Randomly generating three-dimensional realistic schistous sand particles using deep learning: Variational autoencoder implementation

Nanjing sand, a type of greenish-grey schistous sand, is rich in weathered mica fragments, making it anisotropic and significantly differ from the round-grained quartz sand in terms of composition, grading, and mechanical properties. In this paper, the variational-autoencoder (VAE), a generative dee...

Full description

Saved in:

Bibliographic Details
Published in:	Engineering geology Vol. 291; p. 106235
Main Authors:	Shi, Jia-jie, Zhang, Wei, Wang, Wei, Sun, Yun-han, Xu, Chuan-yi, Zhu, Hong-hu, Sun, Zheng-xing
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 20.09.2021
Subjects:	anisotropy Deep learning Gaussian random noise mica micro-computed tomography Micro-computed tomography imaging quartz Random particle generation sand Schistous sand Variational autoencoder Deep learning Variational autoencoder Micro-computed tomography imaging Schistous sand Random particle generation Gaussian random noise
ISSN:	0013-7952, 1872-6917
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!