Statistical approach to X-ray CT imaging and its applications in image analysis. I. Statistical analysis of X-ray CT imaging

1992 ◽  
Vol 11 (1) ◽  
pp. 53-61 ◽  
Author(s):  
T. Lei ◽  
W. Sewchand
Keyword(s):  
Image Analysis ◽  
Statistical Analysis ◽  
Statistical Approach ◽  
Ct Imaging ◽  
X Ray

Quantitative X-ray Map Analyser (Q-XRMA): A new GIS-based statistical approach to Mineral Image Analysis

2018 ◽  
Vol 115 ◽  
pp. 56-65 ◽  
Author(s):  
Gaetano Ortolano ◽  
Roberto Visalli ◽  
Gaston Godard ◽  
Rosolino Cirrincione
Keyword(s):  
Image Analysis ◽  
Statistical Approach ◽  
X Ray

scholarly journals Decoding Rocks: An Assessment of Geomaterial Microstructure Using X-ray Microtomography, Image Analysis and Multivariate Statistics

Materials ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3266
Author(s):  
Piotr Jan Strzelecki ◽  
Anna Świerczewska ◽  
Katarzyna Kopczewska ◽  
Adam Fheed ◽  
Jacek Tarasiuk ◽  
...  
Keyword(s):  
Image Analysis ◽  
Statistical Approach ◽  
Methodological Approach ◽  
Spatial Arrangement ◽  
Sandstone Sample ◽  
X Ray ◽  
Spatial Properties ◽  
Geometrical Features ◽  
Directional Trend

An understanding of the microstructure of geomaterials such as rocks is fundamental in the evaluation of their functional properties, as well as the decryption of their geological history. We present a semi-automated statistical protocol for a complex 3D characterization of the microstructure of granular materials, including the clustering of grains and a description of their chemical composition, size, shape, and spatial properties with 44 unique parameters. The approach consists of an X-ray microtomographic image processing procedure, followed by measurements using image analysis and statistical multivariate analysis of its results utilizing freeware and widely available software. The statistical approach proposed was tested out on a sandstone sample with hidden and localized deformational microstructures. The grains were clustered into distinctive groups covering different compositional and geometrical features of the sample’s granular framework. The grains are pervasively and evenly distributed within the analysed sample. The spatial arrangement of grains in particular clusters is well organized and shows a directional trend referring to both microstructures. The methodological approach can be applied to any other rock type and enables the tracking of microstructural trends in grains arrangement.

scholarly journals Equivalent Pore Channel Model for Fluid Flow in Rock Based on Microscale X-ray CT Imaging

Materials ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2619 ◽  
Author(s):  
Chae-Soon Choi ◽  
Yong-Ki Lee ◽  
Jae-Joon Song
Keyword(s):  
Image Analysis ◽  
Fluid Flow ◽  
Channel Model ◽  
Ct Imaging ◽  
Two Dimensions ◽  
Pore Channel ◽  
Computational Domain ◽  
Ct Image ◽  
X Ray ◽  
Ct Image Analysis

Pore-scale modeling with a reconstructed rock microstructure has become a dominant technique for fluid flow characterization in rock thanks to technological improvements in X-ray computed tomography (CT) imaging. A new method for the construction of a pore channel model from micro-CT image analysis is suggested to improve computational efficiency by simplifying a highly complex pore structure. Ternary segmentation was applied through matching a pore volume experimentally measured by mercury intrusion porosimetry with a CT image voxel volume to distinguish regions denoted as “apparent” and “indistinct” pores. The developed pore channel model, with distinct domains of different pore phases, captures the pore shape dependence of flow in two dimensions and a tortuous flow path in three dimensions. All factors determining these geometric characteristics were identified by CT image analysis. Computation of an interaction flow regime with apparent and indistinct pore domains was conducted using both the Stokes and Brinkman equations. The coupling was successfully simulated and evaluated against the experimental results of permeability derived from Darcy’s law. Reasonable agreement was found between the permeability derived from the pore channel model and that estimated experimentally. However, the model is still incapable of accurate flow modeling in very low-permeability rock. Direct numerical simulation in a computational domain with a complex pore space was also performed to compare its accuracy and efficiency with the pore channel model. Both schemes achieved reasonable results, but the pore channel model was more computationally efficient.

scholarly journals Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4595
Author(s):  
Parisa Asadi ◽  
Lauren E. Beckingham
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Ct Images ◽  
Ct Imaging ◽  
Learning Method ◽  
Learning Methods ◽  
X Ray ◽  
Machine Learning Methods ◽  
Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

scholarly journals Statistical analysis of the reproducibility of residual stress measurements in cold extruded parts

2021 ◽  
Author(s):  
Fabian Jaeger ◽  
Alessandro Franceschi ◽  
Holger Hoche ◽  
Peter Groche ◽  
Matthias Oechsner
Keyword(s):  
Residual Stress ◽  
Statistical Analysis ◽  
Stress Measurement ◽  
Industrial Applications ◽  
Cold Extrusion ◽  
X Ray Diffraction ◽  
Forming Process ◽  
X Ray ◽  
Stress States ◽  
Key Aspects

AbstractCold extruded components are characterized by residual stresses, which originate from the experienced manufacturing process. For industrial applications, reproducibility and homogeneity of the final components are key aspects for an optimized quality control. Although striving to obtain identical deformation and surface conditions, fluctuation in the manufacturing parameters and contact shear conditions during the forming process may lead to variations of the spatial residual stress distribution in the final product. This could lead to a dependency of the residual stress measurement results on the relative axial and circumferential position on the sample. An attempt to examine this problem is made by the employment of design of experiments (DoE) methods. A statistical analysis of the residual stress results generated through X-Ray diffraction is performed. Additionally, the ability of cold extrusion processes to generate uniform stress states is analyzed on specimens of austenitic stainless steel 1.4404 and possible correlations with the pre-deformed condition are statistically examined. Moreover, the influence of the coating, consisting of oxalate and a MoS2 based lubricant, on the X-Ray diffraction measurements of the surface is investigated.

Statistical analysis of soft X-ray solar flares during solar cycles 22, 23, and 24

2021 ◽  
Vol 366 (1) ◽  
Author(s):  
Bo Xiong ◽  
Ting Wang ◽  
Xiaolin Li ◽  
Yunxing Yin
Keyword(s):  
Statistical Analysis ◽  
Solar Flares ◽  
Solar Cycles ◽  
X Ray
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