Uncertainty quantification in imaging and automatic horizon tracking: a Bayesian deep-prior based approach

@article{Siahkoohi2020UncertaintyQI,
  title={Uncertainty quantification in imaging and automatic horizon tracking: a Bayesian deep-prior based approach},
  author={Ali Siahkoohi and Gabrio Rizzuti and F. Herrmann},
  journal={ArXiv},
  year={2020},
  volume={abs/2004.00227}
}
In inverse problems, uncertainty quantification (UQ) deals with a probabilistic description of the solution nonuniqueness and data noise sensitivity. Setting seismic imaging into a Bayesian framework allows for a principled way of studying uncertainty by solving for the model posterior distribution. Imaging, however, typically constitutes only the first stage of a sequential workflow, and UQ becomes even more relevant when applied to subsequent tasks that are highly sensitive to the inversion… 

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