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Stochastic Seismic Waveform Inversion Using Generative Adversarial Networks As A Geological Prior
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, First EAGE/PESGB Workshop Machine Learning, Nov 2018, Volume 2018, p.1 - 3
Abstract
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.