# 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…

## 9 Citations

Deep Bayesian inference for seismic imaging with tasks

- Computer Science
- 2021

This method is designed to handle large scale Bayesian inference problems with computationally expensive forward operators as in seismic imaging and introduces a flexible inductive bias that is a surprisingly good fit for many diverse domains in imaging.

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

- Computer ScienceArXiv
- 2020

This work proposes an approach characterized by training a deep network that "pushes forward" Gaussian random inputs into the model space (representing, for example, density or velocity) as if they were sampled from the actual posterior distribution, designed to solve a variational optimization problem based on the Kullback-Leibler divergence between the posterior and the network output distributions.

Learning by example: fast reliability-aware seismic imaging with normalizing flows

- GeologyFirst International Meeting for Applied Geoscience & Energy Expanded Abstracts
- 2021

This is the first attempt to train a conditional network on what the authors know from neighboring images to improve the current image and assess its reliability, and is a data-driven variational inference approach where a normalizing flow is trained, capable of cheaply sampling the posterior distribution given previously unseen seismic data from neighboring surveys.

An encoder-decoder deep surrogate for reverse time migration in seismic imaging under uncertainty

- GeologyComputational Geosciences
- 2021

An encoder-decoder deep learning surrogate model for RTM under uncertainty is proposed that can reproduce the seismic images accurately, and, more importantly, the uncertainty propagation from the input velocity fields to the image ensemble.

Velocity continuation with Fourier neural operators for accelerated uncertainty quantification

- GeologyArXiv
- 2022

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 quantiﬁcation is…

Faster Uncertainty Quantification for Inverse Problems with Conditional Normalizing Flows

- Computer Science, MathematicsArXiv
- 2020

This work proposes a two-step scheme that makes use of normalizing flows and joint data to train a conditional generator to approximate the target posterior density of a invertible generator, and presents some synthetic results that demonstrate considerable training speedup when reusing the pretrained network as a warm start or preconditioning for approximating a prior.

An introduction to variational inference in geophysical inverse problems

- MathematicsInversion of Geophysical Data
- 2021

Surface wave dispersion inversion using an energy likelihood function

- Geology
- 2022

Seismic surface wave dispersion inversion is used widely to study the subsurface structure of the Earth. The dispersion property is usually measured by using frequency-phase velocity (f-c) analysis…

Seismic characterization of deeply buried paleocaves based on Bayesian deep learning

- GeologyJournal of Natural Gas Science and Engineering
- 2021

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