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Performance and accuracy of machine learning techniques to segment rockgrains, matrix and pore voxels from a 3-D volume of X-ray tomographic (XCT)grayscale rock images was evaluated. The segmentation and classificationcapability of unsupervised (k-means, fuzzy c-means, self-organized maps),supervised (artificial neural networks, least-squares support vectormachines) and ensemble classifiers (bragging and boosting) were tested usingXCT images of andesite volcanic rock, Bereasandstone, Rotliegend sandstone and a synthetic sample. The averaged porosityobtained for andesite (15.8 ± 2.5 %), Berea sandstone(16.3 ± 2.6 %), Rotliegend sandstone (13.4 ± 7.4 %) andthe synthetic sample (48.3 ± 13.3 %) is in very good agreement withthe respective laboratory measurement data and varies by a factor of 0.2. Thek-means algorithm is the fastest of all machine learning algorithms,whereas a least-squares support vector machine is the most computationallyexpensive. Metrics entropy, purity, mean square root error, receiveroperational characteristic curve and 10 K-fold cross-validation were used todetermine the accuracy of unsupervised, supervised and ensemble classifiertechniques. In general, the accuracy was found to be largely affected by thefeature vector selection scheme. As it is always a trade-off betweenperformance and accuracy, it is difficult to isolate one particular machinelearning algorithm which is best suited for the complex phase segmentationproblem. Therefore, our investigation provides parameters that can help inselecting the appropriate machine learning techniques for phase segmentation

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