Linear elastic properties of granular rocks derived from X‐ray‐CT images

2007 ◽  
Author(s):  
Christoph H. Arns ◽  
Mahyar Madadi ◽  
Adrian P. Sheppard ◽  
Mark A. Knackstedt
Keyword(s):  
Elastic Properties ◽  
Ct Images ◽  
X Ray ◽  
Linear Elastic

scholarly journals Observation of temperature gradient metamorphism in snow by X-ray computed microtomography: measurement of microstructure parameters and simulation of linear elastic properties

2010 ◽  
Vol 51 (54) ◽  
pp. 73-82 ◽  
Author(s):  
P.K. Srivastava ◽  
P. Mahajan ◽  
P.K. Satyawali ◽  
V. Kumar
Keyword(s):  
Temperature Gradient ◽  
Elastic Properties ◽  
Volume Fraction ◽  
Strong Dependence ◽  
Fabric Tensor ◽  
X Ray ◽  
Linear Elastic ◽  
Ice Volume ◽  
Computed Microtomography ◽  
X Ray Computed

AbstractThe process of temperature gradient metamorphism in snow strongly affects the microstructure and associated mechanical properties of the snow. The purpose of this study was to: (1) examine the temporal variations in three-dimensional snow microstructure under the influence of a strong temperature gradient for 6 days using X-ray computed microtomography (μCT); and (2) numerically simulate the linear elastic properties of snow from microtomographic data using a voxel-based finite-element technique. The temporal changes in the snow structure were analyzed in terms of density, specific surface area (SSA), thickness distribution of ice matrix and pores, structure model index and mean intercept length (MIL) fabric tensor. The structural indices and orthotropic elastic compliance matrix were computed over several sub-volumes within the reconstructed volume to account for statistical uncertainties. The mean density increased by about 14% on day 1 and no significant trend was observed thereafter. The SSA decreased by 22%, whereas both the ice and pore thickness distributions widened with time. The computed Young’s moduli were 1.5–4 times larger than previously published dynamic measurements and found to be significantly correlated with ice volume fraction and MIL fabric measures. The increasing trend in computed moduli during the experiment is consistent with the observed development of thicker vertical ice structures. Multiple linear regression models of elastic compliances using fabric tensor formulation and ice volume fraction could explain 89.9–93.0% of the variance. Our results suggest a strong dependence of elastic properties on both density and microstructural fabric.

PETROPHYSICAL PROPERTIES DERIVED FROM X-RAY CT IMAGES

2003 ◽  
Vol 43 (1) ◽  
pp. 577 ◽  
Author(s):  
C.H. Arns ◽  
A. Sakellariou ◽  
T.J. Senden ◽  
A.P. Sheppard ◽  
R.M. Sok ◽  
...  
Keyword(s):  
Elastic Properties ◽  
Excellent Agreement ◽  
Ct Images ◽  
Three Dimensions ◽  
Rock Properties ◽  
Experimental Measurements ◽  
Petrophysical Properties ◽  
Tomographic Images ◽  
X Ray ◽  
Wide Range

A micro-CT facility for imaging, visualising and modelling sedimentary rock properties in three dimensions (3D) is described. The facility is capable of acquiring 3D X-ray CT images of full-diameter cores and core plugs at up to 2,0003 voxels with resolutions down to 2μm. This allows the 3D pore-space of a rock to be imaged and, with the aid of SEM, to identify regions of different mineralogy. Computational results are presented which demonstrate that accurate predictions of petrophysical properties can be made directly from the digitised tomographic images. Computations of both formation factor and permeability from micro-tomographic images of Fontainebleau sandstone are shown to be in excellent agreement with experimental measurements over a wide range of porosities. Computed elastic properties for dry and water-saturated conditions are shown to be consistent with the exact Gassmann’s equations and are in excellent agreement with experimental measurements. Experimental measurements of Vp/Vs ratio for cemented sandstone morphologies are very noisy and cannot be used to infer relationships between elastic properties, mineralogy and rock microstructure. Computations on tomographic images show that the Vp/Vs ratio exhibits predictable limiting behavior which holds for any number of solid phases and is insensitive to the manner in which the phases are distributed. This allows the development of more accurate empirical methods for deriving the full velocity-porosity relationship for cemented sands. The results demonstrate the feasibility of combining digitised images with numerical calculations to accurately predict petrophysical properties of individual rock morphologies.

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 Vulnerability in Deep Transfer Learning Models to Adversarial Fast Gradient Sign Attack for COVID-19 Prediction from Chest Radiography Images

2021 ◽  
Vol 11 (9) ◽  
pp. 4233
Author(s):  
Biprodip Pal ◽  
Debashis Gupta ◽  
Md. Rashed-Al-Mahfuz ◽  
Salem A. Alyami ◽  
Mohammad Ali Moni
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Chest Radiography ◽  
High Sensitivity ◽  
Ct Images ◽  
Classification Performance ◽  
Learning Models ◽  
X Ray ◽  
Image Capturing ◽  
Fast Gradient

The COVID-19 pandemic requires the rapid isolation of infected patients. Thus, high-sensitivity radiology images could be a key technique to diagnose patients besides the polymerase chain reaction approach. Deep learning algorithms are proposed in several studies to detect COVID-19 symptoms due to the success in chest radiography image classification, cost efficiency, lack of expert radiologists, and the need for faster processing in the pandemic area. Most of the promising algorithms proposed in different studies are based on pre-trained deep learning models. Such open-source models and lack of variation in the radiology image-capturing environment make the diagnosis system vulnerable to adversarial attacks such as fast gradient sign method (FGSM) attack. This study therefore explored the potential vulnerability of pre-trained convolutional neural network algorithms to the FGSM attack in terms of two frequently used models, VGG16 and Inception-v3. Firstly, we developed two transfer learning models for X-ray and CT image-based COVID-19 classification and analyzed the performance extensively in terms of accuracy, precision, recall, and AUC. Secondly, our study illustrates that misclassification can occur with a very minor perturbation magnitude, such as 0.009 and 0.003 for the FGSM attack in these models for X-ray and CT images, respectively, without any effect on the visual perceptibility of the perturbation. In addition, we demonstrated that successful FGSM attack can decrease the classification performance to 16.67% and 55.56% for X-ray images, as well as 36% and 40% in the case of CT images for VGG16 and Inception-v3, respectively, without any human-recognizable perturbation effects in the adversarial images. Finally, we analyzed that correct class probability of any test image which is supposed to be 1, can drop for both considered models and with increased perturbation; it can drop to 0.24 and 0.17 for the VGG16 model in cases of X-ray and CT images, respectively. Thus, despite the need for data sharing and automated diagnosis, practical deployment of such program requires more robustness.

scholarly journals Curvature Elastic Properties of Phosphatidylethanolamines with Mono-and Polyunsaturated Hydrocarbon Chains - An NMR and X-Ray Diffraction Study

2012 ◽  
Vol 102 (3) ◽  
pp. 413a
Author(s):  
Walter E. Teague ◽  
Olivier Soubias ◽  
Nola L. Fuller ◽  
R. Peter Rand ◽  
Klaus Gawrisch
Keyword(s):  
Diffraction Study ◽  
Elastic Properties ◽  
X Ray Diffraction ◽  
X Ray ◽  
Hydrocarbon Chains

THE COMPARISION OF RADIOLOGICAL DISTANCE DIFFERENCE BETWEEN THE PARS INTER-ARTICULARIS AND PEDICLE, AS A REPRODUCIBLE AND CONSISTENT GUIDE FOR PEDICULAR SCREW FIXATION TECHNIQUE, IN SPINAL LUMBAR SURGERIES.

2021 ◽  
pp. 61-63
Author(s):  
Bharath. V ◽  
Hemanth Kumar ◽  
Ashwanth Narayan ◽  
Venkatachalam .K ◽  
Ashwin. VY ◽  
...  
Keyword(s):  
Ct Images ◽  
Steady Increase ◽  
Lateral Border ◽  
X Ray ◽  
Screw Insertion ◽  
Pedicular Screw ◽  
Pars Interarticularis ◽  
Upward Direction ◽  
Anatomical Relationship ◽  
Distance Difference

The Inter-Pedicular and Inter-Pars distance was measured in a plain AP radiography (X-Ray) of 150 and 75 CT images normal patients between 18- 47 years of age. The aim of the study is to measure the normal Inter-Pedicular and Inter-Pars distance. We found that by studying the anatomical relationship between the inner or medial Pedicular border and the Pars outer or lateral border, gives the Orthopaedic Surgeon a reproducible and consistent guide towards exacting a pedicular screw placing. We found that both X-Ray and CT images shows steady increase in the Ipr and Ipd from L1 to L5, there is a minimal difference from L1-L2 and marked difference seen from L3 to L5, and showing the differences in distances are more in the males, compared to females. The Means of all the groups compared also proves that there is steady raise in the diameter of the IPR and IPD from L1 to L5, where there is dramatical and signicant change in the upward direction, noted from L3 to L5. The mean difference is almost constant from L1to L2. So this study, did essentially to help, establish that, the inner medial border of pedicle, is in near relationship to, the outer lateral border of the Pars-Interarticularis, which helps in establishing the latero-medial entry point for the pedicular screw insertion in the lumbar spine.

Deep Learning Enabled Deblurring of Computed Tomography Images of Porous Media

2021 ◽  
Author(s):  
Khalid Labib Alsamadony ◽  
Ertugrul Umut Yildirim ◽  
Guenther Glatz ◽  
Umair bin Waheed ◽  
Sherif M. Hanafy
Keyword(s):  
Computed Tomography ◽  
Image Quality ◽  
Transfer Learning ◽  
Temporal Resolution ◽  
Exposure Time ◽  
Ct Images ◽  
Loss Functions ◽  
High Quality ◽  
X Ray ◽  
Training Images

Abstract Computed tomography (CT) is an important tool to characterize rock samples allowing quantification of physical properties in 3D and 4D. The accuracy of a property delineated from CT data is strongly correlated with the CT image quality. In general, high-quality, lower noise CT Images mandate greater exposure times. With increasing exposure time, however, more wear is put on the X-Ray tube and longer cooldown periods are required, inevitably limiting the temporal resolution of the particular phenomena under investigation. In this work, we propose a deep convolutional neural network (DCNN) based approach to improve the quality of images collected during reduced exposure time scans. First, we convolve long exposure time images from medical CT scanner with a blur kernel to mimic the degradation caused because of reduced exposure time scanning. Subsequently, utilizing the high- and low-quality scan stacks, we train a DCNN. The trained network enables us to restore any low-quality scan for which high-quality reference is not available. Furthermore, we investigate several factors affecting the DCNN performance such as the number of training images, transfer learning strategies, and loss functions. The results indicate that the number of training images is an important factor since the predictive capability of the DCNN improves as the number of training images increases. We illustrate, however, that the requirement for a large training dataset can be reduced by exploiting transfer learning. In addition, training the DCNN on mean squared error (MSE) as a loss function outperforms both mean absolute error (MAE) and Peak signal-to-noise ratio (PSNR) loss functions with respect to image quality metrics. The presented approach enables the prediction of high-quality images from low exposure CT images. Consequently, this allows for continued scanning without the need for X-Ray tube to cool down, thereby maximizing the temporal resolution. This is of particular value for any core flood experiment seeking to capture the underlying dynamics.

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