Articles | Volume 7, issue 4
https://doi.org/10.5194/se-7-1125-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/se-7-1125-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
19 Jul 2016
Research article |
| 19 Jul 2016
Phase segmentation of X-ray computer tomography rock images using machine learning techniques: an accuracy and performance study
Swarup Chauhan
Department of Geothermal Science and Technology, Institute of Applied
Geosciences, Technische Universität Darmstadt, Darmstadt, Germany
Wolfram Rühaak
CORRESPONDING AUTHOR
Department of Geothermal Science and Technology, Institute of Applied
Geosciences, Technische Universität Darmstadt, Darmstadt, Germany
Darmstadt Graduate School of Excellence Energy Science and
Engineering, Technische Universität Darmstadt, Darmstadt, Germany
Hauke Anbergen
APS Antriebs-, Prüf- und Steuertechnik GmbH, Göttingen,
Rosdorf, Germany
Alen Kabdenov
APS Antriebs-, Prüf- und Steuertechnik GmbH, Göttingen,
Rosdorf, Germany
Marcus Freise
APS Antriebs-, Prüf- und Steuertechnik GmbH, Göttingen,
Rosdorf, Germany
Thorsten Wille
APS Antriebs-, Prüf- und Steuertechnik GmbH, Göttingen,
Rosdorf, Germany
Ingo Sass
Department of Geothermal Science and Technology, Institute of Applied
Geosciences, Technische Universität Darmstadt, Darmstadt, Germany
Darmstadt Graduate School of Excellence Energy Science and
Engineering, Technische Universität Darmstadt, Darmstadt, Germany
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- Quantitative assessment of machine-learning segmentation of battery electrode materials for active material quantification J. Bailey et al. 10.1016/j.jpowsour.2022.232503
- A Hybrid Artificial Intelligence Model to Predict the Elastic Behavior of Sandstone Rocks . Gowida et al. 10.3390/su11195283
- Comparison of Methods to Segment Variable-Contrast XCT Images of Methane-Bearing Sand Using U-Nets Trained on Single Dataset Sub-Volumes F. Alvarez-Borges et al. 10.3390/methane2010001
- Artificial Intelligence as a Tool to Study the 3D Skeletal Architecture in Newly Settled Coral Recruits: Insights into the Effects of Ocean Acidification on Coral Biomineralization F. Scucchia et al. 10.3390/jmse10030391
- Predictive Soft Computing Methods for Building Digital Rock Models Verified by Positron Emission Tomography Experiments M. Ebadi et al. 10.1029/2021WR031814
- Three-dimensional nanostructure analysis of non-stained Nafion in fuel-cell electrode by combined ADF-STEM tomography T. UBE 10.1093/jmicro/dfae002
- Automatic fracture characterization in CT images of rocks using an ensemble deep learning approach C. Pham et al. 10.1016/j.ijrmms.2023.105531
- An Application of Deep Neural Networks for Segmentation of Microtomographic Images of Rock Samples I. Varfolomeev et al. 10.3390/computers8040072
- Emergent Properties of Microbial Activity in Heterogeneous Soil Microenvironments: Different Research Approaches Are Slowly Converging, Yet Major Challenges Remain P. Baveye et al. 10.3389/fmicb.2018.01929
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- A Simplified Convolutional Network for Soil Pore Identification Based on Computed Tomography Imagery Q. Han et al. 10.2136/sssaj2019.04.0119
- Automatic batch recognition of rock deformation areas based on image segmentation methods C. Yuan et al. 10.3389/feart.2022.1093764
- Comparing petrophysical properties and pore network characteristics of carbonate reservoir rocks using micro X-ray tomography imaging and microfacies analyses H. Alatrash & F. Velledits 10.1007/s13137-023-00243-8
- Application of machine learning techniques in mineral phase segmentation for X-ray microcomputed tomography (µCT) data P. Guntoro et al. 10.1016/j.mineng.2019.105882
- Automatic classification of plutonic rocks with deep learning G. Alférez et al. 10.1016/j.acags.2021.100061
- The emerging role of 4D synchrotron X-ray micro-tomography for climate and fossil energy studies: five experiments showing the present capabilities at beamline 8.3.2 at the Advanced Light Source M. Voltolini et al. 10.1107/S1600577517012449
- A neighborhood median weighted fuzzy c-means method for soil pore identification Q. HAN et al. 10.1016/S1002-0160(21)60034-6
- The Sensitivity of Estimates of Multiphase Fluid and Solid Properties of Porous Rocks to Image Processing G. Garfi et al. 10.1007/s11242-019-01374-z
- X-ray Microcomputed Tomography (µCT) for Mineral Characterization: A Review of Data Analysis Methods P. Guntoro et al. 10.3390/min9030183
- Permeability upscaling in complex carbonate samples using textures of micro-computed tomography images M. Jouini et al. 10.1080/02286203.2019.1596728
- Research on Intelligent Identification of Rock Types Based on Faster R-CNN Method X. Liu et al. 10.1109/ACCESS.2020.2968515
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2 citations as recorded by crossref.
Latest update: 25 Apr 2024
Short summary
Machine learning techniques are a promising alternative for processing (phase segmentation) of 3-D X-ray computer tomographic rock images. Here the performance and accuracy of different machine learning techniques are tested. The aim is to classify pore space, rock grains and matrix of four distinct rock samples. The porosity obtained based on the segmented XCT images is cross-validated with laboratory measurements. Accuracies of the different methods are discussed and recommendations proposed.
Machine learning techniques are a promising alternative for processing (phase segmentation) of...
