Pages that link to "Item:Q2676501"
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The following pages link to Application of Bayesian generative adversarial networks to geological facies modeling (Q2676501):
Displaying 11 items.
- A new approach for conditioning process-based geologic models to well data (Q1789191) (← links)
- Parametric generation of conditional geological realizations using generative neural networks (Q2009823) (← links)
- Geological facies modeling based on progressive growing of generative adversarial networks (GANs) (Q2027203) (← links)
- Comparison of recursive neural network and Markov chain models in facies inversion (Q2040689) (← links)
- GANSim: conditional facies simulation using an improved progressive growing of generative adversarial networks (GANs) (Q2066806) (← links)
- Three-dimensional structural geological modeling using graph neural networks (Q2066838) (← links)
- Conditioning generative adversarial networks on nonlinear data for subsurface flow model calibration and uncertainty quantification (Q2130944) (← links)
- U-net generative adversarial network for subsurface facies modeling (Q2225390) (← links)
- Value of geologically derived features in machine learning facies classification (Q2284090) (← links)
- Stochastic seismic waveform inversion using generative adversarial networks as a geological prior (Q2284094) (← links)
- Deep learning-aided image-oriented history matching of geophysical data (Q6074265) (← links)
