p. 49, ISBN: 1351830503, 9781351830508, 1993. a
Bharodiya, A. K. and Gonsai, A. M.: An improved edge detection algorithm for
X-Ray images based on the statistical range, Heliyon, 5, e02743,
https://doi.org/10.1016/j.heliyon.2019.e02743, 2019. a
Buades, A., Coll, B., and Morel, J.-M.: Non-Local Means Denoising, Image
Processing On Line, 1, 208–212, https://doi.org/10.5201/ipol.2011.bcm_nlm, 2011. a
Canny, J.: A Computational Approach to Edge Detection, IEEE T.
Pattern Anal., 8, 679–698,
https://doi.org/10.1109/TPAMI.1986.4767851, 1986. a
Caselles, V., Catté, F., Coll, T., and Dibos, F.: A geometric model for
active contours in image processing, Numer. Math., 66, 1–31,
https://doi.org/10.1007/BF01385685, 1993. a, b
Chan, T. F. and Vese, L. A.: Active contours without edges, IEEE T. Image Process., 10, 266–277, https://doi.org/10.1109/83.902291, 2001. a, b
Chauhan, S., Rühaak, W., Khan, F., Enzmann, F., Mielke, P., Kersten, M.,
and Sass, I.: Processing of rock core microtomography images: Using seven
different machine learning algorithms, Comput. Geosci., 86,
120–128, https://doi.org/10.1016/j.cageo.2015.10.013, 2016. a
Chollet, F. and others: Keras, https://github.com/fchollet/keras (last access: 3 September 2022) [code], 2015. a
Christe, P., Bernasconi, M., Vontobel, P., Turberg, P., and Parriaux, A.:
Three-dimensional petrographical investigations on borehole rock samples: A
comparison between X-ray computed- and neutron tomography, Acta Geotech.,
2, 269–279, https://doi.org/10.1007/s11440-007-0045-9, 2007. a
Chung, S. Y., Kim, J. S., Stephan, D., and Han, T. S.: Overview of the use of
micro-computed tomography (micro-CT) to investigate the relation between the
material characteristics and properties of cement-based materials,
Constr. Build. Mater., 229, 116843,
https://doi.org/10.1016/j.conbuildmat.2019.116843, 2019. a
Coady, J., O'Riordan, A., Dooly, G., Newe, T., and Toal, D.: An overview of
popular digital image processing filtering operations, Proceedings of the
International Conference on Sensing Technology, ICST, 2019,
https://doi.org/10.1109/ICST46873.2019.9047683, 2019. a
Cracknell, M. J. and Reading, A. M.: Geological mapping using remote sensing
data: A comparison of five machine learning algorithms, their response to
variations in the spatial distribution of training data and the use of
explicit spatial information, Comput. Geosci., 63, 22–33,
https://doi.org/10.1016/j.cageo.2013.10.008, 2014. a
Crawford, B. R., Tsenn, M. C., Homburg, J. M., Stehle, R. C., Freysteinson,
J. A., and Reese, W. C.: Incorporating Scale-Dependent Fracture Stiffness
for Improved Reservoir Performance Prediction, Rock Mech. Rock
Eng., 50, 3349–3359, https://doi.org/10.1007/s00603-017-1314-z, 2017. a
Davis, C.: The norm of the Schur product operation, Numer. Math., 4,
343–344, https://doi.org/10.1007/BF01386329, 1962. a
De Kock, T., Boone, M. A., De Schryver, T., Van Stappen, J., Derluyn, H.,
Masschaele, B., De Schutter, G., and Cnudde, V.: A pore-scale study of
fracture dynamics in rock using X-ray micro-CT under ambient freeze-thaw
cycling, Environ. Sci. Technol., 49, 2867–2874,
https://doi.org/10.1021/es505738d, 2015. a
Delle Piane, C., Arena, A., Sarout, J., Esteban, L., and Cazes, E.:
Micro-crack enhanced permeability in tight rocks: An experimental and
microstructural study, Tectonophysics, 665, 149–156,
https://doi.org/10.1016/j.tecto.2015.10.001, 2015. a, b
Deng, H., Fitts, J. P., and Peters, C. A.: Quantifying fracture geometry with
X-ray tomography: Technique of Iterative Local Thresholding (TILT) for 3D
image segmentation, Comput. Geosci., 20, 231–244,
https://doi.org/10.1007/s10596-016-9560-9, 2016. a, b
Dhanachandra, N., Manglem, K., and Chanu, Y. J.: Image Segmentation Using
K-means Clustering Algorithm and Subtractive Clustering Algorithm, Procedia
Comput. Sci., 54, 764–771, https://doi.org/10.1016/j.procs.2015.06.090, 2015. a, b
Dong, Y., Li, P., Tian, W., Xian, Y., and Lu, D.: Journal of Natural Gas
Science and Engineering An equivalent method to assess the production
performance of horizontal wells with complicated hydraulic fracture network
in shale oil reservoirs, J. Nat. Gas Sci. Eng., 71,
102975, https://doi.org/10.1016/j.jngse.2019.102975, 2019. a
Drechsler, K. and Oyarzun Laura, C.: Comparison of vesselness functions for
multiscale analysis of the liver vasculature, in: Proceedings of the 10th
IEEE International Conference on Information Technology and Applications in
Biomedicine, 1–5, https://doi.org/10.1109/ITAB.2010.5687627, 2010. a
Dura, R. and Hart, P.: Pattern Classification and Scene Analysis, John Wiley
& Sons, first edn., ISBN: 0471223611, 9780471223610, 1973. a
Erdt, M., Raspe, M., and Suehling, M.: Automatic Hepatic Vessel Segmentation
Using Graphics Hardware, in: Medical Imaging and Augmented Reality, edited by:
Dohi, T., Sakuma, I., and Liao, H., Springer Berlin Heidelberg,
Berlin, Heidelberg, 403–412, ISBN: 978-3-540-79982-5, 2008. a
Fei, Y., Wang, K. C. P., Zhang, A., Chen, C., Li, J. Q., Liu, Y., Yang, G., and
Li, B.: Pixel-Level Cracking Detection on 3D Asphalt Pavement Images Through
Deep-Learning- Based CrackNet-V, IEEE T. Intell. Transp., 21, 273–284, https://doi.org/10.1109/TITS.2019.2891167, 2020. a
Frangakis, A. S. and Hegerl, R.: Noise reduction in electron tomographic
reconstructions using nonlinear anisotropic diffusion, J. Struct. Biol., 135, 239–250, https://doi.org/10.1006/jsbi.2001.4406, 2001. a
Frangi, A. F., Niessen, W. J., Vincken, K. L., and Viergever, M. A.: Multiscale vessel enhancement filtering, edited by: Wells, W. M., Colchester, A., and Delp, S., Medical Image Computing and Computer-Assisted Intervention – MICCAI’98, MICCAI 1998, Lecture Notes in Computer Science, vol 1496, Springer, Berlin, Heidelberg, https://doi.org/10.1007/BFb0056195, 1998. a
Fredrich, J. T. and Wong, T.-f.: Micromechanics of thermally induced cracking
in three crustal rocks, J. Geophys. Res.-Sol. Ea., 91,
12743–12764, https://doi.org/10.1029/JB091iB12p12743, 1986. a
Furat, O., Wang, M., Neumann, M., Petrich, L., Weber, M., Krill, C. E., and
Schmidt, V.: Machine learning techniques for the segmentation of tomographic
image data of functional materials, Front. Material., 6, 145,
https://doi.org/10.3389/fmats.2019.00145, 2019. a, b
Gastal, E. S. and Oliveira, M. M.: Domain transform for edge-aware image and
video processing, ACM T. Graphic., 30, 1–12,
https://doi.org/10.1145/1964921.1964964, 2011. a, b, c
Gastal, E. S. and Oliveira, M. M.: Adaptive manifolds for real-time
high-dimensional filtering, ACM T. Graphic., 31, 1–13,
https://doi.org/10.1145/2185520.2185529, 2012. a, b, c, d
Golub, G. H. and Van Loan, C. F.: Matrix Computations, The Johns Hopkins
University Press, third edn., ISBN: 0801830109, 9780801830105, 1996. a
Griffiths, L., Heap, M., Baud, P., and Schmittbuhl, J.: Quantification of
microcrack characteristics and implications for stiffness and strength of
granite, Int. J. Rock Mech. Min. Sci., 100,
138–150, https://doi.org/10.1016/j.ijrmms.2017.10.013, 2017. a
Halisch, M., Steeb, H., Henkel, S., and Krawczyk, C. M.: Pore-scale tomography and imaging: applications, techniques and recommended practice, Solid Earth, 7, 1141–1143, https://doi.org/10.5194/se-7-1141-2016, 2016. a
Healy, D., Rizzo, R. E., Cornwell, D. G., Farrell, N. J., Watkins, H., Timms,
N. E., Gomez-Rivas, E., and Smith, M.: FracPaQ: A MATLAB™ toolbox for the
quantification of fracture patterns, J. Struct. Geol., 95,
1–16, https://doi.org/10.1016/j.jsg.2016.12.003, 2017. a
Huang, N., Liu, R., Jiang, Y., Cheng, Y., and Li, B.: Shear-flow coupling
characteristics of a three-dimensional discrete fracture network-fault model
considering stress-induced aperture variations, J. Hydrol., 571,
416–424, https://doi.org/10.1016/j.jhydrol.2019.01.068, 2019. a
Jiang, T., Zhang, J., and Wu, H.: Experimental and numerical study on
hydraulic fracture propagation in coalbed methane reservoir, J. Nat. Gas Sci. Eng., 35, 455–467,
https://doi.org/10.1016/j.jngse.2016.08.077, 2016. a
Jing, Y., Armstrong, R. T., and Mostaghimi, P.: Rough-walled discrete fracture
network modelling for coal characterisation, Fuel, 191, 442–453,
https://doi.org/10.1016/j.fuel.2016.11.094, 2017. a
Jollife, I. T. and Cadima, J.: Principal component analysis: A review and
recent developments, Philos. T. Roy. Soc. A, 374, 2065,
https://doi.org/10.1098/rsta.2015.0202, 2016. a, b
Karimpouli, S., Tahmasebi, P., Ramandi, H. L., Mostaghimi, P., and Saadatfar,
M.: Stochastic modeling of coal fracture network by direct use of
micro-computed tomography images, Int. J. Coal Geol., 179,
153–163, https://doi.org/10.1016/j.coal.2017.06.002, 2017. a
Karimpouli, S., Tahmasebi, P., and Saenger, E. H.: Coal Cleat/Fracture
Segmentation Using Convolutional Neural Networks, Nat. Resour. Res., 29, 1675–1685, https://doi.org/10.1007/s11053-019-09536-y, 2019. a
Ketcham, R. A. and Hanna, R. D.: Beam hardening correction for X-ray computed
tomography of heterogeneous natural materials, Comput. Geosci., 67,
49–61, https://doi.org/10.1016/j.cageo.2014.03.003, 2014. a
Khryashchev, V., Ivanovsky, L., Pavlov, V., Ostrovskaya, A., and Rubtsov, A.:
Comparison of Different Convolutional Neural Network Architectures for
Satellite Image Segmentation, in: Conference of Open Innovation Association,
FRUCT, 2018, 172–179, https://doi.org/10.23919/FRUCT.2018.8588071,
2018. a
Kingma, D. P. and Ba, J.: Adam: A Method for Stochastic Optimization, arxiv [preprint], https://doi.org/10.48550/arXiv:1412.6980, 2017. a
Kodym, O. and Španěl, M.: Semi-automatic ct image segmentation
using random forests learned from partial annotations, in: BIOIMAGING 2018 –
5th International Conference on Bioimaging, Proceedings; Part of 11th
International Joint Conference on Biomedical Engineering Systems and
Technologies, BIOSTEC 2018, 124–131,
https://doi.org/10.5220/0006588801240131, 2018. a
Kumari, W. G., Ranjith, P. G., Perera, M. S., and Chen, B. K.: Experimental
investigation of quenching effect on mechanical, microstructural and flow
characteristics of reservoir rocks: Thermal stimulation method for geothermal
energy extraction, J. Petrol. Sci. Eng., 162,
419–433, https://doi.org/10.1016/j.petrol.2017.12.033, 2018. a
Lai, J., Wang, G., Fan, Z., Chen, J., Qin, Z., Xiao, C., Wang, S., and Fan, X.:
Three-dimensional quantitative fracture analysis of tight gas sandstones
using industrial computed tomography, Sci. Rep., 7, 1–12,
https://doi.org/10.1038/s41598-017-01996-7, 2017. a
Lei, Q. and Gao, K.: Correlation Between Fracture Network Properties and Stress
Variability in Geological Media, Geophys. Res. Lett., 45,
3994–4006, https://doi.org/10.1002/2018GL077548, 2018. a
Li, P., Xia, H., Zhou, B., Yan, F., and Guo, R.: A Method to Improve the
Accuracy of Pavement Crack Identification by Combining a Semantic
Segmentation and Edge Detection Model, Appl. Sci., 12, 4714,
https://doi.org/10.3390/app12094714, 2022. a
Li, S., Song, W., Fang, L., Chen, Y., Ghamisi, P., and Benediktsson, J. A.:
Deep learning for hyperspectral image classification: An overview, IEEE T. Geosci. Remote, 57, 6690–6709,
https://doi.org/10.1109/TGRS.2019.2907932, 2019. a
Li, S. Z. and Jain, A. (Eds.): Local Adaptive Thresholding,
Springer US, Boston, MA, 939–939, https://doi.org/10.1007/978-0-387-73003-5_506, 2009. a
Lissa, S., Ruf, M., Steeb, H., and Quintal, B.: Effects of crack roughness on
attenuation caused by squirt flow in Carrara marble, in: SEG Technical
Program Expanded Abstracts 2020, Society of Exploration Geophysicists,
https://doi.org/10.1190/segam2020-3427789.1, 2020. a
Lissa, S., Ruf, M., Steeb, H., and Quintal, B.: Digital rock physics applied to
squirt flow, Geophysics, 86, MR235, https://doi.org/10.1190/geo2020-0731.1, 2021. a
Long, J., Shelhamer, E., and Darrell, T.: Fully Convolutional Networks for
Semantic Segmentation, IEEE T. Pattern Anal., 39, 640–651, https://doi.org/10.1109/TPAMI.2016.2572683, 2017. a
MacQueen, J.: Some methods for classification and analysis of multivariate
observations, in: Proceedings of the Fifth Berkeley Symposium on Mathematical
Statistics and Probability, Volume 1: Statistics, University of
California Press, Berkeley, Calif., 281–297,
https://projecteuclid.org/euclid.bsmsp/1200512992 (last access: 4 September 2022), 1967. a
Marr, D. and Hildreth, E.: Theory of edge detection, P. R. Soc. London, 207, 187–217,
https://doi.org/10.1098/rspb.1980.0020, 1980. a
Maurer, C. R., Qi, R., and Raghavan, V.: A linear time algorithm for computing
exact Euclidean distance transforms of binary images in arbitrary
dimensions, IEEE T. Pattern Anal.,
25, 265–270, https://doi.org/10.1109/TPAMI.2003.1177156, 2003. a
Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., and
Terzopoulos, D.: Image Segmentation Using Deep Learning: A Survey,
1–23, http://arxiv.org/abs/2001.05566 (last access: 4 September 2022), 2020. a
Mumford, D. and Shah, J.: Optimal approximations by piecewise smooth functions
and associated variational problems, Commun. Pure Appl. Math., 42, 577–685, https://doi.org/10.1002/cpa.3160420503, 1989. a
Natick, Massachusetts: MATLAB version 9.4.0.813654 (R2018a), [code], https://www.mathworks.com/ (last access: 4 September 2022), The Mathworks, Inc., 2018. a
Nguyen, T. S., Avila, M., and Begot, S.: Automatic detection and classification
of defect on road pavement using anisotropy measure, in: 2009 17th European
Signal Processing Conference, 617–621, 2009. a
Osher, S. and Tsai, R.: Level Set Methods and Their Applications in Image
Science, Commun. Math. Sci., 1, 1–20,
https://doi.org/10.4310/cms.2003.v1.n4.a1, 2003. a
Palafox, L. F., Hamilton, C. W., Scheidt, S. P., and Alvarez, A. M.: Automated
detection of geological landforms on Mars using Convolutional Neural
Networks, Comput. Geosci., 101, 48–56,
https://doi.org/10.1016/j.cageo.2016.12.015, 2017. a
Peacock, S., McCann, C., Sothcott, J., and Astin, T.: Seismic velocities in
fractured rocks: an experimental verification of Hudson's theory,
Geophys. Prospect., 42, 27–80,
https://doi.org/10.1111/j.1365-2478.1994.tb00193.x, 1994. a
Pearson, K.: LIII. On lines and planes of closest fit to systems of points in
space, The London, Edinburgh, and Dublin Philosophical Magazine and Journal
of Science, 2, 559–572, 1901. a
Perona, P. and Malik, J.: Scale-Space and Edge Detection Using Anisotropic
Diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence,
12, 629–639, https://doi.org/10.1109/34.56205, 1990. a
Python: Python 3.7.7, [code], https://www.python.org/ (last access: 4 September 2022), Python Software Foundation, 2020. a
Pieri, M., Burlini, L., Kunze, K., Stretton, I., and Olgaard, D. L.:
Rheological and microstructural evolution of Carrara marble with high shear
strain: results from high temperature torsion experiments, J. Struct. Geol., 23, 1393–1413, https://doi.org/10.1016/S0191-8141(01)00006-2,
2001. a
Pimienta, L., Orellana, L. F., and Violay, M.: Variations in Elastic and
Electrical Properties of Crustal Rocks With Varying Degree of
Microfracturation, J. Geophys. Res.-Sol. Ea., 124,
6376–6396, https://doi.org/10.1029/2019jb017339, 2019. a
Poulose, M.: Literature Survey on Image Deblurring Techniques, International
J. Comput. Appl. Tech. Res., 2, 286–288,
https://doi.org/10.7753/ijcatr0203.1014, 2013. a
Ramandi, H. L., Mostaghimi, P., and Armstrong, R. T.: Digital rock analysis
for accurate prediction of fractured media permeability, J. Hydrol., 554, 817–826, https://doi.org/10.1016/j.jhydrol.2016.08.029, 2017. a, b, c
Rezaie, A., Achanta, R., Godio, M., and Beyer, K.: Comparison of crack
segmentation using digital image correlation measurements and deep learning,
Constr. Build. Mater., 261, 120474,
https://doi.org/10.1016/j.conbuildmat.2020.120474, 2020. a
Roberts, G., Haile, S. Y., Sainju, R., Edwards, D. J., Hutchinson, B., and Zhu,
Y.: Deep Learning for Semantic Segmentation of Defects in Advanced STEM
Images of Steels, Sci. Rep., 9, 1–12,
https://doi.org/10.1038/s41598-019-49105-0, 2019. a
Ronneberger, O., Fischer, P., and Brox, T.: U-net: Convolutional networks for
biomedical image segmentation, Lecture Notes in Computer Science (including
subseries Lecture Notes in Artificial Intelligence and Lecture Notes in
Bioinformatics), 9351, 234–241, https://doi.org/10.1007/978-3-319-24574-4_28, 2015. a, b
Ruf, M. and Steeb, H.: micro-XRCT data set of Carrara marble with
artificially created crack network: fast cooling down from 600 ∘C, [data set],
https://doi.org/10.18419/DARUS-682, 2020b. a, b, c, d
Saenger, E. H., Vialle, S., Lebedev, M., Uribe, D., Osorno, M., Duda, M., and Steeb, H.: Digital carbonate rock physics, Solid Earth, 7, 1185–1197, https://doi.org/10.5194/se-7-1185-2016, 2016. a
Salman, N.: Image Segmentation Based on Watershed and Edge Detection
Techniques, The International Arab Journal of Information Technology, 3,
104–110, 2006. a
Sarout, J., Cazes, E., Delle Piane, C., Arena, A., and Esteban, L.:
Stress-dependent permeability and wave dispersion in tight cracked rocks:
Experimental validation of simple effective medium models, J. Geophys. Res.-Sol. Ea., 122, 6180–6201,
https://doi.org/10.1002/2017jb014147, 2017. a
Sato, Y., Nakajima, S., Atsumi, H., Roller, T., Gerig, G., Yoshida, S., and
Kikinis, R.: 3D Multi-Scale Line Filter for Segmentation and Visualization
of Curvilinear Structures in Medical Images, Lecture Notes in Computer
Science (including subseries Lecture Notes in Artificial Intelligence and
Lecture Notes in Bioinformatics), 1205, 213–222, https://doi.org/10.1007/bfb0029240,
1997. a
Sheppard, A. P., Sok, R. M., and Averdunk, H.: Techniques for image
enhancement and segmentation of tomographic images of porous materials,
Physica A, 339, 145–151,
https://doi.org/10.1016/j.physa.2004.03.057, 2004. a, b
Shorten, C. and Khoshgoftaar, T. M.: A survey on Image Data Augmentation for
Deep Learning, J. Big Data, 6, 60, https://doi.org/10.1186/s40537-019-0197-0,
2019. a
Simonyan, K. and Zisserman, A.: Very Deep Convolutional Networks for
Large-Scale Image Recognition, https://doi.org/10.48550/ARXIV.1409.1556, 2014. a
Singh, S. P., Wang, L., Gupta, S., Goli, H., Padmanabhan, P., and Gulyás,
B.: 3D Deep Learning on Medical Images: A Review,
http://arxiv.org/abs/2004.00218 (last access: 4 September 2022), 2020. a
Su, T. C., Yang, M. D., Wu, T. C., and Lin, J. Y.: Morphological segmentation
based on edge detection for sewer pipe defects on CCTV images, Expert
Syst. Appl., 38, 13094–13114,
https://doi.org/10.1016/j.eswa.2011.04.116, 2011. a
Suzuki, A., Miyazawa, M., Okamoto, A., Shimizu, H., Obayashi, I., Hiraoka, Y.,
Tsuji, T., Kang, P., and Ito, T.: Inferring fracture forming processes by
characterizing fracture network patterns with persistent homology, Comput. Geosci., 143, 104550,
https://doi.org/10.1016/j.cageo.2020.104550, 2020.
a
Taylor, H. F., O'Sullivan, C., and Sim, W. W.: A new method to identify void
constrictions in micro-CT images of sand, Comput. Geosci., 69,
279–290, https://doi.org/10.1016/j.compgeo.2015.05.012, 2015. a, b, c
van Santvoort, J. and Golombok, M.: Improved recovery from fractured oil
reservoirs, J. Petrol. Sci. Eng., 167, 28–36,
https://doi.org/10.1016/j.petrol.2018.04.002, 2018. a
Vincent, L. and Dougherty, E. R.: Morphological Segmentation for Textures and
Particles, Digital Image Processing Methods, 43–102,
https://doi.org/10.1201/9781003067054-2, 1994. a
Voorn, M., Exner, U., and Rath, A.: Multiscale Hessian fracture filtering for
the enhancement and segmentation of narrow fractures in 3D image data,
Comput. Geosci., 57, 44–53, https://doi.org/10.1016/j.cageo.2013.03.006,
2013. a, b, c
Weerakone, W. M. and Wong, R. C.: Characterization of Variable Aperture Rock
Fractures Using X-ray Computer Tomography, in: Advances in X-ray Tomography
for Geomaterials, Wiley Online Libary, 229–235, https://doi.org/10.1002/9780470612187.ch21, 2010. a, b
Wetzstein, G., Lanman, D., Hirsch, M., and Raskar, R.: Supplementary Material:
Tensor Displays: Compressive Light Field Synthesis using Multilayer Displays
with Directional Backlighting A Additional Details on Nonnegative Matrix and
Tensor Factorization, ACM T. Graphic., 31, 1–11, https://doi.org/10.1145/2185520.2185576, 2012. a
Xing, C., Huang, J., Xu, Y., Shu, J., and Zhao, C.: Research on crack
extraction based on the improved tensor voting algorithm, Arab. J. Geosci., 11, 342, https://doi.org/10.1007/s12517-018-3676-2, 2018. a
Yamaguchi, T. and Hashimoto, S.: Fast crack detection method for large-size
concrete surface images using percolation-based image processing, Mach. Vision Appl., 21, 797–809, https://doi.org/10.1007/s00138-009-0189-8, 2010. a
Zhang, G., Ranjith, P. G., Perera, M. S., Haque, A., Choi, X., and Sampath,
K. S.: Characterization of coal porosity and permeability evolution by
demineralisation using image processing techniques: A micro-computed
tomography study, J. Nat. Gas Sci. Eng., 56,
384–396, https://doi.org/10.1016/j.jngse.2018.06.020, 2018. a
Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., and Liang, J.: UNet++:
Redesigning Skip Connections to Exploit Multiscale Features in Image
Segmentation, IEEE T. Med. Imag., 39, 1856–1867,
https://doi.org/10.1109/tmi.2019.2959609, 2019. a
Short summary
This research article focuses on filtering and segmentation methods employed in high-resolution µXRCT studies for crystalline rocks, bearing fractures, or fracture networks, of very small aperture. Specifically, we focus on the identification of artificially induced (via quenching) fractures in Carrara marble samples. Results from the same dataset from all five different methods adopted were produced and compared with each other in terms of their output quality and time efficiency.
This research article focuses on filtering and segmentation methods employed in high-resolution...