Machine learning for predicting properties of porous media from 2d X-ray images

Alqahtani, Naif; Alzubaidi, Fatimah; Armstrong, Ryan T.; Swietojanski, Paweł; Mostaghimi, Peyman

doi:10.1016/j.petrol.2019.106514

Cited by 118 publications

(60 citation statements)

References 45 publications

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“…The three-dimensional data obtained helps to better inform numerical models improving their predictive power and enables delineation of physical properties of the specimen under investigation. Both qualities are particularly valued in the area of digital rock physics [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Driven Noise Reduction for Reduced Flux Computed Tomography

Alsamadony

Yildirim

Glatz

et al. 2021

Sensors

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Deep neural networks have received considerable attention in clinical imaging, particularly with respect to the reduction of radiation risk. Lowering the radiation dose by reducing the photon flux inevitably results in the degradation of the scanned image quality. Thus, researchers have sought to exploit deep convolutional neural networks (DCNNs) to map low-quality, low-dose images to higher-dose, higher-quality images, thereby minimizing the associated radiation hazard. Conversely, computed tomography (CT) measurements of geomaterials are not limited by the radiation dose. In contrast to the human body, however, geomaterials may be comprised of high-density constituents causing increased attenuation of the X-rays. Consequently, higher-dose images are required to obtain an acceptable scan quality. The problem of prolonged acquisition times is particularly severe for micro-CT based scanning technologies. Depending on the sample size and exposure time settings, a single scan may require several hours to complete. This is of particular concern if phenomena with an exponential temperature dependency are to be elucidated. A process may happen too fast to be adequately captured by CT scanning. To address the aforementioned issues, we apply DCNNs to improve the quality of rock CT images and reduce exposure times by more than 60%, simultaneously. We highlight current results based on micro-CT derived datasets and apply transfer learning to improve DCNN results without increasing training time. The approach is applicable to any computed tomography technology. Furthermore, we contrast the performance of the DCNN trained by minimizing different loss functions such as mean squared error and structural similarity index.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Driven Noise Reduction for Reduced Flux Computed Tomography

Alsamadony

Yildirim

Glatz

et al. 2021

Sensors

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“…For dense layer, where each input feature is assigned a vector of weights that connects to activation output, this operation applies [23]:…”

Section: Machine Learning Methods Using Mutual Induction Datamentioning

confidence: 99%

Interior Void Classification in Liquid Metal using Multi-Frequency Magnetic Induction Tomography with a Machine Learning Approach

Muttakin¹,

Soleimani²

2021

Preprint

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Identification of gas bubble, void detection and porosity estimation are important factors in many liquid metal processes. In steel casting, the importance of flow condition and phase distribution in crucial parts, such as submerged entry nozzle (SEN) and mould raises the needs to observe the phenomena. Cross-section of flow shapes can be visualised using the magnetic induction tomography (MIT) technique. However, the inversion procedure in the image reconstruction has either limited resolution or complex computation degrading its real-time capability. Additionally, in some cases, the actual image may not be essential whereas the void fraction or porosity needs to be estimated. This work proposes an interior void classifier based on multi-frequency mutual induction measurements with eutectic alloy GaInSn as a cold liquid metal model contained in a 3D printed plastic miniature of an SEN. The sensors consist of eight coils arranged in a circle encapsulating the column, providing combinatorial detection on conductive surface and depth. The datasets are induced voltage collections of several non-metallic inclusions (NMI) patterns in liquid metal static test and used to train a machine learning model. The model architectures are a fully connected neural network (FCNN) for 1D; and a convolutional neural network (CNN) for 2D data. The classifier using 1D data has been trained to approximately 86% accuracy on this dataset. CNN classification using multi-dimensional data with more classes produces 96% of test accuracy. Refined with representative flow scenarios, the trained model could be deployed for an intelligent online control system of the liquid metal process.

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“…CNNs can be classified as a deep and complex version of ANNs, in which a series of matrix manipulations and multiplications transform an input tensor of data into a target tensor (Valavi et al, 2018). Considering the high potentials of CNNs in data transformation they have been frequently used for property estimation purposes in the porous material research (Alqahtani et al, 2020;Kamrava et al, 2020;Rabbani et al, 2020;Wu et al, 2019). As an example, effective diffusivity of the porous material as a macroscopic property which is an important parameter for reaction and catalysis modeling, can be obtained using CNN models (Wu et al, 2019).…”

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

“…The results of the model trained with binary images exhibited high accuracy for the properties in contrast to the greyscale images. Therefore, the authors reflected on the model dependence of intermediate image processing steps, such as binarization and segmentation, thus, highlighted the requisite of a detailed future study (Alqahtani et al, 2020). The preceding studies pertinent to CNNs by Baraboshkin et al (2020) value of 0.91 on the test dataset (Kamrava et al, 2020).…”

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%