1887

Abstract

Summary

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.

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/content/papers/10.3997/2214-4609.201803018
2018-11-30
2024-04-20
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References

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