20 citations · 135 across the 34 of their papers we have counts for
6 papers · 1 filter
Faster Uncertainty Quantification for Inverse Problems with Conditional Normalizing Flows
Ali Siahkoohi, Gabrio Rizzuti, Philipp A. Witte +1
In inverse problems, we often have access to data consisting of paired samples where are partial observations of a physical system, and represents…
Parameterizing uncertainty by deep invertible networks, an application to reservoir characterization
Gabrio Rizzuti, Ali Siahkoohi, Philipp A. Witte +1
Uncertainty quantification for full-waveform inversion provides a probabilistic characterization of the ill-conditioning of the problem, comprising the sensitivity of the solution…
Transfer learning in large-scale ocean bottom seismic wavefield reconstruction
Mi Zhang, Ali Siahkoohi, Felix J. Herrmann
Achieving desirable receiver sampling in ocean bottom acquisition is often not possible because of cost considerations. Assuming adequate source sampling is available, which is ach…
Uncertainty quantification in imaging and automatic horizon tracking: a Bayesian deep-prior based approach
Ali Siahkoohi, Gabrio Rizzuti, Felix J. Herrmann
In inverse problems, uncertainty quantification (UQ) deals with a probabilistic description of the solution nonuniqueness and data noise sensitivity. Setting seismic imaging into a…
Weak deep priors for seismic imaging
Ali Siahkoohi, Gabrio Rizzuti, Felix J. Herrmann
Incorporating prior knowledge on model unknowns of interest is essential when dealing with ill-posed inverse problems due to the nonuniqueness of the solution and data noise. Unfor…
A deep-learning based Bayesian approach to seismic imaging and uncertainty quantification
Ali Siahkoohi, Gabrio Rizzuti, Felix J. Herrmann
Uncertainty quantification is essential when dealing with ill-conditioned inverse problems due to the inherent nonuniqueness of the solution. Bayesian approaches allow us to determ…