Reconstruction of three-dimension digital rock guided by prior information with a combination of InfoGAN and style-based GAN

Cao, Danping; Hou, Zhiyu; Liu, Qiang; Fu, Feiqi

doi:10.1016/j.petrol.2021.109590

Cited by 27 publications

(6 citation statements)

References 17 publications

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“…Every organization on the planet organizes its data in order to gain a better understanding of its customers, market penetration, and future strategies because "information is wealth." is holds true for a wide range of objects in our surroundings [19].…”

Section: Introductionmentioning

confidence: 89%

Wearable Sensor Data for Classification and Analysis of Functional Fitness Exercises Using Unsupervised Deep Learning Methodologies

Ajay

Huang

2022

Security and Communication Networks

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Healthcare institutions, policymakers, and leaders around the world all agree that improving people’s health and livelihoods is our number one priority. Aging, disability, long-term care, and palliative care all pose significant challenges to the burden of illness and the health system. Wearable technology has a number of healthcare applications, from patient care to personal health. Wearable devices, sensors, mobile apps, and tracking technologies are essential for the diagnosis, prevention, monitoring, and treatment of chronic diseases. Create and test a method to automatically classify four functional fitness exercises commonly used in current circuit training routines. The proposed algorithm, fuzzy local feature C-means algorithm (FLFCM), enhanced with information-maximizing generative adversarial network, was used to locate five inertial measurement units on the upper and lower limbs, as well as the trunk, of fourteen participants (INFOGAN). The proposed method is suitable for this situation because it yields promising results.

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Section: Introductionmentioning

confidence: 89%

Wearable Sensor Data for Classification and Analysis of Functional Fitness Exercises Using Unsupervised Deep Learning Methodologies

Ajay

Huang

2022

Security and Communication Networks

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“…It is crucial to acquire digital images of rocks at different scales using suitable scanning techniques before constructing numerical models based on digital images of rocks. There are currently a variety of techniques available for obtaining 3D digital images of rocks on the micro-, meso-, and macroscales [41,49,[61][62][63]. Even so, the three-dimensional structural characteristics of engineering-scale rocks are often difficult to determine, particularly for rock reservoirs that are deep underground, such as shale oil and gas reservoirs.…”

Section: Discussionmentioning

confidence: 99%

Numerical Simulation Study of Brittle Rock Materials from Micro to Macro Scales Using Digital Image Processing and Parallel Computing

et al. 2022

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The multi-scale, high-resolution and accurate structural modeling of rocks is a powerful means to reveal the complex failure mechanisms of rocks and evaluate rock engineering safety. Due to the non-uniformity and opacity of rocks, describing their internal microstructure, mesostructure and macro joints accurately, and how to model their progressive fracture process, is a significant challenge. This paper aims to build a numerical method that can take into account real spatial structures of rocks and be applied to the study of crack propagation and failure in different scales of rocks. By combining the failure process analysis (RFPA) simulator with digital image processing technology, large-scale finite element models of multi-scale rocks, considering microstructure, mesostructure, and macro joints, were created to study mechanical and fracture behaviors on a cloud computing platform. The Windows-Linux interactive method was used for digital image processing and parallel computing. The simulation results show that the combination of a parallel RFPA solver and digital image modeling technology can achieve high-resolution structural modeling and high-efficiency calculation. In microscopic simulations, the influence of shale fractures and mineral spatial distribution on the fracture formation process can be revealed. In the mesostructure simulation, it can be seen that the spatial distribution of minerals has an impact on the splitting mode of the Brazilian splitting model. In the simulation of a joined rock mass, the progressive failure process can be effectively simulated. According to the results, it seems that the finite element parallel computing simulation method based on digital images can simulate the multi-scale failure process of brittle materials from micro to macro scales. Primarily, efficient parallel computing based on a cloud platform allows for the multi-scale, high-resolution and realistic modeling and analysis of rock materials.

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“…The hybrid modelling method combines the advantages of the first two methods, obtaining two-dimensional (2D) slice data of rocks via SEM and other physical methods, and then reconstructing the 3D digital rocks through numerical reconstruction. However, this reconstruction method is often aimed at highly homogeneous rocks, making it unsuitable for unconventional reservoirs with complex and heterogeneous pore structures, such as shale (Cao et al, 2022).…”

Section: D Digital Rock Reconstructionmentioning

confidence: 99%

Advances in the application of deep learning methods to digital rock technology

Liu

et al. 2023

Adv. Geo-Energy Res.

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Reconstruction of three-dimension digital rock guided by prior information with a combination of InfoGAN and style-based GAN

Cited by 27 publications

References 17 publications

Wearable Sensor Data for Classification and Analysis of Functional Fitness Exercises Using Unsupervised Deep Learning Methodologies

Wearable Sensor Data for Classification and Analysis of Functional Fitness Exercises Using Unsupervised Deep Learning Methodologies

Numerical Simulation Study of Brittle Rock Materials from Micro to Macro Scales Using Digital Image Processing and Parallel Computing

Advances in the application of deep learning methods to digital rock technology

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