@presentation {rex2023ML4SEISMIClsp,
	title = {Large-scale parametric PDE approximations with model-parallel Fourier neural operators},
	year = {2023},
	month = {11},
	abstract = {Solving PDEs to simulate two-phase flow is expensive since it involves the inversion of large ill-conditioned matrices. FNOs represent a special type of neural network capable of approximating solutions to two-phase flow equations. In order to speed up this computation, we develop a high-level software abstraction tool to exploit the linearly separable property of Fourier Transforms via Kronecker products. We perform a series of all-to-all operations where we partition the data to apply the operations in a distributed fashion. We apply these FNOs to predict the evolution of CO2-plumes in subsurface environments. Our model takes an input permeability model, and outputs time-varying CO2 saturations in a quick and cost-effective manner. Additionally, our research involves developing a distributed matrix-free abstraction library that can be used to represent any generic linear and nonlinear operator. This library is scalable and auto-differentiable thanks to the hand-written customized AD rules, allowing us to represent and train any network. We provide an evaluation on the accuracy of FNO simulations compared to traditional PDE simulations in solving various classes of PDEs.},
	keywords = {CCS, deep learning, Fourier neural operators, HPC, ML4SEISMIC, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/rex2023ML4SEISMIClsp},
	author = {Richard Rex and Thomas J. Grady II and Rishi Khan and Ziyi Yin and Felix J. Herrmann}
}
@article {louboutin2023lmi,
	title = {Learned multiphysics inversion with differentiable programming and machine learning},
	journal = {The Leading Edge},
	volume = {42},
	year = {2023},
	note = {(The Leading Edge)},
	month = {07},
	pages = {452-516},
	abstract = {We present the Seismic Laboratory for Imaging and Modeling/Monitoring (SLIM) open-source software framework for computational geophysics and, more generally, inverse problems involving the wave-equation (e.g., seismic and medical ultrasound), regularization with learned priors, and learned neural surrogates for multiphase flow simulations. By integrating multiple layers of abstraction, our software is designed to be both readable and scalable. This allows researchers to easily formulate their problems in an abstract fashion while exploiting the latest developments in high-performance computing. We illustrate and demonstrate our design principles and their benefits by means of building a scalable prototype for permeability inversion from time-lapse crosswell seismic data, which aside from coupling of wave physics and multiphase flow, involves machine learning.},
	keywords = {CCS, deep learning, Fourier neural operators, GCS, Imaging, inversion, invertible networks, machine learning, monitoring, multiphysics, Normalizing flows, SLIM, software, time-lapse},
	url = {https://library.seg.org/doi/10.1190/tle42070474.1},
	presentation = {https://slim.gatech.edu/Publications/Public/Journals/TheLeadingEdge/2023/louboutin2023lmi/le_software.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Journals/TheLeadingEdge/2023/louboutin2023lmi/le_software.html},
	author = {Mathias Louboutin and Ziyi Yin and Rafael Orozco and Thomas J. Grady II and Ali Siahkoohi and Gabrio Rizzuti and Philipp A. Witte and Olav M{\o}yner and Gerard J. Gorman and Felix J. Herrmann}
}
@conference {louboutin2023IMAGEloi,
	title = {Learned non-linear simultenous source and corresponding supershot for seismic imaging.},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2023},
	note = {(IMAGE, Houston)},
	month = {08},
	abstract = {Seismic imaging{\textquoteright}s main limiting factor is the scale of the involved dataset and the number of independent wave-equation solves required to migrate thousands of shots. To tackle this dimensionality curse, we introduce a learned framework that extends the conventional computationally reductive linear source superposition (e.g., via random simultaneous-source encoding) to a nonlinear learned source superposition and its corresponding learned supershot. With this method, we can image the subsurface at the cost of a one-shot migration by learning the most informative superposition of shots.},
	keywords = {deep learning, Imaging, SEG, simultaneous},
	url = {https://slimgroup.github.io/IMAGE2023/OneShot/abstract.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2023/louboutin2023IMAGEloi/louboutin2023IMAGEloi_pres.pdf},
	author = {Mathias Louboutin and Rafael Orozco and Ali Siahkoohi and Felix J. Herrmann}
}
@conference {yin2022SEGlci,
	title = {Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators},
	booktitle = {International Meeting for Applied Geoscience and Energy Expanded Abstracts},
	year = {2022},
	note = {(IMAGE, Houston)},
	month = {05},
	abstract = {Seismic monitoring of carbon storage sequestration is a challenging problem involving both fluid-flow physics and wave physics. Additionally, monitoring usually requires the solvers for these physics to be coupled and differentiable to effectively invert for the subsurface properties of interest. To drastically reduce the computational cost, we introduce a learned coupled inversion framework based on the wave modeling operator, rock property conversion and a proxy fluid-flow simulator. We show that we can accurately use a Fourier neural operator as a proxy for the fluid-flow simulator for a fraction of the computational cost. We demonstrate the efficacy of our proposed method by means of a synthetic experiment. Finally, our framework is extended to carbon sequestration forecasting, where we effectively use the surrogate Fourier neural operator to forecast the CO$_2$ plume in the future at near-zero additional cost.},
	keywords = {CCS, deep learning, Fourier neural operators, inversion, machine learning, multiphysics, SEG, time-lapse},
	doi = {10.1190/image2022-3722848.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/yin2022SEGlci/paper.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/yin2022SEGlci/index.html},
	software = {https://github.com/slimgroup/FNO4CO2},
	author = {Ziyi Yin and Ali Siahkoohi and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {louboutin2022ML4SEISMIClew,
	title = {Learned extensions for wave-based simulation and inversion},
	year = {2022},
	month = {11},
	abstract = {We introduce a new method that explores velocities as an operator (extended velocities) for wave-equation based inversion. Through this extended formulation, we obtain the known benefits of working with subsurface offset volumes. The offset-dependence of these volumes has been studied in the linear case, i.e as part of extended Born scattering and extended least-squares reverse-time migration, but has been avoided for non-linear inversion due to computational concderns and challenges. By using techniques from randomized linear algebra, we will show that we can work with extended velocities for inversion while maintaining an acceptable computational cost much lower than solving one PDE per extended velocity model.},
	keywords = {deep learning, Imaging, ML4SEISMIC, SLIM, wave equation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/louboutin2022ML4SEISMIClew/index.html},
	author = {Mathias Louboutin and Yadhu Kartha and Rafael Orozco and Felix J. Herrmann}
}
@presentation {siahkoohi2022ML4SEISMICluq,
	title = {Low-cost uncertainty quantification for large-scale inverse problems},
	year = {2022},
	month = {11},
	abstract = {Bayesian inference for large-scale inverse problems is challenged by the computationally costly forward operator evaluations during posterior distribution sampling. Recent advances in variational inference and deep learning reduce these costs by pretraining a neural network capable of sampling the posterior distribution for previously unseen observed data. In geophysical applications, however, the accuracy of these methods depends on sufficiently capturing subsurface variability through a training dataset, which is challenging given the heterogeneity of the Earth{\textquoteright}s subsurface and our lack of access to it. Moreover, these methods may be unreliable in the presence of data distribution shifts, e.g., a change in the number of source experiments, noise distribution, or geological features to be imaged. As such, we present a solution that increases the robustness of deep-learning-based Bayesian inference approaches when faced with changes in data distribution. Our proposed method involves a physics-based adaptation to the latent distribution of a conditional normalizing flow that is pretrained to approximate the posterior distribution for previously unseen data. Instead of feeding standard Gaussian latent samples to the conditional normalizing flow, this method parameterizes the latent distribution by a Gaussian distribution with an unknown mean and diagonal covariance, estimated by minimizing the Kullback-Leibler divergence between predicted and true posterior distributions. This method is applicable to a wide range of inverse problems and has the potential to significantly reduce the costs of Bayesian variational inference. By means of a realistic seismic imaging example we demonstrate that the proposed latent distribution adaptation method mitigates the Bayesian inference errors induced by data distribution shifts, including shifts in the forward model and prior distribution.},
	keywords = {Inverse problems, ML4SEISMIC, Normalizing flows, seismic imaging, SLIM, Uncertainty quantification, Variational Inference},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/siahkoohi2022ML4SEISMICluq/siahkoohi2022ML4SEISMICluq_pres.pdf},
	author = {Ali Siahkoohi and Gabrio Rizzuti and Rafael Orozco and Felix J. Herrmann}
}
@conference {herrmann2021Delftlwi,
	title = {Learned wave-based imaging - variational inference at scale},
	booktitle = {Delft},
	year = {2021},
	note = {(Delft, virtual)},
	month = {06},
	abstract = {High dimensionality, complex physics, and lack of access to the ground truth rank medical ultrasound and seismic imaging amongst the most challenging problems in the computational imaging sciences. If these challenges were not bad enough, modern applications of computational imaging increasingly call for the assessment of uncertainty on the image itself and on subsequent tasks. During this talk, I will show how recent developments in Normalizing Flows, a new type of invertible neural networks, can be used to cast wave-based imaging into a scalable Bayesian framework. Contrary to conventional methods, where sample images are drawn from the posterior distribution during inversion, our approach trains Normalizing Flows capable of generating samples from the posterior. Aside from greatly reducing the computational cost, this approach gives us access to the image itself (via Maximum a posteriori or mean estimation) and its multidimensional statistical distribution including its pointwise variance.},
	keywords = {medical imaging, Normalizing flows, seismic imaging, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/Delft/2021/herrmann2021Delftlwi/herrmann2021Delftlwi.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/Delft/2021/herrmann2021Delftlwi/herrmann2021Delftlwi.mp4},
	author = {Felix J. Herrmann and Ali Siahkoohi and Rafael Orozco and Gabrio Rizzuti and Philipp A. Witte and Mathias Louboutin}
}
@conference {siahkoohi2021SEGlbe,
	title = {Learning by example: fast reliability-aware seismic imaging with normalizing flows},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2021},
	note = {(IMAGE, Denver)},
	month = {09},
	pages = {1580-1585},
	abstract = {Uncertainty quantification provides quantitative measures on the reliability of candidate solutions of ill-posed inverse problems. Due to their sequential nature, Monte Carlo sampling methods require large numbers of sampling steps for accurate Bayesian inference and are often computationally infeasible for large-scale inverse problems, such as seismic imaging. Our main contribution is a data-driven variational inference approach where we train a normalizing flow (NF), a type of invertible neural net, capable of cheaply sampling the posterior distribution given previously unseen seismic data from neighboring surveys. To arrive at this result, we train the NF on pairs of low- and high-fidelity migrated images. In our numerical example, we obtain high-fidelity images from the Parihaka dataset and low-fidelity images are derived from these images through the process of demigration, followed by adding noise and migration. During inference, given shot records from a new neighboring seismic survey, we first compute the reverse-time migration image. Next, by feeding this low-fidelity migrated image to the NF we gain access to samples from the posterior distribution virtually for free. We use these samples to compute a high-fidelity image including a first assessment of the image{\textquoteright}s reliability. To our knowledge, this is the first attempt to train a conditional network on what we know from neighboring images to improve the current and assess its reliability.},
	keywords = {deep learning, Normalizing flows, SEG, seismic imaging, Variational Inference},
	doi = {10.1190/segam2021-3581836.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2021/siahkoohi2021SEGlbe/abstract.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2021/siahkoohi2021SEGlbe/Tue-09-15-siahkoohi.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2021/siahkoohi2021SEGlbe/Tue-09-15-siahkoohi.mp4},
	software = {https://github.com/slimgroup/Software.SEG2021},
	author = {Ali Siahkoohi and Felix J. Herrmann}
}
@conference {herrmann2021SEGWSlts,
	title = {Low-cost time-lapse seismic imaging of CCS with the joint recovery model},
	booktitle = {SEG Workshop on Geophysical Challenges in Presalt Carbonates; virtual},
	year = {2021},
	note = {(IMAGE Workshop, virtual)},
	month = {10},
	keywords = {born, CCS, Imaging, JRM, multiple, SEG, workshop},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2021/herrmann2021SEGWSlts/herrmann2021SEGWSlts_pres.pdf},
	author = {Felix J. Herrmann and Mathias Louboutin and Ziyi Yin and Philipp A. Witte}
}
@techreport {louboutin2021NIPSmcte,
	title = {Low-memory stochastic backpropagation with multi-channel randomized trace estimation},
	number = {TR-CSE-2021-1},
	year = {2021},
	month = {06},
	abstract = {Thanks to the combination of state-of-the-art accelerators and highly optimized open software frameworks, there has been tremendous progress in the performance of deep neural networks. While these developments have been responsible for many breakthroughs, progress towards solving large-scale problems, such as video encoding and semantic segmentation in 3D, is hampered because access to on-premise memory is often limited. Instead of relying on (optimal) checkpointing or invertibility of the network layers{\textendash}-to recover the activations during backpropagation{\textendash}-we propose to approximate the gradient of convolutional layers in neural networks with a multi-channel randomized trace estimation technique. Compared to other methods, this approach is simple, amenable to analyses, and leads to a greatly reduced memory footprint. Even though the randomized trace estimation introduces stochasticity during training, we argue that this is of little consequence as long as the induced errors are of the same order as errors in the gradient due to the use of stochastic gradient descent. We discuss the performance of networks trained with stochastic backpropagation and how the error can be controlled while maximizing memory usage and minimizing computational overhead.},
	keywords = {Convolutions, HPC, Low Memory, machine learning, randomized linear algebra},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2021/louboutin2021NIPSmcte/louboutin2021NIPSmcte.pdf},
	software = {https://github.com/slimgroup/XConv},
	author = {Mathias Louboutin and Ali Siahkoohi and Rongrong Wang and Felix J. Herrmann}
}
@article {yang2020lrpo,
	title = {Low-rank representation of omnidirectional subsurface extended image volumes},
	journal = {Geophysics},
	volume = {86},
	number = {3},
	year = {2021},
	note = {(Geophysics)},
	month = {1},
	pages = {1MJ-WA152},
	abstract = {Subsurface-offset gathers play an increasingly important role in seismic imaging. These gathers are used during velocity model building and inversion of rock properties from amplitude variations. While powerful, these gathers come with high computational and storage demands to form and manipulate these high dimensional objects. This explains why only limited numbers of image gathers are computed over a limited offset range. We avoid these high costs by working with highly compressed low-rank factorizations. We arrive at these factorizations via a combination of probings with the double two-way wave equation and randomized singular value decompositions. In turn, the resulting factorizations give us access to all subsurface offsets without having to form the full extended image volumes. The latter is computationally prohibitive because extended image volumes are quadratic in image size. As a result, we can easily handle situations where conventional horizontal offset gathers are no longer focused. More importantly, the factorization also provides a mechanism to use the invariance relation of extended image volumes for velocity continuation. With this technique, extended image volumes for one background velocity model can directly be mapped to those of another background velocity model. Our low-rank factorization inherits this invariance property so we incur factorization costs only once when examining different imaging scenarios. Because all imaging experiments only involve the factors, they are computationally cheap with costs that scale with the rank of the factorization. We validate our methodology on 2D synthetics including a challenging imaging example with salt. Our experiments show that our low-rank factorization parameterizes extended image volumes naturally. Instead of brute force explicit cross-correlations between shifted source and receiver wavefields, our approach relies on the underlying linear-algebra structure that enables us to work with these objects without incurring unfeasible demands on computation and storage.},
	keywords = {extended image volumes, invariance relationship, low rank, power schemes, randomized linear algebra},
	doi = {10.1190/geo2020-0152.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2021/yang2020lrpo/Paper_final.html},
	author = {Mengmeng Yang and Marie Graff and Rajiv Kumar and Felix J. Herrmann}
}
@techreport {sharan2020lsh,
	title = {Large scale high-frequency wavefield reconstruction with recursively weighted matrix factorizations},
	number = {TR-CSE-2020-4},
	year = {2020},
	month = {10},
	abstract = {Acquiring seismic data on a regular periodic fine grid is challenging. By exploiting the low-rank approximation property of fully sampled seismic data in some transform domain, low-rank matrix completion offers a scalable way to reconstruct seismic data on a regular periodic fine grid from coarsely randomly sampled data acquired in the field. While wavefield reconstruction have been applied successfully at the lower end of the spectrum, its performance deteriorates at the higher frequencies where the low-rank assumption no longer holds rendering this type of wavefield reconstruction ineffective in situations where high resolution images are desired. We overcome this shortcoming by exploiting similarities between adjacent frequency slices explicitly. During low-rank matrix factorization, these similarities translate to alignment of subspaces of the factors, a notion we propose to employ as we reconstruct monochromatic frequency slices recursively starting at the low frequencies. While this idea is relatively simple in its core, to turn this recent insight into a successful scalable wavefield reconstruction scheme for 3D seismic requires a number of important steps. First, we need to move the weighting matrices, which encapsulate the prior information from adjacent frequency slices, from the objective to the data misfit constraint. This move considerably improves the performance of the weighted low-rank matrix factorization on which our wavefield reconstructions is based. Secondly, we introduce approximations that allow us to decouple computations on a row-by-row and column-by-column basis, which in turn allow to parallelize the alternating optimization on which our low-rank factorization relies. The combination of weighting and decoupling leads to a computationally feasible full-azimuth wavefield reconstruction scheme that scales to industry-scale problem sizes. We demonstrate the performance of the proposed parallel algorithm on a 2D field data and on a 3D synthetic dataset. In both cases our approach produces high-fidelity broadband wavefield reconstructions from severely subsampled data.},
	keywords = {5D reconstruction, compressed sensing, frequency-domain, parallel, signal processing},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2020/sharan2020lsh/sharan2020lsh.html},
	author = {Shashin Sharan and Yijun Zhang and Oscar Lopez and Felix J. Herrmann}
}
@article {witte2018alf,
	title = {A large-scale framework for symbolic implementations of seismic inversion algorithms in Julia},
	journal = {Geophysics},
	volume = {84},
	number = {3},
	year = {2019},
	note = {(Geophysics)},
	month = {03},
	pages = {F57-F71},
	abstract = {Writing software packages for seismic inversion is a very challenging task, since problems such as full-waveform inversion or least-squares imaging are both algorithmically and computationally demanding due to the large number of unknown parameters and the fact that we are propagating waves over many wavelengths. Software frameworks therefore need to combine both versatility and performance to provide geophysicists with the means and flexibility to implement complex algorithms that scale to exceedingly large 3D problems. Following these principles, we introduce the Julia Devito Inversion framework, an open-source software package in Julia for large-scale seismic modeling and inversion based on Devito, a domain-specific language compiler for automatic code generation. The framework consists of matrix-free linear operators for implementing seismic inversion algorithms that closely resembles the mathematical notation, a flexible resilient parallelization and an interface to Devito for generating optimized stencil code to solve the underlying wave equations. In comparison to many manually optimized industry codes written in low-level languages, our software is built on the idea of independent layers of abstractions and user interfaces with symbolic operators, making it possible to manage both the complexity of algorithms and performance optimizations, while preserving modularity, which allows for a level of expressiveness needed to formulate a broad range of wave-equation-based inversion problems. Through a series of numerical examples, we demonstrate that this allows users to implement algorithms for waveform inversion and imaging as simple Julia scripts that scale to large-scale 3D problems; thus providing a truly performant research and production framework.},
	keywords = {FWI, inversion, LSRTM, Modeling, software},
	doi = {10.1190/geo2018-0174.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2019/witte2018alf/witte2018alf.pdf},
	author = {Philipp A. Witte and Mathias Louboutin and Navjot Kukreja and Fabio Luporini and Michael Lange and Gerard J. Gorman and Felix J. Herrmann}
}
@presentation {herrmann2019HOTCSEliwcuq,
	title = {Learned imaging with constraints and uncertainty quantification},
	year = {2019},
	note = {(HotCSE)},
	abstract = {We outline new approaches to incorporate ideas from convolutional networks into wave-based least-squares imaging. The aim is to combine hand-crafted constraints with deep convolutional networks allowing us to directly train a network capable of generating samples from the posterior. The main contributions include combination of weak deep priors with hard handcrafted constraints and a possible new way to sample the posterior.},
	keywords = {deep learning, Expectation Maximization, HotCSE, Imaging, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Lectures/HotCSE/2019/herrmann2019HOTCSEliwcuq/herrmann2019HOTCSEliwcuq.pdf},
	author = {Felix J. Herrmann and Ali Siahkoohi and Gabrio Rizzuti}
}
@conference {herrmann2019NIPSliwcuc,
	title = {Learned imaging with constraints and uncertainty quantification},
	booktitle = {Neural Information Processing Systems (NeurIPS)},
	year = {2019},
	note = {(NIPS, Vancouver)},
	month = {12},
	abstract = {We outline new approaches to incorporate ideas from convolutional networks into wave-based least-squares imaging. The aim is to combine hand-crafted constraints with deep convolutional networks allowing us to directly train a network capable of generating samples from the posterior. The main contributions include combination of weak deep priors with hard handcrafted constraints and a possible new way to sample the posterior.},
	keywords = {constraint, deep learning, Imaging, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/NIPS/2019/herrmann2019NIPSliwcuc/herrmann2019NIPSliwcuc.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/NIPS/2019/herrmann2019NIPSliwcuc/herrmann2019NIPSliwcuc_pres.pdf},
	url2 = {https://openreview.net/pdf?id=Hyet2Q29IS},
	author = {Felix J. Herrmann and Ali Siahkoohi and Gabrio Rizzuti}
}
@conference {rizzuti2019EAGElis,
	title = {Learned iterative solvers for the Helmholtz equation},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2019},
	note = {(EAGE, Copenhagen)},
	month = {06},
	abstract = {We propose a {\textquoteleft}learned{\textquoteright} iterative solver for the Helmholtz equation, by combining traditional Krylov-based solvers with machine learning. The method is, in principle, able to circumvent the shortcomings of classical iterative solvers, and has clear advantages over purely data-driven ap- proaches. We demonstrate the effectiveness of this approach under a 1.5-D assumption, when ade- quate a priori information about the velocity distribution is known.},
	keywords = {EAGE, Helmholtz, Iterative, machine learning},
	doi = {10.3997/2214-4609.201901542},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2019/rizzuti2019EAGElis/rizzuti2019EAGElis.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2019/rizzuti2019EAGElis/rizzuti2019EAGElis_pres.pdf},
	author = {Gabrio Rizzuti and Ali Siahkoohi and Felix J. Herrmann}
}
@conference {yang2019SEGlrr,
	title = {Low-rank representation of subsurface extended image volumes with power iterations},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2019},
	note = {(SEG, San Antonio)},
	month = {09},
	pages = {4470-4474},
	abstract = {Full subsurface extended image volumes (EIVs) contain abundant information such as subsurface image gathers used in imaging, interpretation of rock properties or velocity analysis, but are expensive in term of computational cost and storage requirement due to their large size. Yet, due to their redundant information, monochromatic EIVs exhibit a low-rank structure that allows us to get a good approximation at a computational cost proportional to the rank k , while conventional techniques require at least the number of sources. Such low-rank factorized EIVs are computed thanks to the randomized singular values decomposition (SVD) algorithm. However, recent developments on low-rank approximations of EIVs raise two major questions. First, monochromatic EIVs rely on time-harmonic wave equation solvers, which do not scale well to realistic 3D models. Second, the rank of the monochromatic EIVs increases with the frequency, which yields increasing computational costs and storage. Here, we propose a construction approach based on a time-domain finite-difference wave-equation solver with time stepping, which is combined to power iteration schemes in the randomized SVD algorithm to accelerate the decay of the singular values. We compare the performances of simultaneous iterations, block-Krylov iterations and the original randomized SVD on the computation of the EIV for a small section of the Marmousi model. Then, we show further results on the full Marmousi model to validate our approach.},
	keywords = {full extended image volumes, power iterations, randomized SVD, SEG, time domain},
	doi = {10.1190/segam2019-3215961.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/yang2019SEGlrr/yang2019SEGlrr.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/yang2019SEGlrr/yang2019SEGlrr_pres.pdf},
	author = {Mengmeng Yang and Marie Graff and Rajiv Kumar and Felix J. Herrmann}
}
@conference {kumar2018SEGlreiv,
	title = {Low-rank representation of extended image volumes{\textendash}{\textendash}applications to imaging and velocity continuation},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2018},
	note = {(SEG, Anaheim)},
	month = {10},
	pages = {4418-4422},
	abstract = {In this paper, we present a cost-effective low-rank factorized form of computing the full subsurface extended image volumes without explicitly calculating the receiver wavefields. The propose approach is computationally feasible, which exploits the low-rank structure of full subsurface extended image volumes organized as a matrix, thus avoiding the customary loop over sources. Using carefully selected stylized examples, we show how conventional migration images can be extracted from the low-rank factorized form. We also present a velocity continuation procedure that uses the same low-rank form and allows us to map the extended image for one velocity model to another velocity model without recomputing all the source wavefields.},
	keywords = {image volume, low-rank, SEG, velocity continuation},
	doi = {10.1190/segam2018-2998404.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/kumar2018SEGlreiv/kumar2018SEGlreiv.html},
	author = {Rajiv Kumar and Marie Graff-Kray and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {witte2017SINBADFals,
	title = {A large-scale framework in Julia for fast prototyping of seismic inversion algorithms},
	year = {2017},
	publisher = {SINBAD},
	abstract = {We present our progress on a large-scale seismic modeling workflow in Julia for wave-equation based inversion. The software offers a range of high-level abstractions to easily express PDE constrained optimization problems in terms of linear algebra expressions, while utilizing the DSL Devito to symbolically express the underlying PDEs and to generate fast and parallel code for solving them. Data containers and linear operators can be set up without much effort from input SEG-Y data and scale to large-scale 3D applications. This talk provides an overview of the basic functionalities of our software and applications to least squares imaging and 3D FWI.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/witte2017SINBADFals/witte2017SINBADFals.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/witte2017SINBADFals/witte2017SINBADFals.mov},
	author = {Philipp A. Witte and Mathias Louboutin and Felix J. Herrmann}
}
@techreport {witte2016OGHPClst,
	title = {A large-scale time-domain seismic modeling and inversion workflow in Julia},
	number = {TR-EOAS-2017-1},
	year = {2017},
	institution = {UBC},
	abstract = {We present our initial steps towards the development of a large-scale seismic modeling workflow in Julia that provides a framework for wave equation based inversion methods like full waveform inversion or least squares migration. Our framework is based on the Devito, a finite difference domain specific language compiler that generates highly optimized and parallel code. We develop a flexible workflow that is based on abstract matrixfree linear operators and enables developers to write code that closely resembles the underlying math, while at the same time leveraging highly optimized wave equation solvers, allowing us to solve large-scale three-dimensional inverse problems.},
	keywords = {HPC, inversion, Modelling},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2017/witte2016OGHPClst/witte2016OGHPClst.pdf},
	author = {Philipp A. Witte and Mathias Louboutin and Gerard Gorman and Felix J. Herrmann}
}
@conference {witte2017SIAMlsw,
	title = {Large-scale workflows for wave-equation based inversion in Julia},
	booktitle = {SIAM Conference on Computational Science and Engineering},
	year = {2017},
	note = {(SIAM Conference on Computational Science and Engineering, Atlanta)},
	month = {02-03},
	abstract = {Full-waveform inversion is a PDE-constrained optimisation problem involving massive amounts of data (petabytes) and large numbers of unknowns (O(10^9)). This well known compute-intensive and data-intensive is extremely challenging for several reasons. First, there is the complexity of having to handle extremely large data volumes with metadata related to experimental details in the field, and the discretization of the unknown earth parameters and approximate physics. Second, reduced or adjoint-state methods call for computationally intensive PDE solves for each source experiment (of which there are thousands) for each iteration of a gradient-based optimization scheme. The talks will give an overview how carefully chosen layers of abstraction can help manage both the complexity and scale of inversion while still achieving the high degree of computational performance required to make full-waveform a practical tool. Specifically, the presentations will focus on domain specific stencil language for time-stepping methods to solve various types of wave equations and on abstracts for large-scale parallel optimization frameworks.},
	keywords = {Full-waveform inversion, Julia, large scale, least-squares migration, SIAM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SIAM/2017/witte2017SIAMlsw/witte2017SIAMlsw_pres.mov},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SIAM/2017/witte2017SIAMlsw/witte2017SIAMlsw_pres.pdf},
	author = {Philipp A. Witte and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {louboutin2017SINBADFldi,
	title = {Latest developments in Devito},
	year = {2017},
	publisher = {SINBAD},
	abstract = {We present an overview of the latest developments in Devito. We introduced Devito in the previous meeting as a prototype finite-difference DSL for seismic modelling and inversion. We are presenting here the latest improvements and functionalities of Devito. We will also discuss the current future plans as well as non-supported features that the audience may be interested in. This presentation will be followed by/mixed with a hands-in tutorial if the time and resources allows it.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/louboutin2017SINBADFldi/louboutin2017SINBADFldi.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/louboutin2017SINBADFldi/louboutin2017SINBADFldi.mov},
	author = {Mathias Louboutin and Michael Lange and Fabio Luporini and Navjot Kurjeka and Jan Hueckelheim and Gerard Gorman and Philipp A. Witte and Felix J. Herrmann}
}
@article {oghenekohwo2016GEOPctl,
	title = {Low-cost time-lapse seismic with distributed compressive sensing{\textendash}-Part 1: exploiting common information among the vintages},
	journal = {Geophysics},
	volume = {82},
	number = {3},
	year = {2017},
	note = {(Geophysics)},
	month = {05},
	pages = {P1-P13},
	abstract = {Time-lapse seismic is a powerful technology for monitoring a variety of subsurface changes due to reservoir fluid flow. However, the practice can be technically challenging when one seeks to acquire colocated time-lapse surveys with high degrees of replicability among the shot locations. We have determined that under "ideal" circumstances, in which we ignore errors related to taking measurements off the grid, high-quality prestack data can be obtained from randomized subsampled measurements that are observed from surveys in which we choose not to revisit the same randomly subsampled on-the-grid shot locations. Our acquisition is low cost because our measurements are subsampled. We have found that the recovered finely sampled prestack baseline and monitor data actually improve significantly when the same on-the-grid shot locations are not revisited. We achieve this result by using the fact that different time-lapse data share information and that nonreplicated (on-the-grid) acquisitions can add information when prestack data are recovered jointly. Whenever the time-lapse data exhibit joint structure{\textendash}-i.e., they are compressible in some transform domain and share information{\textendash}-sparsity-promoting recovery of the "common component" and "innovations," with respect to this common component, outperforms independent recovery of the prestack baseline and monitor data. The recovered time-lapse data are of high enough quality to serve as the input to extract poststack attributes used to compute time-lapse differences. Without joint recovery, artifacts{\textendash}-due to the randomized subsampling{\textendash}-lead to deterioration of the degree of repeatability of the time-lapse data. We tested this method by carrying out experiments with reliable statistics from thousands of repeated experiments. We also confirmed that high degrees of repeatability are achievable for an ocean-bottom cable survey acquired with time-jittered continuous recording.},
	keywords = {Acquisition, joint recovery method, marine, random sampling, time-lapse seismic},
	doi = {10.1190/geo2016-0076.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2017/oghenekohwo2016GEOPctl/oghenekohwo2016GEOPctl.html},
	author = {Felix Oghenekohwo and Haneet Wason and Ernie Esser and Felix J. Herrmann}
}
@article {wason2016GEOPctl,
	title = {Low-cost time-lapse seismic with distributed compressive sensing{\textendash}-Part 2: impact on repeatability},
	journal = {Geophysics},
	volume = {82},
	number = {3},
	year = {2017},
	note = {(Geophysics)},
	month = {05},
	pages = {P15-P30},
	abstract = {Irregular or off-the-grid spatial sampling of sources and receivers is inevitable in field seismic acquisitions. Consequently, time-lapse surveys become particularly expensive since current practices aim to replicate densely sampled surveys for monitoring changes occurring in the reservoir due to hydrocarbon production. We demonstrate that under certain circumstances, high-quality prestack data can be obtained from cheap randomized subsampled measurements that are observed from nonreplicated surveys. We extend our time-jittered marine acquisition to time-lapse surveys by designing acquisition on irregular spatial grids that render simultaneous, subsampled and irregular measurements. Using the fact that different time-lapse data share information and that nonreplicated surveys add information when prestack data are recovered jointly, we recover periodic densely sampled and colocated prestack data by adapting the recovery method to incorporate a regularization operator that maps traces from an irregular spatial grid to a regular periodic grid. The recovery method is, therefore, a combined operation of regularization, interpolation (estimating missing fine-grid traces from subsampled coarse-grid data), and source separation (unraveling overlapping shot records). By relaxing the insistence on replicability between surveys, we find that recovery of the time-lapse difference shows little variability for realistic field scenarios of slightly nonreplicated surveys that suffer from unavoidable natural deviations in spatial sampling of shots (or receivers) and pragmatic compressed-sensing based nonreplicated surveys when compared to the "ideal" scenario of exact replicability between surveys. Moreover, the recovered densely sampled prestack baseline and monitor data improve significantly when the acquisitions are not replicated, and hence can serve as input to extract poststack attributes used to compute time-lapse differences. Our observations are based on experiments conducted for an ocean-bottom cable survey acquired with time-jittered continuous recording assuming source equalization (or same source signature) for the time-lapse surveys and no changes in wave heights, water column velocities or temperature and salinity profiles, etc.},
	keywords = {joint recovery method, Marine acquisition, off-the-grid recovery, Optimization, random sampling, time-lapse seismic},
	doi = {10.1190/geo2016-0252.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2017/wason2016GEOPctl/wason2016GEOPctl.html},
	author = {Haneet Wason and Felix Oghenekohwo and Felix J. Herrmann}
}
@presentation {graff2017SINBADFlrp,
	title = {Low-rank representation of omnidirectional subsurface extended image volumes},
	year = {2017},
	publisher = {SINBAD},
	abstract = {Extended image volumes are an important migration tool in seismic exploration. However the computation and the storage of omnidirectional subsurface extended image volumes are usually prohibitive. That is why some solutions have been already proposed for instance by focusing on horizontal offsets only. In our work, we will consider a linear algebra approach to deal with the low-rank representation of extended image volumes with full offsets. We will never build entirely the resulting matrix but get only actions of it on well-chosen probing vectors, based on Low-Rank decomposition or randomized SVD. This representation allows us to have access to all the energy of the extended image volume matrix and still limits the storage of the information and the computational cost.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/graff2017SINBADFlrp/graff2017SINBADFlrp.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/graff2017SINBADFlrp/graff2017SINBADFlrp.mov},
	author = {Marie Graff-Kray and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {witte2016SINBADFlst,
	title = {A large-scale time-domain modeling and inversion workflow in Julia},
	year = {2016},
	publisher = {SINBAD},
	abstract = {We present our initial steps towards the development of a large-scale seismic modeling workflow in Julia which provides a framework for wave-equation based inversion methods like full waveform inversion or least squares migration. Our framework is based on the Devito tool for optimized finite difference computations that generates highly optimized code from symbolic PDEs. We aim at developing a flexible workflow which is based on abstract operators and allows coding that is {\textquotedblleft}close to the math{\textquotedblright}, while at the same time relying on extremely fast and efficient, state of the art wave equation solvers.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFlst/witte2016SINBADFlst.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFlst/witte2016SINBADFlst.mov},
	author = {Philipp A. Witte and Felix J. Herrmann}
}
@conference {chai2016SEGlbm,
	title = {A linearized Bregman method for compressive waveform inversion},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2016},
	note = {(SEG, Dallas)},
	month = {10},
	pages = {1449-1454},
	abstract = {We present an implementation of a recently developed and relatively simple linearized Bregman method to solve the large-scale $\ell_1$-norm sparsity-promoting Gauss-Newton (GN) full-waveform inversion (FWI) problem. Numerical experiments demonstrate that: the much simpler linearized Bregman method does a comparable and even superior job compared to a state-of-the-art $\ell_1$-norm solver SPGL1, which is previously used in the modified Gauss-Newton FWI; the linearized Bregman method is also more efficient (faster) than SPGL1; the FWI result with the linearized Bregman method solving $\ell_1$-norm sparsity-promoting problems to get the model updates is better than that obtained by solving $\ell_2$-norm least-squares problems to get the model updates.},
	keywords = {Bregman method, SEG, waveform inversion},
	doi = {10.1190/segam2016-13848105.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/chai2016SEGlbm/chai2016SEGlbm.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/chai2016SEGlbm/chai2016SEGlbm_pres.pdf},
	author = {Xintao Chai and Mengmeng Yang and Philipp A. Witte and Rongrong Wang and Zhilong Fang and Felix J. Herrmann}
}
@presentation {wason2016SINBADFlcr,
	title = {Low-cost, randomized 3D towed-marine time-lapse seismic acquisition},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Most often conventional 3D towed-marine seismic data acquisition suffers from limited illumination of subsurface due to narrow azimuth. Although, acquisition techniques such as Ocean Bottom Cable (OBC), Ocean Bottom Node (OBN), Rich-Azimuth (RAZ) acquisition, Multi-Azimuth (MAZ) acquisition have been developed to illuminate the subsurface from all possible angles, these acquisition techniques are expensive. Motivated by the design principles of compressed sensing, we acquire randomly subsampled and simultaneous (e.g., simultaneous long offset) 3D towed-marine time-lapse seismic data for few (random) azimuths, and recover densely sampled multiple azimuth baseline and monitor data via sparsity promotion and rank minimization. Our acquisition is low cost since we have subsampled measurements.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/wason2016SINBADFlcr/wason2016SINBADFlcr.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/wason2016SINBADFlcr/marine_acq_mask.pdf},
	author = {Rajiv Kumar and Felix Oghenekohwo and Shashin Sharan and Haneet Wason and Felix J. Herrmann}
}
@presentation {lopez2016SINBADFlrm,
	title = {Low-rank matrix recovery for parallel architectures},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Low-rank matrix recovery (LRMR) techniques offer potential tools for frugal seismic data acquisition, where dense acquisition is replaced by large-scale optimization. This shift of focus means that efficient numerical methods are critical for successful LRMR implementation in seismic applications. In this talk, we extend the rank-penalization methodology to a parallelizable framework. We adopt a factorization-based alternating minimization scheme and decouple it into an independent system of simpler sub-problems that can be handled in parallel. The methodology is flexible, where the approach can be adapted to the number of workers available. Numerical experiments are conducted to demonstrate that the quality of reconstruction is comparable to existing methods at a fraction of the computational time.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/lopez2016SINBADFlrm/lopez2016SINBADFlrm.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/lopez2016SINBADFlrm/lopez2016SINBADFlrm.mov},
	author = {Oscar Lopez and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {kumar2016SINBADFlrm,
	title = {Low-rank methods for on-the-fly slicing \& dicing of seismic data \& image volumes},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Conventional oil and gas fields are increasingly difficult to explore and produce, which calls for more complex wave-equation based inversion (WEI) algorithms that require dense long-offset samplings. These requirements result in an exponential growth in data volumes and prohibitive demands on computational resources. In this work, we propose a fast, resilient, and scalable wave-equation based inversion methodology for both the data and image-domain, which can handle complicated wave physics. First we show that both the data and image domains exhibit low-rank structure in a transform-domain, which can be exploited to compress the dense data or image volumes. Then, by accessing information from the compressed volumes on-the fly, we devise a scalable computational inversion framework driven by gradient calculations, which works with small subsets of source experiments. In the full-waveform inversion context, we demonstrate the efficacy of low-rank interpolation to improve downstream inversion results compared to merely inverting the velocity model using the subsampled data volume directly. We demonstrate the effectiveness of the proposed framework on two different waveform-inversion formulations, specifically performing full-waveform inversion on 5D data set generated using the overthrust model, and wave-equation based migration velocity analysis on the Marmousi model. This is joint work with Curt Da Silva and Yiming Zhang.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/kumar2016SINBADFlrm/kumar2016SINBADFlrm.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/kumar2016SINBADFlrm/kumar2016SINBADFlrm.mov},
	author = {Rajiv Kumar and Curt Da Silva and Yiming Zhang and Felix J. Herrmann}
}
@presentation {wason2015SINBADldr,
	title = {Latest developments in randomized marine acquisition and source separation},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We present the latest developments in randomized (simultaneous) marine acquisition for time-lapse surveys. We also show a comparison between two source separation algorithms {\textemdash} sparsity-promotion and rank-minimization {\textemdash} for simultaneous data.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/wason2015SINBADldr/wason2015SINBADldr.pdf},
	author = {Haneet Wason and Rajiv Kumar and Felix Oghenekohwo and Felix J. Herrmann}
}
@techreport {kumar2015EAGElse,
	title = {Least-squares extended imaging with surface-related multiples},
	number = {TR-EOAS-2015-1},
	year = {2015},
	month = {01},
	institution = {UBC},
	abstract = {Common image gathers are used in building velocity models, inverting for anisotropy parameters, and analyzing reservoir attributes. Typically, only primary reflections are used to form image gathers as multiples can cause artifacts that interfere with the events of interest. However, it has been shown that multiples can actually provide extra illumination of the subsurface, especially for delineating the near-surface features. In this paper, we aim to form common image gathers directly from the data with surface related multiples by applying concepts that have been used to successfully deal with surface-related multiples in imaging. We achieve this by effectively inverting an extended migration operator. This results in extended images with better near-surface illumination that are free of artifacts that can hamper velocity analysis. In addition, being able to generate extended images directly from the total data avoids the need for (time-consuming) pre-processing. Synthetic examples on a layered model show that the proposed formulation is promising.},
	keywords = {image gathers, surface-related multiples},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2015/kumar2015EAGElse/kumar2015EAGElse.html},
	author = {Rajiv Kumar and Ning Tu and Tristan van Leeuwen and Felix J. Herrmann}
}
@conference {kumar2015CSEGlse,
	title = {Least-squares extended imaging with surface-related multiples},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2015},
	note = {(CSEG, Calgary)},
	month = {05},
	abstract = {Common image gathers are used in building velocity models, inverting anisotropy parameters, and analyzing reservoir attributes. Often primary reflections are used to form image gathers and multiples are typically attenuated in processing to remove strong coherent artifacts generated by multiples that interfere with the imaged reflectors. However, researchers have shown that, if correctly used, multiples can actually provide extra illumination of the subsurface in seismic imaging, especially for delineating the near-surface features. In this work, we borrow ideas from literatures on imaging with surface-related multiples, and apply these ideas to extended imaging. This way we save the massive computation cost in separating multiples from the data before using them during the formation of image gathers. Also, we mitigate the strong coherent artifacts generated by multiples which can send the migration velocity analysis type algorithms in wrong direction. Synthetic examples on a three-layer model show the efficacy of the proposed formulation.},
	keywords = {CSEG, image-gather, surface-related multiples},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2015/kumar2015CSEGlse/kumar2015CSEGlse.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2015/kumar2015CSEGlse/kumar2015CSEGlse_pres.pdf},
	author = {Rajiv Kumar and Ning Tu and Tristan van Leeuwen and Felix J. Herrmann}
}
@conference {esser2015EAGElcs,
	title = {A lifted $\ell_1/\ell_2$ constraint for sparse blind deconvolution},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2015},
	note = {(EAGE, Madrid)},
	month = {06},
	abstract = {We propose a modification to a sparsity constraint based on the ratio of $\ell_1$ and $\ell_2$ norms for solving blind seismic deconvolution problems in which the data consist of linear convolutions of different sparse reflectivities with the same source wavelet. We also extend the approach to the Estimation of Primaries by Sparse Inversion (EPSI) model, which includes surface related multiples. Minimizing the ratio of $\ell_1$ and $\ell_2$ norms has been previously shown to promote sparsity in a variety of applications including blind deconvolution. Most existing implementations are heuristic or require smoothing the $\ell_1/\ell_2$ penalty. Lifted versions of $\ell_1/\ell_2$ constraints have also been proposed but are challenging to implement. Inspired by the lifting approach, we propose to split the sparse signals into positive and negative components and apply an $\ell_1/\ell_2$ constraint to the difference, thereby obtaining a constraint that is easy to implement without smoothing the $\ell_1$ or $\ell_2$ norms. We show that a method of multipliers implementation of the resulting model can recover source wavelets that are not necessarily minimum phase and approximately reconstruct the sparse reflectivities. Numerical experiments demonstrate robustness to the initialization as well as to noise in the data.},
	keywords = {blind deconvolution, EAGE, EPSI},
	doi = {10.3997/2214-4609.201413420},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/esser2015EAGElcs/esser2015EAGElcs.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/esser2015EAGElcs/esser2015EAGElcs_poster.pdf},
	author = {Ernie Esser and Tim T.Y. Lin and Rongrong Wang and Felix J. Herrmann}
}
@presentation {dasilva2014SINBADlss,
	title = {Large-scale seismic data interpolation in a parallel computing environment},
	year = {2014},
	publisher = {SINBAD},
	abstract = {This talk will outline the details for a parallel implementation of the SPG-LR algorithm for matrix completion and its applications to large-scale seismic data interpolation. Previous work done in our group on LR-based matrix factorization has been shown to be extremely efficient for seismic data interpolation that fits in memory. We extend the SPGL1 framework that this method is built on to instances where the full seismic data volume cannot entirely fit in the memory of one computational node and must be run in a distributed environment. We will also look at off-the-grid regularization for tensor completion using a non-uniform FFT operator and examine its effects on interpolating an irregularly sampled, 3D seismic frequency slice.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/dasilva2014SINBADlss/dasilva2014SINBADlss.pdf},
	author = {Curt Da Silva and Felix J. Herrmann}
}
@presentation {herrmann2014SINBADldm,
	title = {Latest developments in marine (4D) acquisition},
	year = {2014},
	publisher = {SINBAD},
	abstract = {During this talk, we will show the advantages of randomly dithered marine acquisition with ocean bottom nodes and how randomization techniques from compressive sensing affect the way we think about time-lapse surveys. This is joint work with Felix and Haneet.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/herrmann2014SINBADldm/herrmann2014SINBADldm.pdf},
	author = {Felix J. Herrmann}
}
@presentation {herrmann2014SINBADldm2,
	title = {Latest developments in randomized (4D) acquisition},
	year = {2014},
	publisher = {SINBAD},
	abstract = {We will present the latest developments in randomized (4D) seismic data acquisition.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/herrmann2014SINBADldm2/herrmann2014SINBADldm2.pdf},
	author = {Felix J. Herrmann}
}
@presentation {esser2014SINBADlcs,
	title = {A lifted $\ell_1$/$\ell_2$ constraint for sparse blind deconvolution},
	year = {2014},
	publisher = {SINBAD},
	abstract = {We propose a modification to a sparsity constraint based on the ratio of l1 and l2 norms for solving blind seismic deconvolution problems in which the data consist of linear convolutions of different sparse reflectivities with the same source wavelet. No assumptions are made about the location of the support of either the wavelet or the sparse signals. Minimizing the ratio of l1 and l2 norms has been previously shown to promote sparsity in a variety of applications including blind deconvolution. Most existing implementations are heuristic or require smoothing the l1/l2 penalty. Lifted versions of l1/l2 constraints have also been proposed but are still challenging to implement. Inspired by the lifting approach, we propose to split the sparse signals into positive and negative components and apply an l1/l2 constraint to the difference, thereby obtaining a constraint that is easy to implement without smoothing the l1 or l2 norms. We show that a method of multipliers implementation of the resulting model can recover source wavelets that are not necessarily minimum phase and approximately reconstruct the sparse reflectivities. It appears to be robust to the initialization and to small amounts of noise in the data. We also discuss extensions to the Estimation of Primaries by Sparse Inversion (EPSI) convolution model.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/esser2014SINBADlcs/esser2014SINBADlcs.pdf},
	author = {Ernie Esser and Tim T.Y. Lin and Rongrong Wang and Felix J. Herrmann}
}
@conference {herrmann2014BIRSlrb,
	title = {Low-rank based matrix/tensor completions for the "real" (seismic) world},
	booktitle = {Workshop on Sparse Representations, Numerical Linear Algebra, and Optimization; Banff},
	year = {2014},
	note = {(Workshop at the Banff International Research Station for Mathematical Innovation and Discovery)},
	month = {10},
	keywords = {hierarchical tucker, low-rank, matrix completion, source separation, workshop},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/BIRS/2014/herrmann2014BIRSlrb_pres.pdf},
	author = {Felix J. Herrmann}
}
@presentation {dasilva2014SINBADlrp,
	title = {Low-rank promoting transformations and tensor interpolation - applications to seismic data denoising},
	year = {2014},
	publisher = {SINBAD},
	abstract = {In this presentation, we extend our previous work in Hierarchical Tucker (HT) tensor completion, which uses an extremely efficient representation for representing high-dimensional tensors exhibiting low-rank structure, to handle subsampled tensors with noisy entries. We consider a {\textquoteleft}low-noise{\textquoteright} case, so that the energies of the noise and the signal are nearly indistinguishable, and a {\textquoteleft}high-noise{\textquoteright} case, in which the noise energy is now scaled to the amplitude of the entire data volume. For the low-noise case in particular, standard trace-by-trace energy comparisons cannot distinguish noise from signal. We examine the behaviour of noise in terms of the singular values along different matricizations of the data, i.e. reshaping of the tensor along different dimensions. By interpreting this effect in the context of tensor completion, we demonstrate the inefficacy of denoising by this method in the source-receiver domain. In light of this observation, we transform the decimated, noisy data in to the midpoint-offset domain, which promotes low-rank behaviour in the signal and high-rank behaviour in the noise. This distinction between signal and noise allows low-rank interpolation to effectively denoise the signal, without knowledge of the noise location, with only a marginal increase in computational cost. We demonstrate the effectiveness of this approach on a 4D frequency slice.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/dasilva2014SINBADlrp/dasilva2014SINBADlrp.pdf},
	author = {Curt Da Silva and Felix J. Herrmann}
}
@conference {dasilva2014EAGEhtucknoisy,
	title = {Low-rank promoting transformations and tensor interpolation - applications to seismic data denoising},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2014},
	month = {06},
	abstract = {In this abstract, we extend our previous work in Hierarchical Tucker (HT) tensor completion, which uses an extremely efficient representation for representing high-dimensional tensors exhibit- ing low-rank structure, to handle subsampled tensors with noisy entries. We consider a "low-noise" case, so that the energies of the noise and the signal are nearly indistinguishable, and a {\textquoteright}high-noise{\textquoteright} case, in which the noise energy is now scaled to the amplitude of the entire data volume. We examine the effect of the noise in terms of the singular values along different matricizations of the data, i.e., reshaping of the tensor along different modes. By interpreting this effect in the context of tensor completion, we demonstrate the inefficacy of denoising by this method in the source-receiver do- main. In light of this observation, we transform the decimated, noisy data in to the midpoint-offset domain, which promotes low-rank behaviour in the signal and high-rank behaviour in the noise. This distinction between signal and noise allows low-rank interpolation to effectively denoise the signal with only a marginal increase in computational cost. We demonstrate the effectiveness of this approach on a 4D frequency slice.},
	keywords = {EAGE, hierarchical tucker, low-rank transform, riemannian optimization, seismic denoising, structured tensor, tensor interpolation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/dasilva2014EAGEhtucknoisy/dasilva2014EAGEhtucknoisy.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/dasilva2014EAGEhtucknoisy/dasilva2014EAGEhtucknoisy_pres.pdf},
	author = {Curt Da Silva and Felix J. Herrmann}
}
@presentation {herrmann2013SINBADldc,
	title = {Latest developments on the Chevron GOM and other datasets},
	year = {2013},
	publisher = {SINBAD},
	abstract = {During this talk, we will give a brief overview on our imaging and FWI results on various synthetic and field datasets we have been working on. This is joint work with Andrew Calvert, Brendan R. Smithyman, and the SLIM team.},
	keywords = {Presentation, SINBAD, SINBADSPRING2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/herrmann2013SINBADldc/herrmann2013SINBADldc.pdf},
	author = {Felix J. Herrmann}
}
@presentation {li2013SINBADgom,
	title = {Lessons learned from Chevron Gulf of Mexico data set},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Chevron Gulf of Mexico data set is very challenging for FWI because of elastic phases, limited offset, lack of low frequencies and salt structure. To overcome these issue, we first use ray-based tomography on the hand-picked first breaks to generate initial model for FWI, and then we apply curvelet-denosing techniques to improve the poor signal-to-noise ratio of the observed data at low frequencies. Finally, Curvelet domain sparsity promoting Gauss-Newton FWI helps to suppress model space artifacts caused by elastic phases. This is joint work with Andrew J. Calvert, Ian Hanlon, Mostafa Javanmehri, Rajiv Kumar, Tristan van Leeuwen, Brendan R. Smithyman, Haneet Wason and Felix J. Herrmann},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/li2013SINBADgom/li2013SINBADgom.pdf},
	author = {Xiang Li and Andrew J. Calvert and Ian Hanlon and Mostafa Javanmehri and Rajiv Kumar and Tristan van Leeuwen and Brendan R. Smithyman and Haneet Wason and Felix J. Herrmann}
}
@conference {tu2013SEGldi,
	title = {Limitations of the deconvolutional imaging condition for two-way propagators},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {32},
	year = {2013},
	month = {09},
	pages = {3916-3920},
	abstract = {The deconvolutional imaging condition has gained wide attention in recent years, as it is often used to image surface-related multiples. However, we noticed on close inspection that this condition was derived from one-way propagation principles. Now that two-way wave-equation based simulations have become more affordable, we revisit the deconvolutional imaging condition and reveal its limitations for two-way propagators. First, it can distort the image due to receiver-side propagation effects. Second, when used to image surface-related multiples, it is not capable of removing all interfering phantom reflectors.},
	keywords = {inversion, migration, multiples, SEG},
	doi = {10.1190/segam2013-1440.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2013/tu2013SEGldi/tu2013SEGldi.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2013/tu2013SEGldi/tu2013SEGldi_pres.pdf},
	author = {Ning Tu and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {herrmann2012SINBADlds,
	title = {Latest developments in seismic-data recovery},
	year = {2012},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADSPRING2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Spring/herrmann2012SINBADlds/herrmann2012SINBADlds_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {tu2012EAGElsm,
	title = {Least-squares migration of full wavefield with source encoding},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2012},
	month = {06},
	abstract = {Multiples can provide valuable information that is missing in primaries, and there is a growing interest in using them for seismic imaging. In our earlier work, we proposed to combine primary estimation and migration to image from the total up-going wavefield. The method proves to be effective but computationally expensive. In this abstract, we propose to reduce the computational cost by removing the multi-dimensional convolution required by primary estimation, and reducing the number of PDE solves in migration by introducing simultaneous sources with source renewal. We gain great performance boost without compromising the quality of the image.},
	keywords = {depth migration, EAGE, surface-related multiples},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2012/tu2012EAGElsm/tu2012EAGElsm.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2012/tu2012EAGElsm/tu2012EAGElsm_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=59688},
	author = {Ning Tu and Felix J. Herrmann}
}
@conference {herrmann11SLIMsummer2,
	title = {Lecture 2. Gene Golub SIAM Summer School July 4 - 15, 2011},
	booktitle = {SLIM},
	year = {2011},
	month = {08},
	keywords = {Presentation},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SIAM/2011/herrmann11SLIMsummer2/herrmann11SLIMsummer2.pdf},
	author = {Felix J. Herrmann}
}
@conference {maysami2008SEGlcf,
	title = {Lithological constraints from seismic waveforms: application to opal-A to opal-CT transition},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {27},
	year = {2008},
	month = {11},
	pages = {2011-2015},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this paper, we present a new method for seismic waveform characterization whose aim is threefold, namely (i) extraction of detailed information on the sharpness of transitions in the subsurface from seismic waveforms, (ii) reflector modeling, based on binary-mixture and percolation theory, and (iii) establishment of well-seismic ties, through parameterizations of our waveform and critical reflector model. We test this methodology on the opal-A (Amorphous) to opal-CT (Cristobalite/Tridymite) transition imaged in a migrated section of North Sea field data West of the Shetlands.},
	keywords = {SEG, SLIM},
	doi = {10.1190/1.3059400},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/maysami08SEGlcf/maysami08SEGlcf.pdf},
	author = {Mohammad Maysami and Felix J. Herrmann}
}
