Stochastic seismic waveform inversion using generative adversarial networks as a geological prior
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Publication:2284094
DOI10.1007/s11004-019-09832-6zbMath1428.86022arXiv1806.03720OpenAlexW2987357275WikidataQ126790201 ScholiaQ126790201MaRDI QIDQ2284094
Lukas Mosser, Olivier Dubrule, Martin J. Blunt
Publication date: 14 January 2020
Published in: Mathematical Geosciences (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1806.03720
Bayesian inference (62F15) Learning and adaptive systems in artificial intelligence (68T05) Inverse problems in geophysics (86A22) Geostatistics (86A32)
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Uses Software
Cites Work
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- Exponential convergence of Langevin distributions and their discrete approximations
- Multilayer feedforward networks are universal approximators
- Parametric generation of conditional geological realizations using generative neural networks
- Stochastic seismic waveform inversion using generative adversarial networks as a geological prior
- Bayesian Gaussian mixture linear inversion for geophysical inverse problems
- Langevin diffusions and the Metropolis-adjusted Langevin algorithm
- Definitions and examples of inverse and ill-posed problems
- Resolution analysis of general inverse problems through inverse Monte Carlo sampling
- Optimal Scaling of Discrete Approximations to Langevin Diffusions
- Inverse Problem Theory and Methods for Model Parameter Estimation
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