A technique for determining the parameter values from experimental images for the porous structures generator for synthesizing porous phantoms is presented. The algorithms for fast determination of porosity and the standard deviation of the Gaussian filter used as input parameters of the phantom generator are considered. The phantoms generated according to the found parameters have geometric characteristics similar to the original images, which makes it possible to use such phantoms both for studying and modeling processes in porous media and as basic structures for creating training samples for segmentation algorithms of experimental images using machine learning methods..
Microtomography is a powerful method of materials investigation. It enables to obtain physical properties of porous media non-destructively that is useful in studies. One of the application ways is a calculation of porosity, pore sizes, surface area, and other parameters of metal-ceramic (cermet) membranes which are widely spread in the filtration industry. The microtomography approach is efficient because all of those parameters are calculated simultaneously in contrast to the conventional techniques. Nevertheless, the calculations on Micro-CT reconstructed images appear to be time-consuming, consequently representative volume element should be chosen to speed them up. This research sheds light on representative elementary volume identification without consideration of any physical parameters such as porosity, etc. Thus, the volume element could be found even in noised and grayscale images. The proposed method is flexible and does not overestimate the volume size in the case of anisotropic samples. The obtained volume element could be used for computations of the domain’s physical characteristics if the image is filtered and binarized, or for selections of optimal filtering parameters for denoising procedure.
Nowadays, microtomography experiments require a lot of time to collect data and process it. In order to observe realtime processes (e.g. fluid flow through porous media), measurements and calculations should be carried out fast enough, therefore optimization task should be solved. Two approaches were developed to solve it. The first one is associated with the search of optimal experimental parameters: number of projection and the quality of the detector. The second one is involved with representative elementary volume determination. Moreover, this determination technique is described in general terms and can be applied not only for porous media studies. Both algorithms are based on comparison methods of pore sizes distribution histograms. On this purposes, apart from common Earth Mover’s Distance (Wasserstein Distance) metric, a new Mean Vector Distance (MVD) metric was designed and described in this paper.
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