
Compressive timelapse seismic monitoring of carbon storage and sequestration with the joint recovery model
Timelapse seismic monitoring of carbon storage and sequestration is oft...
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Learning by example: fast reliabilityaware seismic imaging with normalizing flows
Uncertainty quantification provides quantitative measures on the reliabi...
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Ultralow memory seismic inversion with randomized trace estimation
Inspired by recent work on extended image volumes that lays the ground f...
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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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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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Scaling through abstractions – highperformance vectorial wave simulations for seismic inversion with Devito
[Devito] is an opensource Python project based on domainspecific langu...
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Parameterizing uncertainty by deep invertible networks, an application to reservoir characterization
Uncertainty quantification for fullwaveform inversion provides a probab...
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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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Transfer learning in largescale ocean bottom seismic wavefield reconstruction
Achieving desirable receiver sampling in ocean bottom acquisition is oft...
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Uncertainty quantification in imaging and automatic horizon tracking: a Bayesian deepprior based approach
In inverse problems, uncertainty quantification (UQ) deals with a probab...
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A deeplearning based Bayesian approach to seismic imaging and uncertainty quantification
Uncertainty quantification is essential when dealing with illconditione...
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Serverless seismic imaging in the cloud
This abstract presents a serverless approach to seismic imaging in the c...
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Neural network augmented waveequation simulation
Accurate forward modeling is important for solving inverse problems. An ...
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Learned imaging with constraints and uncertainty quantification
We outline new approaches to incorporate ideas from convolutional networ...
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An EventDriven Approach to Serverless Seismic Imaging in the Cloud
Adapting the cloud for highperformance computing (HPC) is a challenging...
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Generalized Minkowski sets for the regularization of inverse problems
Many works on inverse problems in the imaging sciences consider regulari...
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Algorithms and software for projections onto intersections of convex and nonconvex sets with applications to inverse problems
We propose algorithms and software for computing projections onto the in...
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Devito: an embedded domainspecific language for finite differences and geophysical exploration
We introduce Devito, a new domainspecific language for implementing hig...
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Architecture and performance of Devito, a system for automated stencil computation
Stencil computations are a key part of many highperformance computing a...
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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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Beating levelset methods for 3D seismic data interpolation: a primaldual alternating approach
Acquisition cost is a crucial bottleneck for seismic workflows, and low...
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Fast methods for denoising matrix completion formulations, with applications to robust seismic data interpolation
Recent SVDfree matrix factorization formulations have enabled rank mini...
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Felix J. Herrmann
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