GANSim: conditional facies simulation using an improved progressive growing of generative adversarial networks (GANs)

DOI10.1007/S11004-021-09934-0zbMath1477.86032OpenAlexW3109916085MaRDI QIDQ2066806

Tapan Mukerji, Jiagen Hou, Suihong Song

Publication date: 14 January 2022

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

Full work available at URL: http://eartharxiv.org/fm24b/

zbMATH Keywords

deep learninggenerative adversarial networks (GANs)progressive growing of GANsconditional facies modelinggeological patternreservoir forecast

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Geostatistics (86A32)

Related Items (4)

GANSim ⋮ Application of Bayesian generative adversarial networks to geological facies modeling ⋮ Stochastic reconstruction of porous media based on attention mechanisms and multi-stage generative adversarial network ⋮ Reconstruction, optimization, and design of heterogeneous materials and media: basic principles, computational algorithms, and applications

Uses Software

• CycleGAN
• pix2pix
• WaveNet
• StyleGAN
• Wasserstein GAN

Cites Work

• Unnamed Item
• Parametric generation of conditional geological realizations using generative neural networks
• Geological facies modeling based on progressive growing of generative adversarial networks (GANs)
• Stochastic seismic waveform inversion using generative adversarial networks as a geological prior

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