Imperial College London
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ProfessorMartinBlunt

Faculty of Engineering, Department of Earth Science & Engineering

Chair in Flow in Porous Media
 
 
 
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Contact

 

+44 (0)20 7594 6500m.blunt Website

 
 
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Location

 

2.38ARoyal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Mosser:2018:10.3997/2214-4609.201803018,
author = {Mosser, L and Dubrule, O and Blunt, M},
doi = {10.3997/2214-4609.201803018},
title = {Stochastic seismic waveform inversion using generative adversarial networks as a geological prior},
url = {http://dx.doi.org/10.3997/2214-4609.201803018},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Setting the seismic inversion problem in a Bayesian framework, we seek to obtain the posterior of acoustic rock properties given a set of seismic observations and a prior distribution of the acoustic properties. We use a generative adversarial network (GAN) based on a deep convolutional neural network to represent the prior distribution of acoustic properties. This prior distribution is derived by applying a neural network to a set of Gaussian latent vectors. Samples of the posterior of these latent vectors are obtained using a Metropolis-sampling method that combines gradients obtained from full waveform inversion with back-propagation through the neural network. We apply the proposed method to a synthetic reservoir-scale dataset of channel bodies.
AU  - Mosser,L
AU  - Dubrule,O
AU  - Blunt,M
DO  - 10.3997/2214-4609.201803018
PY  - 2018///
TI  - Stochastic seismic waveform inversion using generative adversarial networks as a geological prior
UR  - http://dx.doi.org/10.3997/2214-4609.201803018
ER  -
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