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.
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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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...
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