202 citations · 467 across the 38 of their papers we have counts for
42 papers
Machine learning-enabled velocity model building with uncertainty quantification
Rafael Orozco, Huseyin Tuna Erdinc, Yunlin Zeng +2
Accurately characterizing migration velocity models is crucial for a wide range of geophysical applications, from hydrocarbon exploration to monitoring of CO2 sequestration project…
ASPIRE: Iterative Amortized Posterior Inference for Bayesian Inverse Problems
Rafael Orozco, Ali Siahkoohi, Mathias Louboutin +1
Due to their uncertainty quantification, Bayesian solutions to inverse problems are the framework of choice in applications that are risk averse. These benefits come at the cost of…
Time-lapse full-waveform permeability inversion: a feasibility study
Ziyi Yin, Mathias Louboutin, Olav Møyner +1
Time-lapse seismic monitoring necessitates integrated workflows that combine seismic and reservoir modeling to enhance reservoir property estimation. We present a feasibility study…
InvertibleNetworks.jl: A Julia package for scalable normalizing flows
Rafael Orozco, Philipp Witte, Mathias Louboutin +4
InvertibleNetworks.jl is a Julia package designed for the scalable implementation of normalizing flows, a method for density estimation and sampling in high-dimensional distributio…
WISE: full-Waveform variational Inference via Subsurface Extensions
Ziyi Yin, Rafael Orozco, Mathias Louboutin +1
We introduce a probabilistic technique for full-waveform inversion, employing variational inference and conditional normalizing flows to quantify uncertainty in migration-velocity…
Automated MPI-X code generation for scalable finite-difference solvers
George Bisbas, Rhodri Nelson, Mathias Louboutin +3
Partial differential equations (PDEs) are crucial in modeling diverse phenomena across scientific disciplines, including seismic and medical imaging, computational fluid dynamics,…
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…
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…
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…
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…
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…
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…
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…