Processing of rock core microtomography images: Using seven different machine learning algorithms

Chauhan, Swarup; Rühaak, W.; Khan, Faisal; Enzmann, Frieder; Mielke, Philipp; Kersten, Michael; Sass, Ingo

doi:10.1016/j.cageo.2015.10.013

Cited by 95 publications

(60 citation statements)

References 25 publications

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“…Therefore, the presence of artefacts and inadequate data value selection for a specific mineral may affect correct image classification and may become computationally costly as the result of the higher dimensionality of the feature vector. In a companion paper, a comparison is presented of our LS-SVM method with other supervised and unsupervised machine learning techniques to demonstrate that it is best suited for µXCT image segmentation (Chauhan et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Multi-phase classification by a least-squares support vector machine approach in tomography images of geological samples

2016

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Abstract. Image processing of X-ray-computed polychromatic cone-beam micro-tomography (µXCT) data of geological samples mainly involves artefact reduction and phase segmentation. For the former, the main beam-hardening (BH) artefact is removed by applying a best-fit quadratic surface algorithm to a given image data set (reconstructed slice), which minimizes the BH offsets of the attenuation data points from that surface. A Matlab code for this approach is provided in the Appendix. The final BH-corrected image is extracted from the residual data or from the difference between the surface elevation values and the original grey-scale values. For the segmentation, we propose a novel least-squares support vector machine (LS-SVM, an algorithm for pixelbased multi-phase classification) approach. A receiver operating characteristic (ROC) analysis was performed on BHcorrected and uncorrected samples to show that BH correction is in fact an important prerequisite for accurate multiphase classification. The combination of the two approaches was thus used to classify successfully three different more or less complex multi-phase rock core samples.

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Section: Discussionmentioning

confidence: 99%

Multi-phase classification by a least-squares support vector machine approach in tomography images of geological samples

2016

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“…Effective porosity of andesite (17 ± 2 %) and Rotliegend sandstone (14 ± 2 %) was measure using a GeoPyc pycnometer (Micromeritics Instrument Corporation, Norcross, GA, USA). Thin-section analysis using a polarized microscope revealed andesite has a porphyritic texture with large plagioclase crystals (up to 3 mm in diameter), pyroxene in a cryptocrystalline matrix and isolated vesicles up to 6 mm in diameter (Chauhan et al, 2016). Rotliegend sandstone had different grain size (between 0.5 and 5 mm) of fine sand and gravel, with 26 % monocrystalline quartz, up to 35 % polycrystalline quartz, 8 % feldspar, 9 % sedimentary volcanic lithoclast grains and 13 % cement (Aretz et al, 2016).…”

Section: Experimental Approachmentioning

confidence: 99%

“…The performance of the k-means algorithm is strongly governed by the initial choice of the cluster centres. The k-means has the tendency to terminate without identifying the global minimum of the objective function (Chauhan et al, 2016). Therefore, it is recommended to run the algorithm several times to increase the likelihood that the global minimum of the objective function will be identified.…”

Section: Unsupervised Techniquesmentioning

confidence: 99%

“…In Chauhan et al (2016), we developed a workflow to segment XCT images using unsupervised, supervised and ensemble classifier ML techniques. The focus of this study is to assess the performance and accuracy of the above mentioned ML techniques to segment rock grain, matrix and pore phases in heterogeneous rock samples such as andesite, Berea sandstone, Rotliegend sandstone and a synthetic sample containing microporosities.…”

Section: Introductionmentioning

confidence: 99%

“…For a detailed description of SOMs the reader is referred to Kohonen (1990) and Chauhan et al (2016). The SOM procedure uses a competitive learning process based on an ANN framework.…”

Section: Unsupervised Techniquesmentioning

confidence: 99%

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

Chauhan

Rühaak

Anbergen³

et al. 2016

Solid Earth

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Abstract. Performance and accuracy of machine learning techniques to segment rock grains, matrix and pore voxels from a 3-D volume of X-ray tomographic (XCT) grayscale rock images was evaluated. The segmentation and classification capability of unsupervised (k-means, fuzzy c-means, self-organized maps), supervised (artificial neural networks, least-squares support vector machines) and ensemble classifiers (bragging and boosting) were tested using XCT images of andesite volcanic rock, Berea sandstone, Rotliegend sandstone and a synthetic sample. The averaged porosity obtained for andesite (15.8 ± 2.5 %), Berea sandstone (16.3 ± 2.6 %), Rotliegend sandstone (13.4 ± 7.4 %) and the synthetic sample (48.3 ± 13.3 %) is in very good agreement with the respective laboratory measurement data and varies by a factor of 0.2. The k-means algorithm is the fastest of all machine learning algorithms, whereas a least-squares support vector machine is the most computationally expensive. Metrics entropy, purity, mean square root error, receiver operational characteristic curve and 10 K-fold cross-validation were used to determine the accuracy of unsupervised, supervised and ensemble classifier techniques. In general, the accuracy was found to be largely affected by the feature vector selection scheme. As it is always a trade-off between performance and accuracy, it is difficult to isolate one particular machine learning algorithm which is best suited for the complex phase segmentation problem. Therefore, our investigation provides parameters that can help in selecting the appropriate machine learning techniques for phase segmentation.

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Calculating porosity and permeability from synthetic micro‐CT scan images based on a hybrid artificial intelligence

Mohyeddini

Rasaei

2023

Can J Chem Eng

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Nowadays, lattice Boltzmann is one of the standard and exact methods of simulation in micro‐CT images of rock. However, it has a high weakness in run time. Therefore, the effort in this article is to reach a comprehensive substitute method for permeability calculation with less run time than the lattice Boltzmann method. The other purposes are the automation of processing operations, preparation of images, and in the end, the calculation of porosity. The best way to achieve these outcomes is to use hybrid artificial intelligence. In this research work, comprehensive model architecture has been used to design a hybrid artificial intelligence to be able to calculate permeability and porosity in complex images. A thousand images were randomly generated with high complexity, which makes the model comprehensive and extensible, and image processing was applied. After that, the lattice Boltzmann method as the direct simulation was selected. Finally, the convolutional neural network and multilayer perceptron based on a new and comprehensive model were evaluated for the first time; the mean squared error resulting from the evaluation of training data is 0.01, and the test data is 0.03. Expert systems have been used as a subset of artificial intelligence for automated image processing and porosity calculation. In this way, problems related to the direct implementation of classical algorithms for image processing, models, and patterns related to machine learning and needing an expert were solved to an acceptable extent, and an error of less than 5% was achieved.

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Processing of rock core microtomography images: Using seven different machine learning algorithms

Cited by 95 publications

References 25 publications

Multi-phase classification by a least-squares support vector machine approach in tomography images of geological samples

Multi-phase classification by a least-squares support vector machine approach in tomography images of geological samples

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

Calculating porosity and permeability from synthetic micro‐CT scan images based on a hybrid artificial intelligence

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