Search Results for author:
Found 34 papers, 22 papers with code
BEACON: Bayesian Experimental design Acceleration with Conditional Normalizing flows $-$ a case study in optimal monitor well placement for CO$_2$ sequestration
CO$_2$ sequestration is a crucial engineering solution for mitigating climate change.
Probabilistic Bayesian optimal experimental design using conditional normalizing flows
Bayesian optimal experimental design (OED) seeks to conduct the most informative experiment under budget constraints to update the prior knowledge of a system to its posterior from the experimental data in a Bayesian framework.
InvertibleNetworks.jl: A Julia package for scalable normalizing flows
InvertibleNetworks. jl is a Julia package designed for the scalable implementation of normalizing flows, a method for density estimation and sampling in high-dimensional distributions.
WISE: full-Waveform variational Inference via Subsurface Extensions
We introduce a probabilistic technique for full-waveform inversion, employing variational inference and conditional normalizing flows to quantify uncertainty in migration-velocity models and its impact on imaging.
Inference of CO2 flow patterns -- a feasibility study
To arrive at a formulation capable of inferring flow patterns for regular and irregular flow from well and seismic data, the performance of conditional normalizing flow will be analyzed on a series of carefully designed numerical experiments.
Solving multiphysics-based inverse problems with learned surrogates and constraints
Solving multiphysics-based inverse problems for geological carbon storage monitoring can be challenging when multimodal time-lapse data are expensive to collect and costly to simulate numerically.
Refining Amortized Posterior Approximations using Gradient-Based Summary Statistics
We validate our method in a controlled setting by applying it to a stylized problem, and observe improved posterior approximations with each iteration.
Learned multiphysics inversion with differentiable programming and machine learning
We present the Seismic Laboratory for Imaging and Modeling/Monitoring (SLIM) open-source software framework for computational geophysics and, more generally, inverse problems involving the wave-equation (e. g., seismic and medical ultrasound), regularization with learned priors, and learned neural surrogates for multiphase flow simulations.
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 technology to combat climate change, monitoring and detection of potential CO2 leakage through existing or storage induced faults are critical to the safe and long-term viability of the technology.
De-risking geological carbon storage from high resolution time-lapse seismic to explainable leakage detection
Amongst the different monitoring modalities, seismic imaging stands out with its ability to attain high resolution and high fidelity images.
Reliable amortized variational inference with physics-based latent distribution correction
While generic and applicable to other inverse problems, by means of a linearized seismic imaging example, we show that our correction step improves the robustness of amortized variational inference with respect to changes in the number of seismic sources, noise variance, and shifts in the prior distribution.
Memory Efficient Invertible Neural Networks for 3D Photoacoustic Imaging
Photoacoustic imaging (PAI) can image high-resolution structures of clinical interest such as vascularity in cancerous tumor monitoring.
Model-Parallel Fourier Neural Operators as Learned Surrogates for Large-Scale Parametric PDEs
Fourier neural operators (FNOs) are a recently introduced neural network architecture for learning solution operators of partial differential equations (PDEs), which have been shown to perform significantly better than comparable deep learning approaches.
Velocity continuation with Fourier neural operators for accelerated uncertainty quantification
As such, the main contribution of this work is a survey-specific Fourier neural operator surrogate to velocity continuation that maps seismic images associated with one background model to another virtually for free.
Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators
We show that we can accurately use a Fourier neural operator as a proxy for the fluid-flow simulator for a fraction of the computational cost.
Deep Bayesian inference for seismic imaging with tasks
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 horizon tracking.
Low-memory stochastic backpropagation with multi-channel randomized trace estimation
Thanks to the combination of state-of-the-art accelerators and highly optimized open software frameworks, there has been tremendous progress in the performance of deep neural networks.
Learning by example: fast reliability-aware seismic imaging with normalizing flows
To arrive at this result, we train the NF on pairs of low- and high-fidelity migrated images.
Preconditioned training of normalizing flows for variational inference in inverse problems
Obtaining samples from the posterior distribution of inverse problems with expensive forward operators is challenging especially when the unknowns involve the strongly heterogeneous Earth.
Faster Uncertainty Quantification for Inverse Problems with Conditional Normalizing Flows
In inverse problems, we often have access to data consisting of paired samples $(x, y)\sim p_{X, Y}(x, y)$ where $y$ are partial observations of a physical system, and $x$ represents the unknowns of the problem.
Scaling through abstractions -- high-performance vectorial wave simulations for seismic inversion with Devito
[Devito] is an open-source Python project based on domain-specific language and compiler technology.
Parameterizing uncertainty by deep invertible networks, an application to reservoir characterization
Uncertainty quantification for full-waveform inversion provides a probabilistic characterization of the ill-conditioning of the problem, comprising the sensitivity of the solution with respect to the starting model and data noise.
Transfer learning in large-scale ocean bottom seismic wavefield reconstruction
Because different frequency slices share information, we propose the use the method of transfer training to make our approach computationally more efficient by warm starting the training with CNN weights obtained from a neighboring frequency slices.
Weak deep priors for seismic imaging
The chief advantage of this approach is that the updates for the CNN weights do not involve the modeling operator, and become relatively cheap.
Geophysics Image and Video Processing
Uncertainty quantification in imaging and automatic horizon tracking: a Bayesian deep-prior based approach
In this paper, we focus on how UQ trickles down to horizon tracking for the determination of stratigraphic models and investigate its sensitivity with respect to the imaging result.
A deep-learning based Bayesian approach to seismic imaging and uncertainty quantification
Uncertainty quantification is essential when dealing with ill-conditioned inverse problems due to the inherent nonuniqueness of the solution.
Neural network augmented wave-equation simulation
One proxy of incomplete physics is an inaccurate discretization of Laplacian in simulation of wave equation via finite-difference method.
Learned imaging with constraints and uncertainty quantification
We outline new approaches to incorporate ideas from deep learning into wave-based least-squares imaging.
An Event-Driven Approach to Serverless Seismic Imaging in the Cloud
As an alternative to the generic lift and shift approach, we consider the specific application of seismic imaging and demonstrate a serverless and event-driven approach for running large-scale instances of this problem in the cloud.
Distributed, Parallel, and Cluster Computing Geophysics
Devito: an embedded domain-specific language for finite differences and geophysical exploration
We introduce Devito, a new domain-specific language for implementing high-performance finite difference partial differential equation solvers.
Discrete Mathematics Geophysics
Architecture and performance of Devito, a system for automated stencil computation
Some of these are obtained through well-established stencil optimizers, integrated in the back-end of the Devito compiler.
Mathematical Software 65N06, 68N20
A Unified 2D/3D Large Scale Software Environment for Nonlinear Inverse Problems
Large scale parameter estimation problems are among some of the most computationally demanding problems in numerical analysis.
Mathematical Software Numerical Analysis
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-rank formulations for data interpolation allow practitioners to `fill in' data volumes from critically subsampled data acquired in the field.
Fast methods for denoising matrix completion formulations, with applications to robust seismic data interpolation
In this paper, we consider matrix completion formulations designed to hit a target data-fitting error level provided by the user, and propose an algorithm called LR-BPDN that is able to exploit factorized formulations to solve the corresponding optimization problem.
