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Volume 7, issue 4
Solid Earth, 7, 1125-1139, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Pore-scale tomography & imaging - applications, techniques...

Solid Earth, 7, 1125-1139, 2016
© Author(s) 2016. This work is distributed under
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 et al.
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Cited articles  
Amigó, E., Gonzalo, J., Artiles, J., and Verdejo, F.: A comparison of extrinsic clustering evaluation metrics based on formal constraints, Inform. Retrieval, 12, 461–486, 2009.
Aretz, A., Bär, K., Götz, A. E., and Sass, I.: Outcrop analogue study of Permocarboniferous geo-thermal sandstone reservoir formations (northern Upper Rhine Graben, Germany): impact of mineral content, depositional environment and diagenesis on petrophysical properties, Int. J. Earth Sci., 105, 1431–1452, 2016.
Bradley, A. P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern Recogn., 30, 1145–1159. 1997.
Breiman, L.: Bagging predictors, Mach. Lear., 24, 123–140, 1996.
Publications Copernicus
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...