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.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201803018
2018-11-30
2020-04-03
Loading full text...

Full text loading...

References

  1. Chan, S., Elsheikh, A. H.
    [2018]. Parametric generation of conditional geological realizations using generative neural networks. arXiv preprint arXiv:1807.05207.
    [Google Scholar]
  2. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Bengio, Y.
    [2014]. Generative adversarial nets. In Advances in neural information processing systems (pp. 2672–2680).
    [Google Scholar]
  3. Lange, M., Kukreja, N., Louboutin, M., Luporini, F., Vieira, F., Pandolfo, V., Gorman, G.
    [2016]. Devito: towards a generic finite difference DSL using symbolic Python. In Python for High-Performance and Scientific Computing (PyHPC), Workshop on (pp. 67–75). IEEE.
    [Google Scholar]
  4. Mosser, L., Dubrule, O., & Blunt, M. J.
    [2017]. Reconstruction of three-dimensional porous media using generative adversarial neural networks. Physical Review E, 96 (4), 043309.
    [Google Scholar]
  5. Plessix, R. E.
    [2006]. A review of the adjoint-state method for computing the gradient of a functional with geophysical applications. Geophysical Journal International, 167 (2), 495–503.
    [Google Scholar]
  6. Tarantola, A.
    [2005]. Inverse problem theory and methods for model parameter estimation (Vol. 89). siam.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201803018
Loading
/content/papers/10.3997/2214-4609.201803018
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error