A pore space reconstruction method of shale based on autoencoders and generative adversarial networks

DOI10.1007/s10596-021-10083-wzbMath1477.86006OpenAlexW3192485199MaRDI QIDQ2065844

Yanyan Li

Publication date: 13 January 2022

Published in: Computational Geosciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10596-021-10083-w

zbMATH Keywords

autoencodershaleporegenerative adversarial networkdigital core

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Geostatistics (86A32) Computational methods for problems pertaining to geophysics (86-08)

Related Items

Digital core image reconstruction based on residual self-attention generative adversarial networks

Uses Software

• darch
• Avizo
• Wasserstein GAN

Cites Work

• Filter-based classification of training image patterns for spatial simulation
• Conditional simulation of complex geological structures using multiple-point statistics
• An end-to-end three-dimensional reconstruction framework of porous media from a single two-dimensional image based on deep learning
• Reducing the Dimensionality of Data with Neural Networks
• Random heterogeneous materials. Microstructure and macroscopic properties