20 citations · 135 across the 34 of their papers we have counts for
14 papers · 1 filter
Distributed Proximal Stein Variational Gradient Descent Algorithm for Large-scale Bayesian Inference in Traveltime Tomography
Akshay Vishwakarma, Kamal Aghazade, Ali Siahkoohi +1
We present a distributed framework for large-scale Bayesian inverse problems governed by the eikonal equation, with a specific focus on seismic traveltime tomography. Traditional d…
Scalable Bayesian full waveform inversion via dual augmented Lagrangian SVGD
Kamal Aghazade, Ali Siahkoohi, Ali Gholami
Full waveform inversion is an ill-posed inverse problem whose solution non-uniqueness -- i.e., arising from band-limited, finite-aperture, noisy data -- calls for uncertainty quant…
Dual-space posterior sampling for Bayesian inference in constrained inverse problems
Ali Siahkoohi, Kamal Aghazade, Ali Gholami
Inverse problems constrained by partial differential equations are often ill-conditioned due to noisy, incomplete data or inherent non-uniqueness. A prominent example is full wavef…
3D seismic survey design by maximizing the spectral gap
Yijun Zhang, Ziyi Yin, Oscar López +3
The massive cost of 3D acquisition calls for methods to reduce the number of receivers by designing optimal receiver sampling masks. Recent studies on 2D seismic showed that maximi…
Optimized time-lapse acquisition design via spectral gap ratio minimization
Yijun Zhang, Ziyi Yin, Oscar Lopez +4
Modern-day reservoir management and monitoring of geological carbon storage increasingly call for costly time-lapse seismic data collection. In this letter, we show how techniques…
Wave-equation-based inversion with amortized variational Bayesian inference
Ali Siahkoohi, Rafael Orozco, Gabrio Rizzuti +1
Solving inverse problems involving measurement noise and modeling errors requires regularization in order to avoid data overfit. Geophysical inverse problems, in which the Earth's…
Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators
Ziyi Yin, Ali Siahkoohi, Mathias Louboutin +1
Seismic monitoring of carbon storage sequestration is a challenging problem involving both fluid-flow physics and wave physics. Additionally, monitoring usually requires the solver…
Velocity continuation with Fourier neural operators for accelerated uncertainty quantification
Ali Siahkoohi, Mathias Louboutin, Felix J. Herrmann
Seismic imaging is an ill-posed inverse problem that is challenged by noisy data and modeling inaccuracies -- due to errors in the background squared-slowness model. Uncertainty qu…
Deep Bayesian inference for seismic imaging with tasks
Ali Siahkoohi, Gabrio Rizzuti, Felix J. Herrmann
We propose to use techniques from Bayesian inference and deep neural networks to translate uncertainty in seismic imaging to uncertainty in tasks performed on the image, such as ho…
Learning by example: fast reliability-aware seismic imaging with normalizing flows
Ali Siahkoohi, Felix J. Herrmann
Uncertainty quantification provides quantitative measures on the reliability of candidate solutions of ill-posed inverse problems. Due to their sequential nature, Monte Carlo sampl…
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…