Deep generative networks for multivariate fullstack seismic data inversion using inverse autoregressive flows
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State: Public
Version: Final published version
License: CC BY-NC 4.0
State: Public
Version: Final published version
License: CC BY-NC 4.0
Serval ID
serval:BIB_78AE96705A4C
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Deep generative networks for multivariate fullstack seismic data inversion using inverse autoregressive flows
Journal
Computers & Geosciences
ISSN
0098-3004
Publication state
Published
Issued date
06/2024
Peer-reviewed
Oui
Volume
188
Pages
105622
Language
english
Abstract
The simultaneous prediction of the subsurface distribution of facies and acoustic impedance (IP) from fullstack seismic data requires solving an inverse problem and is fundamental in natural resources exploration, carbon capture and storage, and environmental risk management. In recent years, deep generative models (DGM), such as variational autoencoders (VAE) and generative adversarial networks (GAN), were proposed to reproduce complex facies patterns honoring prior geological information. Variational Bayesian inference using inverse autoregressive flows (IAF) can be performed to infer the solution to a geophysical inverse problem from the encoded latent space of such pre-trained DGM. Successful applications of such approach on crosshole ground-penetrating radar synthetic data inversion demonstrated that the technique’s accuracy is comparable to that of Markov chain Monte Carlo (MCMC) inference methods, while significantly reducing the computational cost. Nonetheless, these application examples did not account for the spatial uncertainty affecting the faciesdependent
continuous physical property, from which the geophysical data are calculated. This uncertainty can significantly affect the inversion accuracy and its applicability to real data. In this work, specific VAE and GAN architectures are proposed to simultaneously predict facies and co-located IP, while accounting for their spatial uncertainties. The two types of generative networks are used in Bayesian inversion with IAF for the inversion of seismic data. The results are found to reproduce the statistics of the training images and solve the seismic inversion problem accurately, comparably to MCMC inversion. Furthermore, advantages and limitations of the two DGMs are evaluated by comparing the results obtained.
continuous physical property, from which the geophysical data are calculated. This uncertainty can significantly affect the inversion accuracy and its applicability to real data. In this work, specific VAE and GAN architectures are proposed to simultaneously predict facies and co-located IP, while accounting for their spatial uncertainties. The two types of generative networks are used in Bayesian inversion with IAF for the inversion of seismic data. The results are found to reproduce the statistics of the training images and solve the seismic inversion problem accurately, comparably to MCMC inversion. Furthermore, advantages and limitations of the two DGMs are evaluated by comparing the results obtained.
Web of science
Open Access
Yes
Create date
01/11/2024 11:49
Last modification date
01/11/2024 14:10