Training-Image Based Geostatistical Inversion Using a Spatial Generative Adversarial Neural Network

Détails

ID Serval
serval:BIB_057E3A79282A
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Training-Image Based Geostatistical Inversion Using a Spatial Generative Adversarial Neural Network
Périodique
Water Resources Research
Auteur(s)
Laloy E., Hérault R., Jacques D., Linde N.
ISSN
0043-1397
ISSN-L
1944-7973
Statut éditorial
Publié
Date de publication
2018
Peer-reviewed
Oui
Volume
54
Pages
381-406
Langue
anglais
Résumé
Probabilistic inversion within a multiple‐point statistics framework is often computationally prohibitive for high‐dimensional problems. To partly address this, we introduce and evaluate a new training‐image based inversion approach for complex geologic media. Our approach relies on a deep neural network of the generative adversarial network (GAN) type. After training using a training image (TI), our proposed spatial GAN (SGAN) can quickly generate 2‐D and 3‐D unconditional realizations. A key characteristic of our SGAN is that it defines a (very) low‐dimensional parameterization, thereby allowing for efficient probabilistic inversion using state‐of‐the‐art Markov chain Monte Carlo (MCMC) methods. In addition, available direct conditioning data can be incorporated within the inversion. Several 2‐D and 3‐D categorical TIs are first used to analyze the performance of our SGAN for unconditional geostatistical simulation. Training our deep network can take several hours. After training, realizations containing a few millions of pixels/voxels can be produced in a matter of seconds. This makes it especially useful for simulating many thousands of realizations (e.g., for MCMC inversion) as the relative cost of the training per realization diminishes with the considered number of realizations. Synthetic inversion case studies involving 2‐D steady state flow and 3‐D transient hydraulic tomography with and without direct conditioning data are used to illustrate the effectiveness of our proposed SGAN‐based inversion. For the 2‐D case, the inversion rapidly explores the posterior model distribution. For the 3‐D case, the inversion recovers model realizations that fit the data close to the target level and visually resemble the true model well.
Web of science
Création de la notice
19/12/2018 16:51
Dernière modification de la notice
20/08/2019 12:27
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