20 citations · 115 across the 24 of their papers we have counts for
24 papers
SAGE: Subsurface AI-driven Geostatistical Extraction with proxy posterior
Huseyin Tuna Erdinc, Ipsita Bhar, Rafael Orozco +2
Recent advances in generative networks have enabled new approaches to subsurface velocity model synthesis, offering a compelling alternative to traditional methods such as Full Wav…
Full-waveform variational inference with full common-image gathers and diffusion network
Yunlin Zeng, Huseyin Tuna Erdinc, Rafael Orozco +1
Accurate seismic imaging and velocity estimation are essential for subsurface characterization. Conventional inversion techniques, such as full-waveform inversion, remain computati…
Advancing Geological Carbon Storage Monitoring With 3d Digital Shadow Technology
Abhinav Prakash Gahlot, Rafael Orozco, Felix J. Herrmann
Geological Carbon Storage (GCS) is a key technology for achieving global climate goals by capturing and storing CO2 in deep geological formations. Its effectiveness and safety rely…
Probabilistic Joint Recovery Method for CO Plume Monitoring
Zijun Deng, Rafael Orozco, Abhinav Prakash Gahlot +1
Reducing CO emissions is crucial to mitigating climate change. Carbon Capture and Storage (CCS) is one of the few technologies capable of achieving net-negative CO emission…
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…
An uncertainty-aware Digital Shadow for underground multimodal CO2 storage monitoring
Abhinav Prakash Gahlot, Rafael Orozco, Ziyi Yin +1
Geological Carbon Storage GCS is arguably the only scalable net-negative CO2 emission technology available While promising subsurface complexities and heterogeneity of reservoir pr…
WISER: multimodal variational inference for full-waveform inversion without dimensionality reduction
Ziyi Yin, Rafael Orozco, Felix J. Herrmann
We present a semi-amortized variational inference framework designed for computationally feasible uncertainty quantification in 2D full-waveform inversion to explore the multimodal…
Generative Geostatistical Modeling from Incomplete Well and Imaged Seismic Observations with Diffusion Models
Huseyin Tuna Erdinc, Rafael Orozco, Felix J. Herrmann
In this study, we introduce a novel approach to synthesizing subsurface velocity models using diffusion generative models. Conventional methods rely on extensive, high-quality data…
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…
BEACON: Bayesian Experimental design Acceleration with Conditional Normalizing flows a case study in optimal monitor well placement for CO sequestration
Rafael Orozco, Abhinav Gahlot, Felix J. Herrmann
CO sequestration is a crucial engineering solution for mitigating climate change. However, the uncertain nature of reservoir properties, necessitates rigorous monitoring of CO$…
A Digital Twin for Geological Carbon Storage with Controlled Injectivity
Abhinav Prakash Gahlot, Haoyun Li, Ziyi Yin +2
We present an uncertainty-aware Digital Twin (DT) for geologic carbon storage (GCS), capable of handling multimodal time-lapse data and controlling CO2 injectivity to mitigate rese…
Probabilistic Bayesian optimal experimental design using conditional normalizing flows
Rafael Orozco, Felix J. Herrmann, Peng Chen
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…
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…
Inference of CO2 flow patterns -- a feasibility study
Abhinav Prakash Gahlot, Huseyin Tuna Erdinc, Rafael Orozco +2
As the global deployment of carbon capture and sequestration (CCS) technology intensifies in the fight against climate change, it becomes increasingly imperative to establish robus…
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…
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…
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…
Extended source imaging, a unifying framework for seismic & medical imaging
Ziyi Yin, Rafael Orozco, Philipp Witte +3
We present three imaging modalities that live on the crossroads of seismic and medical imaging. Through the lens of extended source imaging, we can draw deep connections among the…