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07/18/2023
Solving multiphysics-based inverse problems with learned surrogates and constraints
Solving multiphysics-based inverse problems for geological carbon storag...
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05/15/2023
Refining Amortized Posterior Approximations using Gradient-Based Summary Statistics
We present an iterative framework to improve the amortized approximation...
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04/12/2023
Learned multiphysics inversion with differentiable programming and machine learning
We present the Seismic Laboratory for Imaging and Modeling/Monitoring (S...
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12/16/2022
De-risking Carbon Capture and Sequestration with Explainable CO2 Leakage Detection in Time-lapse Seismic Monitoring Images
With the growing global deployment of carbon capture and sequestration t...
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07/24/2022
Reliable amortized variational inference with physics-based latent distribution correction
Bayesian inference for high-dimensional inverse problems is challenged b...
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04/24/2022
Memory Efficient Invertible Neural Networks for 3D Photoacoustic Imaging
Photoacoustic imaging (PAI) can image high-resolution structures of clin...
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04/04/2022
Towards Large-Scale Learned Solvers for Parametric PDEs with Model-Parallel Fourier Neural Operators
Fourier neural operators (FNOs) are a recently introduced neural network...
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03/28/2022
Accelerating innovation with software abstractions for scalable computational geophysics
We present the SLIM (https://github.com/slimgroup) open-source software ...
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03/27/2022
Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators
Seismic monitoring of carbon storage sequestration is a challenging prob...
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03/27/2022
Velocity continuation with Fourier neural operators for accelerated uncertainty quantification
Seismic imaging is an ill-posed inverse problem that is challenged by no...
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01/18/2022
Enabling wave-based inversion on GPUs with randomized trace estimation
By building on recent advances in the use of randomized trace estimation...
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10/10/2021
Deep Bayesian inference for seismic imaging with tasks
We propose to use techniques from Bayesian inference and deep neural net...
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06/13/2021
Low-memory stochastic backpropagation with multi-channel randomized trace estimation
Thanks to the combination of state-of-the-art accelerators and highly op...
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04/15/2021
Compressive time-lapse seismic monitoring of carbon storage and sequestration with the joint recovery model
Time-lapse seismic monitoring of carbon storage and sequestration is oft...
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04/13/2021
Learning by example: fast reliability-aware seismic imaging with normalizing flows
Uncertainty quantification provides quantitative measures on the reliabi...
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04/01/2021
Ultra-low memory seismic inversion with randomized trace estimation
Inspired by recent work on extended image volumes that lays the ground f...
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01/11/2021
Preconditioned training of normalizing flows for variational inference in inverse problems
Obtaining samples from the posterior distribution of inverse problems wi...
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07/15/2020
Faster Uncertainty Quantification for Inverse Problems with Conditional Normalizing Flows
In inverse problems, we often have access to data consisting of paired s...
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04/22/2020
Scaling through abstractions – high-performance vectorial wave simulations for seismic inversion with Devito
[Devito] is an open-source Python project based on domain-specific langu...
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04/16/2020
Parameterizing uncertainty by deep invertible networks, an application to reservoir characterization
Uncertainty quantification for full-waveform inversion provides a probab...
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04/15/2020
Extended source imaging, a unifying framework for seismic medical imaging
We present three imaging modalities that live on the crossroads of seism...
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04/15/2020
Transfer learning in large-scale ocean bottom seismic wavefield reconstruction
Achieving desirable receiver sampling in ocean bottom acquisition is oft...
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04/01/2020
Uncertainty quantification in imaging and automatic horizon tracking: a Bayesian deep-prior based approach
In inverse problems, uncertainty quantification (UQ) deals with a probab...
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01/13/2020
A deep-learning based Bayesian approach to seismic imaging and uncertainty quantification
Uncertainty quantification is essential when dealing with ill-conditione...
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11/27/2019
Serverless seismic imaging in the cloud
This abstract presents a serverless approach to seismic imaging in the c...
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09/27/2019
Neural network augmented wave-equation simulation
Accurate forward modeling is important for solving inverse problems. An ...
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09/13/2019
Learned imaging with constraints and uncertainty quantification
We outline new approaches to incorporate ideas from convolutional networ...
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09/03/2019
An Event-Driven Approach to Serverless Seismic Imaging in the Cloud
Adapting the cloud for high-performance computing (HPC) is a challenging...
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03/10/2019
Generalized Minkowski sets for the regularization of inverse problems
Many works on inverse problems in the imaging sciences consider regulari...
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02/26/2019
Algorithms and software for projections onto intersections of convex and non-convex sets with applications to inverse problems
We propose algorithms and software for computing projections onto the in...
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08/06/2018
Devito: an embedded domain-specific language for finite differences and geophysical exploration
We introduce Devito, a new domain-specific language for implementing hig...
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07/09/2018
Architecture and performance of Devito, a system for automated stencil computation
Stencil computations are a key part of many high-performance computing a...
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03/27/2017
A Unified 2D/3D Large Scale Software Environment for Nonlinear Inverse Problems
Large scale parameter estimation problems are among some of the most com...
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07/09/2016
Beating level-set methods for 3D seismic data interpolation: a primal-dual alternating approach
Acquisition cost is a crucial bottleneck for seismic workflows, and low-...
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02/20/2013