0000000000280252
AUTHOR
Faisal Khan
Processing of rock core microtomography images: Using seven different machine learning algorithms
The abilities of machine learning algorithms to process X-ray microtomographic rock images were determined. The study focused on the use of unsupervised, supervised, and ensemble clustering techniques, to segment X-ray computer microtomography rock images and to estimate the pore spaces and pore size diameters in the rocks. The unsupervised k-means technique gave the fastest processing time and the supervised least squares support vector machine technique gave the slowest processing time. Multiphase assemblages of solid phases (minerals and finely grained minerals) and the pore phase were found on visual inspection of the images. In general, the accuracy in terms of porosity values and pore…
Simultaneous segmentation and beam-hardening correction in computed microtomography of rock cores
We propose a post-reconstruction correction procedure for the beam-hardening artifact that neither requires knowledge of the X-ray spectrum nor of the attenuation coefficients in multi-mineral geologic samples. The beam-hardening artifact in polychromatic X-ray computer tomography (CT) hampers segmentation of the phase assemblage in geologic samples. We show that in cylindrically shaped samples like rock cores, the X-ray attenuation value for a single phase depends mainly on the distance from the center of the cylinder. This relationship could be easily extracted from the CT data for every phase and used to infer the presence of these phases at all positions in the sample. Our new approach …
From connected pathway flow to ganglion dynamics
During imbibition, initially connected oil is displaced until it is trapped as immobile clusters. While initial and final states have been well described before, here we image the dynamic transient process in a sandstone rock using fast synchrotron-based X-ray computed microtomography. Wetting film swelling and subsequent snap off, at unusually high saturation, decreases nonwetting phase connectivity, which leads to nonwetting phase fragmentation into mobile ganglia, i.e., ganglion dynamics regime. We find that in addition to pressure-driven connected pathway flow, mass transfer in the oil phase also occurs by a sequence of correlated breakup and coalescence processes. For example, meniscus…
Multi-phase classification by a least-squares support vector machine approach in tomography images of geological samples
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-squar…
Comparison of Micro X-ray Computer Tomography Image Segmentation Methods: Artificial Neural Networks Versus Least Square Support Vector Machine
Micro X-ray computer tomography (XCT) is a powerful non-destructive method for obtaining information about rock structures and mineralogy. A new methodology to obtain porosity from 2D XCT digital images using artificial neural network and least square support vector machine is demonstrated following these steps: the XCT image was first preprocessed, thereafter clustering algorithms such as K-means, Fuzzy c-means and self-organized maps was used for image segmentation. Then artificial neural network was applied for image classification. For comparison, least square support vector machine approach was used for classification labeling of the scan images. The methodology shows how artificial ne…