Biblio
“Weak deep priors for seismic imaging”, in SEG Technical Program Expanded Abstracts, 2020, pp. 2998-3002.
, “Wave-equation based inversion with amortized variational Bayesian inference”, in EAGE Annual Conference Proceedings, 2022, p. Session 2: Velocity model building and imaging (different domains).
, “Variational inference for artifact removal of adjoint solutions in photoacoustic problems”, ML4SEISMIC Partners Meeting. 2021.
, “Unsupervised data-guided uncertainty analysis in imaging and horizon tracking”, in SIAM Texas-Louisiana, 2020.
, “Uncertainty quantification in imaging and automatic horizon tracking—a Bayesian deep-prior based approach”, ML4SEISMIC Partners Meeting. 2021.
, “Uncertainty quantification in imaging and automatic horizon tracking—a Bayesian deep-prior based approach”, in SEG Technical Program Expanded Abstracts, 2020, pp. 1636-1640.
, “Time-Domain Wavefield Reconstruction Inversion In Tilted Transverse Isostropic Media”, in SIAM Texas-Louisiana, 2020.
, “Time-domain Wavefield Reconstruction Inversion in a TTI medium”, Georgia Institute of Technology, TR-CSE-2020-1, 2020.
, “Time-domain Wavefield Reconstruction Inversion for large-scale seismic inversion”, in SIAM Conference on Mathematical and Computational Issues in the Geosciences, 2021.
, “Time-domain wavefield reconstruction inversion for large-scale seismics”, in EAGE Annual Conference Proceedings, 2020.
, “Seismic Imaging with Uncertainty Quantification: Sampling from the Posterior with Generative Networks”, in SIAM Conference on Imaging Science, 2020.
, “Reliable amortized variational inference with physics-based latent distribution correction”, Geophysics, vol. 88, 2023.
, “Reliable amortized variational inference with conditional normalizing flows via physics-based latent distribution correction”, in IMAGE Workshop on Subsurface Uncertainty Description and Estimation - Moving Away from Single Prediction with Distribution Learning, 2022.
, “Preconditioned training of normalizing flows for variational inference in inverse problems”, in 3rd Symposium on Advances in Approximate Bayesian Inference, 2021.
, “Photoacoustic Imaging with Conditional Priors from Normalizing Flows”, in Neural Information Processing Systems (NeurIPS), 2021.
, “Parameterizing uncertainty by deep invertible networks, an application to reservoir characterization”, in SEG Technical Program Expanded Abstracts, 2020, pp. 1541-1545.
, “Multifidelity conditional normalizing flows for physics-guided Bayesian inference”, ML4SEISMIC Partners Meeting. 2021.
, “Low-cost uncertainty quantification for large-scale inverse problems”, ML4SEISMIC Partners Meeting. 2022.
, “Learned wave-based imaging - variational inference at scale”, in Delft, 2021.
, “Learned multiphysics inversion with differentiable programming and machine learning”, The Leading Edge, vol. 42, pp. 452-516, 2023.
, “Learned iterative solvers for the Helmholtz equation”, in EAGE Annual Conference Proceedings, 2019.
, “Learned imaging with constraints and uncertainty quantification”, HotCSE Seminar. 2019.
, “Learned imaging with constraints and uncertainty quantification”, in Neural Information Processing Systems (NeurIPS), 2019.
, “Julia for Geoscience”, Transform. 2022.
, “InvertibleNetworks.jl - Memory efficient deep learning in Julia”, in JuliaCon, 2021.
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