202 citations · 715 across the 48 of their papers we have counts for
53 papers
3D seismic survey design by maximizing the spectral gap
Yijun Zhang, Ziyi Yin, Oscar López +3
The massive cost of 3D acquisition calls for methods to reduce the number of receivers by designing optimal receiver sampling masks. Recent studies on 2D seismic showed that maximi…
Solving multiphysics-based inverse problems with learned surrogates and constraints
Ziyi Yin, Rafael Orozco, Mathias Louboutin +1
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 simul…
Refining Amortized Posterior Approximations using Gradient-Based Summary Statistics
Rafael Orozco, Ali Siahkoohi, Mathias Louboutin +1
We present an iterative framework to improve the amortized approximations of posterior distributions in the context of Bayesian inverse problems, which is inspired by loop-unrolled…
Learned multiphysics inversion with differentiable programming and machine learning
Mathias Louboutin, Ziyi Yin, Rafael Orozco +7
We present the Seismic Laboratory for Imaging and Modeling/Monitoring (SLIM) open-source software framework for computational geophysics and, more generally, inverse problems invol…
Amortized Normalizing Flows for Transcranial Ultrasound with Uncertainty Quantification
Rafael Orozco, Mathias Louboutin, Ali Siahkoohi +3
We present a novel approach to transcranial ultrasound computed tomography that utilizes normalizing flows to improve the speed of imaging and provide Bayesian uncertainty quantifi…
Optimized time-lapse acquisition design via spectral gap ratio minimization
Yijun Zhang, Ziyi Yin, Oscar Lopez +4
Modern-day reservoir management and monitoring of geological carbon storage increasingly call for costly time-lapse seismic data collection. In this letter, we show how techniques…
De-risking Carbon Capture and Sequestration with Explainable CO2 Leakage Detection in Time-lapse Seismic Monitoring Images
Huseyin Tuna Erdinc, Abhinav Prakash Gahlot, Ziyi Yin +2
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 st…
De-risking geological carbon storage from high resolution time-lapse seismic to explainable leakage detection
Ziyi Yin, Huseyin Tuna Erdinc, Abhinav Prakash Gahlot +2
Geological carbon storage represents one of the few truly scalable technologies capable of reducing the CO2 concentration in the atmosphere. While this technology has the potential…
Reliable amortized variational inference with physics-based latent distribution correction
Ali Siahkoohi, Gabrio Rizzuti, Rafael Orozco +1
Bayesian inference for high-dimensional inverse problems is computationally costly and requires selecting a suitable prior distribution. Amortized variational inference addresses t…
Memory Efficient Invertible Neural Networks for 3D Photoacoustic Imaging
Rafael Orozco, Mathias Louboutin, Felix J. Herrmann
Photoacoustic imaging (PAI) can image high-resolution structures of clinical interest such as vascularity in cancerous tumor monitoring. When imaging human subjects, geometric rest…
A simulation-free seismic survey design by maximizing the spectral gap
Yijun Zhang, Mathias Louboutin, Ali Siahkoohi +3
Due to the tremendous cost of seismic data acquisition, methods have been developed to reduce the amount of data acquired by designing optimal missing trace reconstruction algorith…
Model-Parallel Fourier Neural Operators as Learned Surrogates for Large-Scale Parametric PDEs
Thomas J. Grady, Rishi Khan, Mathias Louboutin +5
Fourier neural operators (FNOs) are a recently introduced neural network architecture for learning solution operators of partial differential equations (PDEs), which have been show…
Wave-equation-based inversion with amortized variational Bayesian inference
Ali Siahkoohi, Rafael Orozco, Gabrio Rizzuti +1
Solving inverse problems involving measurement noise and modeling errors requires regularization in order to avoid data overfit. Geophysical inverse problems, in which the Earth's…
Accelerating innovation with software abstractions for scalable computational geophysics
Mathias Louboutin, Philipp A. Witte, Ali Siahkoohi +4
We present the SLIM (https://github.com/slimgroup) open-source software framework for computational geophysics, and more generally, inverse problems based on the wave-equation (e.g…
Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators
Ziyi Yin, Ali Siahkoohi, Mathias Louboutin +1
Seismic monitoring of carbon storage sequestration is a challenging problem involving both fluid-flow physics and wave physics. Additionally, monitoring usually requires the solver…
Velocity continuation with Fourier neural operators for accelerated uncertainty quantification
Ali Siahkoohi, Mathias Louboutin, Felix J. Herrmann
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 qu…
Spectral Gap-Based Seismic Survey Design
Oscar López, Rajiv Kumar, Nick Moldoveanu +1
Seismic imaging in challenging sedimentary basins and reservoirs requires acquiring, processing, and imaging very large volumes of data (tens of terabytes). To reduce the cost of a…
Enabling wave-based inversion on GPUs with randomized trace estimation
Mathias Louboutin, Felix J. Herrmann
By building on recent advances in the use of randomized trace estimation to drastically reduce the memory footprint of adjoint-state methods, we present and validate an imaging app…
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…