202 citations · 715 across the 48 of their papers we have counts for
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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…
XConv: Low-memory stochastic backpropagation for convolutional layers
Anirudh Thatipelli, Jeffrey Sam, Mathias Louboutin +3
Training convolutional neural networks at scale demands substantial memory, largely because intermediate activations must be stored for backpropagation. Existing remedies (checkpoi…
A practical workflow for land seismic wavefield recovery with weighted matrix factorization
Yijun Zhang, Felix J. Herrmann
While wavefield reconstruction through weighted low-rank matrix factorizations has been shown to perform well on marine data, out-of-the-box application of this technology to land…
Compressive time-lapse seismic monitoring of carbon storage and sequestration with the joint recovery model
Ziyi Yin, Mathias Louboutin, Felix J. Herrmann
Time-lapse seismic monitoring of carbon storage and sequestration is often challenging because the time-lapse signature of the growth of CO2 plumes is weak in amplitude and therefo…
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…
Ultra-low memory seismic inversion with randomized trace estimation
Mathias Louboutin, Felix J. Herrmann
Inspired by recent work on extended image volumes that lays the ground for randomized probing of extremely large seismic wavefield matrices, we present a memory frugal and computat…
Preconditioned training of normalizing flows for variational inference in inverse problems
Ali Siahkoohi, Gabrio Rizzuti, Mathias Louboutin +2
Obtaining samples from the posterior distribution of inverse problems with expensive forward operators is challenging especially when the unknowns involve the strongly heterogeneou…