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Articles | Volume 7, issue 4
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.
Special issue:
https://doi.org/10.5194/se-7-1125-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Articles | Volume 7, issue 4
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, Wolfram Rühaak, Hauke Anbergen, Alen Kabdenov, Marcus Freise, Thorsten Wille, and Ingo Sass

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