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Phase segmentation of X-ray computer tomography rock images using machine learning techniques: an accuracy and performance study (2016)

Copernicus

In: Solid Earth Discussions

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Publication Date: 2016-07-20

Description: 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 Solid Earth, 7, 1125-1139, doi:10.5194/se-7-1125-2016, 2016 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.

Electronic ISSN: 1869-9537

Topics: Geosciences

Published by Copernicus on behalf of The European Geosciences Union (EGU).

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