Deep generative models in inversion: a review and development of a new approach based on a variational autoencoder
Jorge Lopez-Alvis,
Eric Laloy,
Frédéric Nguyen
et al.
Abstract:When solving inverse problems in geophysical imaging, deep generative models (DGMs) may be used to enforce the solution to display highly structured spatial patterns which are supported by independent information (e.g. the geological setting) of the subsurface. In such case, inversion may be formulated in a latent space where a low-dimensional parameterization of the patterns is defined and where Markov chain Monte Carlo or gradient-based methods may be applied. However, the generative mapping between the late… Show more
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