Parameterizing uncertainty by deep invertible networks, an application to reservoir characterization

Understand

Uncertainty quantification for full-waveform inversion provides a probabilistic characterization of the ill-conditioning of the problem, comprising the sensitivity of the solution with respect to the starting model and data noise.

• This analysis allows to assess the confidence in the candidate solution and how it is reflected in the tasks that are typically performed after imaging (e.g., stratigraphic segmentation following reservoir characterization).
• Classically, uncertainty comes in the form of a probability distribution formulated from Bayesian principles, from which we seek to obtain samples.
• A popular solution involves Monte Carlo sampling.