DeePore: a deep learning workflow for rapid and comprehensive characterization of porous materials

• URL: http://arxiv.org/abs/2005.03759v2
• Date: Sat, 10 Oct 2020 09:06:32 GMT
• Title: DeePore: a deep learning workflow for rapid and comprehensive characterization of porous materials
• Authors: Arash Rabbani, Masoud Babaei, Reza Shams, Ying Da Wang, Traiwit Chung
• Abstract summary: DeePore is a deep learning workflow for estimation of a wide range of porous material properties based on micro-tomography images. We generated 17700 semi-real 3-D micro-structures of porous geo-materials with size of 2563 voxels and 30 physical properties of each sample are calculated using physical simulations on the corresponding pore network models. CNN is trained based on the dataset to estimate several morphological, hydraulic, electrical, and mechanical characteristics of the porous material in a fraction of a second.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: DeePore is a deep learning workflow for rapid estimation of a wide range of porous material properties based on the binarized micro-tomography images. By combining naturally occurring porous textures we generated 17700 semi-real 3-D micro-structures of porous geo-materials with size of 256^3 voxels and 30 physical properties of each sample are calculated using physical simulations on the corresponding pore network models. Next, a designed feed-forward convolutional neural network (CNN) is trained based on the dataset to estimate several morphological, hydraulic, electrical, and mechanical characteristics of the porous material in a fraction of a second. In order to fine-tune the CNN design, we tested 9 different training scenarios and selected the one with the highest average coefficient of determination (R^2) equal to 0.885 for 1418 testing samples. Additionally, 3 independent synthetic images as well as 3 realistic tomography images have been tested using the proposed method and results are compared with pore network modelling and experimental data, respectively. Tested absolute permeabilities had around 13 % relative error compared to the experimental data which is noticeable considering the accuracy of the direct numerical simulation methods such as Lattice Boltzmann and Finite Volume. The workflow is compatible with any physical size of the images due to its dimensionless approach and can be used to characterize large-scale 3-D images by averaging the model outputs for a sliding window that scans the whole geometry.

Related papers

• WiNet: Wavelet-based Incremental Learning for Efficient Medical Image Registration [68.25711405944239]
  Deep image registration has demonstrated exceptional accuracy and fast inference. Recent advances have adopted either multiple cascades or pyramid architectures to estimate dense deformation fields in a coarse-to-fine manner. We introduce a model-driven WiNet that incrementally estimates scale-wise wavelet coefficients for the displacement/velocity field across various scales.
  arXiv  Detail & Related papers  (2024-07-18T11:51:01Z)
• Deep learning of multi-resolution X-Ray micro-CT images for multi-scale modelling [0.0]
  We develop a 3D Enhanced Deep Super Resolution (EDSR) convolutional neural network to create enhanced, high-resolution data over large spatial scales. We validate the network with various metrics: textual analysis, segmentation behaviour and pore-network model (PNM) multiphase flow simulations. The EDSR generated model is more accurate than the base LR model at predicting experimental behaviour in the presence of heterogeneities.
  arXiv  Detail & Related papers  (2021-11-01T21:49:22Z)
• A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures [68.8204255655161]
  It is shown that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches. It is shown, that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches.
  arXiv  Detail & Related papers  (2021-10-26T07:02:14Z)
• Estimating permeability of 3D micro-CT images by physics-informed CNNs based on DNS [1.6274397329511197]
  This paper presents a novel methodology for permeability prediction from micro-CT scans of geological rock samples. The training data set for CNNs dedicated to permeability prediction consists of permeability labels that are typically generated by classical lattice Boltzmann methods (LBM) We instead perform direct numerical simulation (DNS) by solving the stationary Stokes equation in an efficient and distributed-parallel manner.
  arXiv  Detail & Related papers  (2021-09-04T08:43:19Z)
• Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth [64.29043589521308]
  We propose a rendering module to augment the training data by synthesizing images with virtual-depths. The rendering module takes as input the RGB image and its corresponding sparse depth image, outputs a variety of photo-realistic synthetic images. Besides, we introduce an auxiliary module to improve the detection model by jointly optimizing it through a depth estimation task.
  arXiv  Detail & Related papers  (2021-07-28T11:00:47Z)
• Multi-Scale Neural Networks for to Fluid Flow in 3D Porous Media [0.0]
  We develop a general multiscale deep learning model that is able to learn from porous media simulation data. We enable the evaluation of large images in approximately one second on a single Graphics Processing Unit.
  arXiv  Detail & Related papers  (2021-02-10T23:38:36Z)
• MudrockNet: Semantic Segmentation of Mudrock SEM Images through Deep Learning [0.0]
  We propose a deep learning SEM segmentation model, MudrockNet based on Google's DeepLab-v3+ architecture. The trained deep learning model obtained a pixel-accuracy about 90%, and predictions for the test data obtained a mean intersection over union (IoU) of 0.6591 for silt grains and 0.6642 for pores.
  arXiv  Detail & Related papers  (2021-02-05T19:38:44Z)
• Fed-Sim: Federated Simulation for Medical Imaging [131.56325440976207]
  We introduce a physics-driven generative approach that consists of two learnable neural modules. We show that our data synthesis framework improves the downstream segmentation performance on several datasets.
  arXiv  Detail & Related papers  (2020-09-01T19:17:46Z)
• Deep learning for lithological classification of carbonate rock micro-CT images [52.77024349608834]
  This work intends to present an application of deep learning techniques to identify patterns in Brazilian pre-salt carbonate rock microtomographic images. Four convolutional neural network models were proposed. According to accuracy, Model 2 trained on resized images achieved the best results, reaching an average of 75.54% for the first evaluation approach and an average of 81.33% for the second.
  arXiv  Detail & Related papers  (2020-07-30T19:14:00Z)
• Intrinsic Autoencoders for Joint Neural Rendering and Intrinsic Image Decomposition [67.9464567157846]
  We propose an autoencoder for joint generation of realistic images from synthetic 3D models while simultaneously decomposing real images into their intrinsic shape and appearance properties. Our experiments confirm that a joint treatment of rendering and decomposition is indeed beneficial and that our approach outperforms state-of-the-art image-to-image translation baselines both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2020-06-29T12:53:58Z)
• Physical Accuracy of Deep Neural Networks for 2D and 3D Multi-Mineral Segmentation of Rock micro-CT Images [0.0]
  The performance of 4 CNN architectures is tested for 2D and 3D cases in 10 configurations. A new network architecture is introduced as a hybrid fusion of U-net and ResNet, combining short and long skip connections in a Network-in-Network configuration. The 3D implementation outperforms all other tested models in voxelwise and physical accuracy measures.
  arXiv  Detail & Related papers  (2020-02-13T03:14:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.