@presentation {yin2024GTwise,
	title = {Generative AI for full-waveform variational inference},
	year = {2024},
	month = {1},
	abstract = {We introduce a probabilistic technique for full-waveform inversion, employing variational inference and conditional normalizing flows to quantify uncertainty in migration-velocity models and its impact on imaging. Our approach integrates generative artificial intelligence with physics-informed common-image gathers, reducing reliance on accurate initial velocity models. Considered case studies demonstrate its efficacy producing realizations of migration-velocity models conditioned by the data. These models are used to quantify amplitude and positioning effects during subsequent imaging.},
	keywords = {amortized Bayes, conditional normalizing flows, deep learning, FWI, generative AI, GT, inversion, machine learning, Normalizing flows, SLIM, Summary Statistics, uncertain quantification, Variational Inference},
	url = {https://slim.gatech.edu/Publications/Public/Lectures/GTseminar/2024/yin2024GTwise},
	author = {Ziyi Yin and Rafael Orozco and Mathias Louboutin and Felix J. Herrmann}
}
@unpublished {yin2024tfp,
	title = {Time-lapse full-waveform permeability inversion: a feasibility study},
	year = {2024},
	month = {3},
	abstract = {Time-lapse seismic monitoring necessitates integrated workflows that combine seismic and reservoir modeling to enhance reservoir property estimation. We present a feasibility study of an end-to-end inversion framework that directly inverts for permeability from prestack time-lapse seismic data. To assess the method{\textquoteright}s robustness, we design experiments focusing on its sensitivity to initial models and potential errors in modeling. Our study leverages the Compass model to simulate CO2 storage in saline aquifers, which is derived from well and seismic data from the North Sea, a candidate site for geological carbon storage.},
	keywords = {4D, CCS, coupled inversion, end-to-end, fluid-flow, GCS, inversion, monitoring, time-lapse},
	doi = {10.48550/arXiv.2403.04083},
	url = {https://slim.gatech.edu/Publications/Public/Submitted/2024/yin2024tfp/paper.html},
	author = {Ziyi Yin and Mathias Louboutin and Olav M{\o}yner and Felix J. Herrmann}
}
@conference {yin2023IMAGEcpi,
	title = {Coupled physics inversion for geological carbon storage monitoring},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2023},
	note = {(IMAGE, Houston)},
	month = {08},
	abstract = {Understanding CO2 plume behavior is key to the success of geological carbon storage projects. While two-phase flow equations provide a good model to make predictions on future CO2 plume behavior, these equations rely on having access to the true permeability model. Unfortunately, accurate information on the permeability is unavailable, which greatly jeopardizes our ability to predict CO2 plume behavior. To overcome this problem, we estimate the permeability from time-lapse seismic data via a coupled inversion methodology that improve as more monitoring data becomes available over time.},
	keywords = {CCS, coupled inversion, end-to-end, fluid-flow, inversion, monitoring, SEG},
	url = {https://slimgroup.github.io/IMAGE2023/yin2023IMAGEend2end/abstract.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2023/yin2023IMAGEcpi},
	author = {Ziyi Yin and Mathias Louboutin and Olav M{\o}yner and Felix J. Herrmann}
}
@article {herrmann2023dte,
	title = {Digital twins in the era of generative AI},
	journal = {The Leading Edge},
	volume = {42},
	year = {2023},
	note = {(The Leading Edge, President{\textquoteright}s Page)},
	month = {11},
	keywords = {CCS, deep learning, GCS, Imaging, inversion, invertible networks, machine learning, monitoring, multiphysics, Normalizing flows, SLIM, software, time-lapse},
	doi = {10.1190/tle42110730.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/TheLeadingEdge/2023/herrmann2023dte/PresidentsPage.pdf},
	author = {Felix J. Herrmann}
}
@presentation {yin2023ML4SEISMICe2e,
	title = {End-to-end permeability inversion from prestack time-lapse seismic data: a case study on Compass model},
	year = {2023},
	month = {11},
	abstract = {Effective geological carbon storage hinges on a deep understanding of CO2 plume behavior. The dynamics of these plumes can be modeled using multiphase flow equations, but their accuracy is tied to a precise permeability model. A significant challenge is that we often lack detailed permeability data, limiting our predictive capabilities. To bridge this gap, we{\textquoteright}ve developed a multiphysics inversion method. This technique inverts for the permeability from observed time-lapse seismic data. Through a case study on the Compass model, we{\textquoteright}ve compared this approach with traditional 4D FWI in forecasting CO2 plume movements. Additionally, our research delves into how different initial permeability models, acquisition setups, and survey frequencies affect the results. Across the board, the inversion method not only enhances our current estimations but also provides valuable insights into future plume dynamics, even without continuous monitoring.},
	keywords = {CCS, coupled inversion, end-to-end, fluid-flow, inversion, ML4SEISMIC, monitoring, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/yin2023ML4SEISMICe2e},
	author = {Ziyi Yin and Mathias Louboutin and Olav M{\o}yner 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 {yin2023EMIutm,
	title = {Uncertainty-aware time-lapse monitoring of geological carbon storage with learned surrogates},
	booktitle = {Engineering Mechanics Institute Conference},
	year = {2023},
	note = {(EMI, Atlanta)},
	month = {06},
	abstract = {Time-lapse seismic monitoring of CO2 sequestration is computationally expensive as it involves modeling of both fluid-flow physics and wave physics. It also requires differentiation through the solvers with respect to properties of interest in the subsurface. In this talk, we present a learned end-to-end inversion framework, which uses a pre-trained Fourier neural operator as a learned surrogate for the fluid-flow simulator in order to greatly reduces the cost associated with fluid-flow modeling and differentiation through the solver. Through synthetic experiments, we demonstrate the efficacy of this framework on inverting the subsurface permeability of the reservoir and on monitoring CO2 plumes. We further quantify the uncertainty of the permeability and CO2 plumes with conditional normalizing flow. With this framework, we can also forecast the growth of CO2 plumes in the future with uncertainty estimation without any acquired seismic data.},
	keywords = {amortized Bayes, conditional normalizing flows, deep learning, EMI, end-to-end, Fourier neural operators, GCS, inversion, machine learning, Normalizing flows, time-lapse, uncertain quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EMI/2023/yin2023EMIutm/yin2023EMIutm.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EMI/2023/yin2023EMIutm/index.html},
	author = {Ziyi Yin and Rafael Orozco and Mathias Louboutin and Ali Siahkoohi and Felix J. Herrmann}
}
@presentation {yin2023ML4SEISMICwise,
	title = {WISE: Full-waveform Inference with Subsurface Extensions},
	year = {2023},
	month = {11},
	abstract = {Quantifying uncertainty in full-waveform inversion is complex given the large sizes of both the model and data. A previous approach employed a variational inference framework, leveraging reverse-time migration to summarize observed data and approximate the posterior distribution through conditional normalizing flows. While reverse-time migration effectively summarizes the data when the background model is close to the true one, its accuracy diminishes with a less accurate background model. In our study, we suggest utilizing subsurface offset gathers as the summary statistics for the variational inference of full-waveform inversion. These gathers retain all the information in seismic data, even when the background model is cycle-skipped or fails to flatten the gathers. Through a case study on Compass model, we confirm our framework{\textquoteright}s effectiveness and show that subsurface offset gathers offer a better summary statistic than just reverse-time migration.},
	keywords = {amortized Bayes, conditional normalizing flows, deep learning, FWI, inversion, machine learning, ML4SEISMIC, Normalizing flows, SLIM, Summary Statistics, uncertain quantification, Variational Inference},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/yin2023ML4SEISMICwise},
	author = {Ziyi Yin and Rafael Orozco and Mathias Louboutin and Felix J. Herrmann}
}
@conference {louboutin2022RHPCafa,
	title = {Abstractions for at-scale seismic inversion},
	booktitle = {Rice Oil and Gas High Performance Computing Conference 2022},
	year = {2022},
	note = {Rice Oil and Gas High Performance Computing Conference 2022},
	month = {03},
	pages = {Thursday Workshop: Devito Training and Hackathon},
	abstract = {We present the SLIM open-source software framework for computational geophysics, and more generally, inverse problems based on the wave-equation (e.g., medical ultrasound). We developed a software environment aimed at scalable research and development by designing multiple layers of abstractions. This environment allows the researchers to easily formulate their problem in an abstract fashion, while still being able to exploit the latest developments in high-performance computing. We illustrate and demonstrate the benefits of our software design on many geophysical applications, including seismic inversion and physics-informed machine learning for geophysics (e.g., loop unrolled imaging, uncertainty quantification), all while facilitating the integration of external software.},
	keywords = {CCS, devito, FWI, HPC, inversion, JUDI, machine learning, RHPC, software, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/RHPC/2022/louboutin2022RHPCafa/RiceHPC22.pdf},
	url2 = {https://youtu.be/scRTbP8w6Wk?t=4542},
	author = {Mathias Louboutin and Ali Siahkoohi and Ziyi Yin and Rafael Orozco and Thomas J. Grady II and Yijun Zhang and Philipp A. Witte and Gabrio Rizzuti and Felix J. Herrmann}
}
@conference {louboutin2022SEGais,
	title = {Accelerating innovation with software abstractions for scalable computational geophysics},
	booktitle = {International Meeting for Applied Geoscience and Energy Expanded Abstracts},
	year = {2022},
	note = {(IMAGE, Houston)},
	month = {05},
	abstract = {We present the SLIM open-source software framework for computational geophysics, and more generally, inverse problems based on the wave-equation (e.g., medical ultrasound). We developed a software environment aimed at scalable research and development by designing multiple layers of abstractions. This environment allows the researchers to easily formulate their problem in an abstract fashion, while still being able to exploit the latest developments in high-performance computing. We illustrate and demonstrate the benefits of our software design on many geophysical applications, including seismic inversion and physics-informed machine learning for geophysics(e.g., loop unrolled imaging, uncertainty quantification), all while facilitating the integration of external software.},
	keywords = {CCS, devito, FWI, HPC, inversion, JUDI, machine learning, SEG, software, Uncertainty quantification},
	doi = {10.1190/image2022-3750561.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/louboutin2022SEGais/louboutin_seg22.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/louboutin2022SEGais/index.html},
	author = {Mathias Louboutin and Philipp A. Witte and Ali Siahkoohi and Gabrio Rizzuti and Ziyi Yin and Rafael Orozco and Felix J. Herrmann}
}
@conference {louboutin2022EAGEewi,
	title = {Enabling wave-based inversion on GPUs with randomized trace estimation},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2022},
	note = {(EAGE, Madrid)},
	month = {06},
	pages = {Seismic Wave Modelling and Least Square Migration 2 session},
	abstract = {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 approach that can be executed exclusively on accelerators. Results obtained on field-realistic synthetic datasets, which include salt and anisotropy, show that our method produces high-fidelity images. These findings open the enticing perspective of 3D wave-based inversion technology with a memory footprint that matches the hardware and that runs exclusively on clusters of GPUs without the undesirable need to offload certain tasks to CPUs.},
	keywords = {EAGE, Image Volumes, inversion, RTM, SEAM, stochastic, TTI},
	doi = {10.3997/2214-4609.202210531},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2022/louboutin2022eageewi/louboutinp.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2022/louboutin2022eageewi/Jun-9-3-15-mloubout.pdf},
	software = {https://github.com/slimgroup/TimeProbeSeismic.jl},
	author = {Mathias Louboutin 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 {yin2022ML4SEISMICutc,
	title = {Uncertainty-aware time-lapse CO$_2$ monitoring with learned end-to-end inversion},
	year = {2022},
	month = {11},
	abstract = {Seismic monitoring of CO2 sequestration is computationally expensive as it involves modeling of both fluid-flow physics modeling and wave physics and differentiation through the solvers with respect to the subsurface properties of interest. In this talk, we demonstrate the effectiveness of learned coupled inversion framework using a pre-trained Fourier neural operator as a learned surrogate for the fluid-flow simulator, which greatly reduces the cost associated with fluid-flow modeling and differentiation through the solver. We study the effectiveness and correctness of inversion based on Fourier neural operator surrogate and a normalizing flow prior. We also demonstrate the efficacy of this framework on monitoring the growth of CO2 plumes during sequestration, and on uncertainty quantification of the permeability and CO2 plumes with conditional normalizing flow. With this framework, we can further forecast the CO2 plume in the future without any acquired seismic data with uncertainty estimation.},
	keywords = {amortized Bayes, conditional normalizing flows, deep learning, Fourier neural operators, GCS, inversion, machine learning, ML4SEISMIC, Normalizing flows, SLIM, time-lapse, uncertain quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/yin2022ML4SEISMICutc/index.html},
	author = {Ziyi Yin and Rafael Orozco and Ali Siahkoohi and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {louboutin2021ML4SEISMICrla,
	title = {Randomized linear algebra for inversion},
	year = {2021},
	month = {11},
	abstract = {Inverse problems in exploration geophysics or machine learning heavily relies on linear algebra and large matrices manipulations. To tackle the growing cost of storing these matrices, randomized algorithms have been developed to obtain information from these matrices via randomized sketching. Inspired by previous work on extended image volumes, we will first show in this talk how the seismic imaging condition can be expressed in a randomized linear algebra framework leading to drastic memory savings. In a second part, we will extend this idea to convolutional neural networks to reduce the memory cost of training by orders of magnitude. We will demonstrate the practicality of these methods on representative examples.},
	keywords = {FWI, HPC, inversion, ML4SEISMIC, randomized linear algebra, SLIM, software},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/louboutin2021ML4SEISMICrla/Tue-10-20-Louboutin.pdf},
	author = {Mathias Louboutin and Felix J. Herrmann}
}
@conference {ren2021AGUsvi,
	title = {Seismic Velocity Inversion and Uncertainty Quantification Using Conditional Normalizing Flows},
	booktitle = {AGU Annual Meeting},
	year = {2021},
	note = {(AGU 2021, New Orleans)},
	month = {12},
	pages = {U12A-03},
	abstract = {In this work, we use a conditional normalizing flow (CNF) to address the seismic velocity inversion problem. Considering the large dimension difference between seismic data and velocity model, we reduce the data dimension by calculating its reverse time. After that, we train the CNF on pairs of migrated data and velocity. During inference, given a new seismic data, feeding the corresponding migrated image into the trained CNF will lead to posterior samples of the velocity inversion distribution. In addition, uncertainty quantification of the inverted results can be achieved by statistical metrics like mean and standard deviation. In our numerical example, the implementation is based on open-sourced software InvertibleNetworks.jl (Witte et al., 2021), JUDI.jl (Witte et al., 2019) and Devito (Louboutin et al., 2019). The training dataset are built based on the SEG/EAGE Overthrust model. For an unseen seismic data, the posterior samples of inversion results given by the trained CNF can be considered as good estimates of the true velocity. Especially, judging from the metrics like MAE, MSE, PSNR, SSIM, et al., the posterior mean is usually closer to the true velocity and the standard deviation indicates that the velocity value is more reliable within the subsurface layers than that on layer edges. Moreover, the inverted results, either the posterior samples or posterior mean, can be used as an initial model in the subsequent FWI for a more accurate result.},
	keywords = {AGU, inversion, machine learning, Normalizing flows, Uncertainty quantification},
	url = {https://agu.confex.com/agu/fm21/meetingapp.cgi/Paper/815883},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/AGU/2021/ren2021AGUsvi/ren2021AGUsvi_pres.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/AGU/2021/ren2021AGUsvi/ren2021AGUsvi.mp4},
	software = {https://github.com/slimgroup/INN_Velocity-Migration},
	author = {Yuxiao Ren and Philipp A. Witte and Ali Siahkoohi and Mathias Louboutin and Ziyi Yin and Felix J. Herrmann}
}
@article {sanavi2021tad,
	title = {True amplitude depth migration using curvelets},
	journal = {Geophysics},
	volume = {86},
	number = {4},
	year = {2021},
	note = {(Geophysics)},
	month = {07},
	pages = {S299-S310},
	abstract = {We have developed a true amplitude solution to the seismic imaging problem. We derive a diagonal scaling approach for the normal operator approximation in the curvelet domain. This is based on the theorem that states that curvelets remain approximately invariant under the action of the normal operator. We use curvelets as essential tools for approximation and inversion. We also exploit the theorem that states that the curvelet-domain approximation should be smooth in phase space by enforcing the smoothness of curvelet coefficients in the angle and space domains. We analyze our method using a reverse time migration-demigration code, simulating the acoustic wave equation on different synthetic models. Our method produces a good resolution with reflecting dips, reproduces the true amplitude reflectors, and compensates for incomplete illumination in seismic images.},
	keywords = {amplitude, depth migration, Imaging, inversion, LSRTM},
	doi = {10.1190/geo2019-0307.1},
	author = {Hamideh Sanavi and Peyman P. Moghaddam and Felix J. Herrmann}
}
@conference {louboutin2021SEGulm,
	title = {Ultra-low memory seismic inversion with randomized trace estimation},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2021},
	note = {(IMAGE, Denver)},
	month = {09},
	pages = {787-791},
	abstract = {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 computationally efficient inversion methodology that uses techniques from randomized linear algebra. By means of a carefully selected realistic synthetic example, we demonstrate that we are capable of achieving competitive inversion results at a fraction of the memory cost of conventional full-waveform inversion with limited computational overhead. By exchanging memory for negligible computational overhead, we open with the presented technology the door towards the use of low-memory accelerators such as GPUs.},
	keywords = {FWI, HPC, inversion, randomized linear algebra, SEG},
	doi = {10.1190/segam2021-3584072.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2021/louboutin2021SEGulm/louboutinp.html},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2021/louboutin2021SEGulm/louboutin2021SEGulm.mp4},
	software = {https://github.com/slimgroup/TimeProbeSeismic.jl},
	author = {Mathias Louboutin and Felix J. Herrmann}
}
@mastersthesis {louboutin2020THmfi,
	title = {Modeling for inversion in exploration geophysics},
	year = {2020},
	note = {(PhD)},
	month = {03},
	school = {Georgia Institute of Technology},
	type = {phd},
	address = {Atlanta},
	abstract = {Seismic inversion, and more generally geophysical exploration, aims at better understanding the earth{\textquoteright}s subsurface, which is one of today{\textquoteright}s most important challenges. Firstly, it contains natural resources that are critical to our technologies such as water, minerals and oil and gas. Secondly, monitoring the subsurface in the context of CO2 sequestration, earthquake detection and global seismology are of major interests with regard to safety and the environment hazards. However, the technologies to monitor the subsurface or find resources are scientifically extremely challenging. Seismic inversion can be formulated as a mathematical optimization problem that minimizes the difference between field recorded data and numerically modeled synthetic data. The process of solving this optimization problem then requires to numerically model, thousands of times, wave-propagation in large three-dimensional representations of part of the earth subsurface. The mathematical and computational complexity of this problem, therefore, calls for software design that abstracts these requirements and facilitates algorithm and software development. My thesis addresses some of the challenges that arise from these problems; mainly the computational cost and access to the right software for research and development. In the first part, I will discuss a performance metric that improves the current runtime-only benchmarks in exploration geophysics. This metric, the roofline model, first provides insight at the hardware level of the performance of a given implementation relative to the maximum achievable performance. Second, this study demonstrates that the choice of numerical discretization has a major impact on the achievable performance depending on the hardware at hand and shows that a flexible framework with respect to the discretization parameters is necessary. In the second part, I will introduce and describe Devito, a symbolic finite-difference DSL that provides a high-level interface to the definition of partial differential equations (PDE) such as the wave equation. Devito, from the symbolic definition of PDEs, then generates and compiles highly optimized C code on-the-fly to compute the solution of the PDE. The combination of the high-level abstractions and the just-in-time compiler enable research for geophysical exploration and PDE-constrainted optimization based on the paradigm of separation of concerns. This allows researchers to concentrate on their respective field of study while having access to computationally performant solvers with a flexible and easy to use interface to successfully implement complex representations of the physics. The second part of my thesis will be split into two sub-parts; first describing the symbolic application programming interface (API), before describing and benchmarking the just-in-time compiler. I will end my thesis with concluding remarks, the latest developments and a brief description of projects that were enabled by Devito.},
	keywords = {finite-differences, FWI, HPC, Imaging, inversion, Modeling, performance, PhD, RTM},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2020/louboutin2020THmfi/louboutin2020THmfi.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Thesis/2020/louboutin2020THmfi/louboutin2020THmfi_pres.pdf},
	author = {Mathias Louboutin}
}
@presentation {witte2019HOTCSEdsagip,
	title = {Domain-specific abstractions for large-scale geophysical inverse problems},
	year = {2019},
	note = {(HotCSE)},
	abstract = {During these times of sustained low oil prices, it is essential to look for new innovative ways to collect (time-lapse) seismic data at reduced costs and preferably also at reduced environmental impact. By now, there is an increasing body of corroborating evidence {\textemdash} whether these are simulated case studies or actual acquisitions on land and marine {\textemdash} that seismic acquisition based on the principles of compressive sensing delivers on this premise by removing the need to acquire replicated dense surveys. Up to ten-fold increases in acquisition efficiency have been reported by industry while there are indications that this breakthrough is only the beginning of a paradigm shift where full-azimuth time-lapse processing will become a reality. To familiarize the audience with this new technology, I will first describe the basics of compressive sensing, how it relates to missing-trace interpolation and simultaneous source acquisition, followed by how this technology is driving innovations in full-azimuth (time-lapse) acquisition, yielding high-fidelity data with a high degree of repeatability and at a fraction of the costs.},
	keywords = {full-waveform-inversion, Imaging, inversion, Julia, large-scale, software},
	url = {https://slim.gatech.edu/Publications/Public/Lectures/HotCSE/2019/witte2019HOTCSEdsagip/witte2019HOTCSEdsagip.pdf},
	author = {Philipp A. Witte and Mathias Louboutin and Felix J. Herrmann}
}
@article {witte2019ecl,
	title = {Geophysics Bright Spots: Efficient coding of large-scale seismic inversion algorithms},
	journal = {The Leading Edge},
	volume = {38},
	number = {6},
	year = {2019},
	note = {(The Leading Edge)},
	pages = {482-484},
	abstract = {In {\textquotedblleft}A large-scale framework for symbolic implementations of seismic inversion algorithms in Julia,{\textquotedblright} Witte et al. describe new developments in how to code complex geophysical algorithms in a concise way. Subsurface seismic imaging and parameter estimation are among the most computationally challenging problems in the scientific community. Codes for solving seismic inverse problems, such as FWI or least-squares reverse time migration (LS-RTM), need to be highly optimized, but at the same time, facilitate the implementation of complex optimization algorithms. Traditionally, production-level codes in the oil and gas industry were exclusively written in low-level languages, such as C or Fortran, with extensive amounts of manual performance optimizations, thus making code maintenance, debugging, and adoption of new algorithms prohibitively challenging. Witte et al. present a paradigm of software engineering for seismic inverse problems based on symbolic user interfaces and code generation with automated performance optimization. Inspired by recent deep learning frameworks, the Julia Devito inversion framework (JUDI; an open-source software package) combines high-level abstractions for expressing seismic inversion algorithms with a domain-specific language compiler called Devito for solving the underlying wave equations. Devito{\textquoteright}s generated code is compiled just in time and outperforms codes with manual performance optimizations. JUDI utilizes Julia{\textquoteright}s high-level parallelization, making the software easily adaptable to a variety of computing environments such as densely connected HPC clusters or the cloud. The numerical examples (Figure 3) demonstrate the ability to implement a variety of complex algorithms for FWI and LS-RTM in a few lines of Julia code and run it on large-scale 3D models. The paper concludes that abstractions and performance are not mutually exclusive, and use of symbolic user interfaces can facilitate the implementation of new and innovative seismic inversion algorithms.},
	keywords = {bright spots, inversion, Julia, large-scale},
	doi = {10.1190/tle38060482.1},
	url = {https://library.seg.org/doi/10.1190/tle38060482.1},
	author = {Philipp A. Witte and Mathias Louboutin and Navjot Kukreja and Fabio Luporini and Michael Lange and Gerard J. Gorman 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}
}
@article {witte2018fwip3,
	title = {Full-Waveform Inversion - Part 3: optimization},
	journal = {The Leading Edge},
	volume = {37},
	number = {2},
	year = {2018},
	note = {(The Leading Edge)},
	month = {1},
	pages = {142-145},
	abstract = {This tutorial is the third part of a full-waveform inversion (FWI) tutorial series with a step-by-step walkthrough of setting up forward and adjoint wave equations and building a basic FWI inversion framework. For discretizing and solving wave equations, we use Devito, a Python-based domain-specific language for automated generation of finite-difference code (Lange et al., 2016). The first two parts of this tutorial (Louboutin et al., 2017, 2018) demonstrated how to solve the acoustic wave equation for modeling seismic shot records and how to compute the gradient of the FWI objective function using the adjoint-state method. With these two key ingredients, we will now build an inversion framework that can be used to minimize the FWI least-squares objective function.},
	keywords = {devito, finite-differences, FWI, inversion, Modeling, tutorial},
	doi = {10.1190/tle37020142.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/TheLeadingEdge/2018/witte2018fwip3/witte2018fwip3.html},
	author = {Philipp A. Witte and Mathias Louboutin and Keegan Lensink and Michael Lange and Navjot Kukreja and Fabio Luporini and Gerard Gorman and Felix J. Herrmann}
}
@conference {louboutin2017EAGEdns,
	title = {Data normalization strategies for full-waveform inversion},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2017},
	note = {(EAGE, Paris)},
	month = {06},
	abstract = {Amplitude mismatch is an inherent problem in seismic inversion. Most of the source estimation techniques are associated with amplitude uncertainty due to incomplete representation of the physics or estimation method parameters. Rewriting the inversion problem in an amplitude free formulation allows to mitigate the amplitude ambiguity and help the inversion process to converge. We present in this work two different strategies to lessen amplitude effects in seismic inversion, derive the corresponding update directions and show how we handle scaling error correctly in both the objective function and the gradient.},
	keywords = {EAGE, FWI, inversion, nonlinear, normalization},
	doi = {10.3997/2214-4609.201700720},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2017/louboutin2017EAGEdns/louboutin2017EAGEdns.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2017/louboutin2017EAGEdns/louboutin2017EAGEdns_poster.pdf},
	author = {Mathias Louboutin and Llu{\'\i}s Guasch and Felix J. Herrmann}
}
@conference {louboutin2017EAGEess,
	title = {Extending the search space of time-domain adjoint-state FWI with randomized implicit time shifts},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2017},
	note = {(EAGE, Paris)},
	month = {06},
	abstract = {Adjoint-state full-waveform inversion aims to obtain subsurface properties such as velocity, density or anisotropy parameters, from surface recorded data. As with any (non-stochastic) gradient based optimization procedure, the solution of this inversion procedure is to a large extend determined by the quality of the starting model. If this starting model is too far from the true model, these derivative-based optimizations will likely end up in local minima and erroneous inversion results. In certain cases, extension of the search space, e.g. by making the wavefields or focused matched sources additional unknowns, has removed some of these non-uniqueness issues but these rely on time-harmonic formulations. Here, we follow a different approach by combining an implicit extension of the velocity model, time compression techniques and recent results on stochastic sampling in non-smooth/non-convex optimization},
	keywords = {cycle skipping, EAGE, FWI, inversion, nonconvex, time domain},
	doi = {10.3997/2214-4609.201700831},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2017/louboutin2017EAGEess/louboutin2017EAGEess.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2017/louboutin2017EAGEess/louboutin2017EAGEess_pres.pdf},
	author = {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 {lange2016dtg,
	title = {Devito: Towards a generic finite difference DSL using symbolic python},
	booktitle = {6th Workshop on Python for High-Performance and Scientific Computing},
	year = {2016},
	note = {(PyHPC, Utah)},
	month = {11},
	pages = {67-75},
	abstract = {Domain specific languages (DSL) have been used in a variety of fields to express complex scientific problems in a concise manner and provide automated performance optimization for a range of computational architectures. As such DSLs provide a powerful mechanism to speed up scientific Python computation that goes beyond traditional vectorization and pre-compilation approaches, while allowing domain scientists to build applications within the comforts of the Python software ecosystem. In this paper we present Devito, a new finite difference DSL that provides optimized stencil computation from high-level problem specifications based on symbolic Python expressions. We demonstrate Devito{\textquoteright}s symbolic API and performance advantages over traditional Python acceleration methods before highlighting its use in the scientific context of seismic inversion problems.},
	keywords = {acoustic, finite differences, HPC, inversion, Modelling, python, software optimization},
	doi = {10.1109/PyHPC.2016.9},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/PyHPC/2016/lange2016dtg/lange2016dtg.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/PyHPC/2016/lange2016dtg/lange2016dtg_pres.pdf},
	author = {Michael Lange and Navjot Kukreja and Mathias Louboutin and Fabio Luporini and Felippe Vieira Zacarias and Vincenzo Pandolfo and Paulius Velesko and Paulius Kazakas and Gerard Gorman}
}
@mastersthesis {tu2015THfis,
	title = {Fast imaging with surface-related multiples},
	year = {2015},
	note = {(PhD)},
	month = {08},
	school = {The University of British Columbia},
	type = {phd},
	address = {Vancouver},
	abstract = {Surface-related multiples, which are waves that bounce more than once between the water surface and the subsurface reflectors, constitute a significant part of the data acquired in marine seismic surveys. If left untreated, they can lead to misplaced phantom reflectors in the image, and result in erroneous interpretations of the subsurface structure. As a result, these multiples are removed before the imaging procedure in conventional seismic data processing. However, because they interact more with the subsurface medium, they may carry extra information that is not present in the primaries. Therefore instead of removing these multiples, a more desirable alternative is to make active use of them. We derive from the well-established "Surface-Related Multiple Elimination" relation, and arrive at a linearized expression of the wave-equation based modelling that incorporates the surface- related multiples. We then present a computationally efficient approach to iteratively invert this expression to obtain an image of the subsurface from data that contain multiples. We achieve the computational efficiency inside each iteration by (i) using the wave-equation solver to implicitly carry out the expensive multiple prediction; and (ii) reducing the number of wave-equation solves during data simulation by subsampling the monochromatic source experiments. We show that, compared with directly applying the cross-correlation/deconvolutional imaging conditions, the presented approach can suppress the coherent imaging artifacts from multiples more effectively. We also show that, by curvelet-domain sparsity promoting and occasionally drawing new data samples during the inversion, the proposed inversion method gains improved robustness to velocity errors in the background model, as well as modelling errors incurred during linearization of the wave-equation. To combine the information encoded in both the primaries and the multiples, we then propose a highly accurate source estimation method to jointly invert the total upgoing wavefield. We show with field data examples that we can reap benefits from both the relative noise-free primaries and the extra illumination coverage of the multiples. We also demonstrate that the inclusion of multiples help mitigate the amplitude ambiguity during source estimation. We conclude the thesis with an outlook for future research directions, as well as potential extensions of the proposed work.},
	keywords = {inversion, least-squares, multiples, seismic imaging},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2015/tu2015THfis/tu2015THfis.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Thesis/2015/tu2015THfis/tu2015THfis_pres.pdf},
	author = {Ning Tu}
}
@article {tu2014fis,
	title = {Fast imaging with surface-related multiples by sparse inversion},
	journal = {Geophysical Journal International},
	volume = {201},
	number = {1},
	year = {2015},
	note = {(Geophysical Journal International)},
	month = {04},
	pages = {304-317},
	abstract = {In marine exploration seismology, surface-related multiples are usually treated as noise mainly because subsequent processing steps, such as migration velocity analysis and imaging, require multiple-free data. Failure to remove these wavefield components from the data may lead to erroneous estimates for migration velocity or result in strong coherent artefacts that interfere with the imaged reflectors. However, multiples can carry complementary information compared to primaries, as they interact with the free surface and are therefore exposed more to the subsurface. Recent work has shown that when processed correctly multiples can improve seismic illumination. Given a sufficiently accurate background velocity model and an estimate for the source signature, we propose a new and computationally efficient linearized inversion procedure based on two-way wave equations, which produces accurate images of the subsurface from the total upgoing wavefield including surface-related multiples. Modelling of the surface-related multiples in the proposed method derives from the well-known surface-related multiple elimination method. We incur a minimal overhead from incorporating the multiples by having the wave-equation solver carry out the multiple predictions via the inclusion of an areal source instead of expensive dense matrix-matrix multiplications. By using subsampling techniques, we obtain high-quality true-amplitude least-squares migrated images at computational costs of roughly a single reverse-time migration (RTM) with all the data. These images are virtually free of coherent artefacts from multiples. Proper inversion of the multiples would be computationally infeasible without using these techniques that significantly bring down the cost. By promoting sparsity in the curvelet domain and using rerandomization, out method gains improved robustness to errors in the background velocity model, and errors incurred in the linearization of the wave equation with respect to the model. We demonstrate the superior performance of the proposed method compared to the conventional RTM using realistic synthetic examples.},
	keywords = {approximate message passing, Compressive Sensing, curvelet, inversion, Kaczmarz, multiples},
	url = {https://slim.gatech.edu/Publications/Public/Journals/GeophysicalJournalInternational/2014/tu2014fis/tu2014fis.pdf},
	url2 = {http://gji.oxfordjournals.org/cgi/content/full/ggv020?ijkey=pKzrSXbtxjlOjzd\&keytype=ref},
	author = {Ning Tu and Felix J. Herrmann}
}
@article {tu2015TLEfls,
	title = {Fast least-squares imaging with surface-related multiples: application to a North-Sea data set},
	journal = {The Leading Edge},
	volume = {34},
	number = {7},
	year = {2015},
	note = {(The Leading Edge)},
	month = {07},
	pages = {788{\textendash}794},
	abstract = {In marine seismic acquisition, surface-related multiples constitute a significant portion of the acquired data. Typically, multiples are removed during early-stage data-processing as they can lead to phantom reflectors during migration that may result in erroneous geological interpretations. However, if properly dealt with, multiples can provide valuable extra information and complement primaries in illuminating the subsurface. In this article, we demonstrate the limitation of the reverse-time migration in imaging these multiples, and present an alternative inversion procedure that is computationally efficient, that jointly maps both primaries and multiples to the true reflectors, and where the source function is estimated on the fly. As a result, we obtain high-quality, mostly artifact-free, broad-band images where the imprint of the source-function are partly removed at a computationally affordable expense compared to the combined costs of the wave-equation based surface-related multiple elimination and the reverse-time migration. We achieve all this by including the total downgoing wavefields as areal sources in the least-squares migration in combination with curvelet-domain sparsity promotion. We apply the proposed method to a shallow-water marine data set from the North sea, which contains abundant short-period surface-related multiples, and show its efficacy in eliminating coherent imaging artifacts associated with these multiples. We also demonstrate the benefits of joint imaging of primaries and multiples compared to imaging these signal components separately.},
	keywords = {field data, inversion, least squares, migration, multiples},
	doi = {10.1190/tle34070788.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/TheLeadingEdge/2015/tu2015TLEfls/tu2015TLEfls.html},
	author = {Ning Tu and Felix J. Herrmann}
}
@mastersthesis {lin2015THpes,
	title = {Primary estimation with sparsity-promoting bi-convex optimization},
	year = {2015},
	note = {(PhD)},
	month = {10},
	school = {The University of British Columbia},
	type = {phd},
	address = {Vancouver},
	abstract = {This thesis establishes a novel inversion methodology for the surface-related primaries from a given recorded seismic wavefield, called the Robust Estimation of Primaries by Sparse Inversion (Robust EPSI, or REPSI). Surface-related multiples are a major source of coherent noise in seismic data, and inferring fine geological structures from active-source seismic recordings typically first necessitates its removal or mitigation. For this task, current practice calls for data-driven approaches which produce only approximate multiple models that must be non-linearly subtracted from the data, often distorting weak primary events in the process. A recently proposed method called Estimation of Primaries by Sparse Inversion (EPSI) avoids this adaptive subtraction by directly inverting for a discrete representation of the underlying multiple-free subsurface impulse response as a set of band-limited spikes. However, in its original form, the EPSI algorithm exhibits a few notable shortcomings that impede adoption. Although it was shown that the correct impulse response can be obtained through a sparsest solution criteria, the current EPSI algorithm is not designed to take advantage of this finding, but instead approximates a sparse solution in an ad-hoc manner that requires practitioners to decide on a multitude of inversion parameters. The Robust EPSI method introduced in this thesis reformulates the original EPSI problem as a formal bi-convex optimization problem that makes obtaining the sparsest solution an explicit goal, while also reliably admit satisfactory solutions using contemporary self-tuning gradient methods commonly seen in large-scale machine learning communities. I show that the Robust EPSI algorithm is able to operate successfully on a variety of datasets with minimal user input, while also producing a more accurate model of the subsurface impulse response when compared to the original algorithm. Furthermore, this thesis makes several contributions that improves the capability and practicality of EPSI: a novel scattering-based multiple prediction model that allows Robust EPSI to deal with wider near-offset receiver gaps than previously demonstrated for EPSI, as well as a multigrid-inspired continuation strategy that significantly reduces the computation time needed to solve EPSI-type problems. These additions are enabled by and built upon the formalism of the Robust EPSI as developed in this thesis.},
	keywords = {biconvex, EPSI, inversion, multiples, Optimization, PhD, sparsity},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2015/lin2015THpes/lin2015THpes.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Thesis/2015/lin2015THpes/lin2015THpes_pres.pdf},
	author = {Tim T.Y. Lin}
}
@conference {kumar2015IIPFWIrmb,
	title = {Rank minimization based seismic data processing and inversion},
	booktitle = {Inaugural Full-Waveform Inversion Workshop},
	year = {2015},
	note = {(Natal, Brazil)},
	month = {08-09},
	abstract = {We present the low-rank extensions of full-waveform inversion to mitigate the local-minima issues. The purpose formulation is computationally tractable and does not involve any extra computational cost compared to the adjoint-state method.},
	keywords = {FWI, inversion, Processing, rank minimization},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/IIPFWI/kumar2015IIPFWIrmb/kumar2015IIPFWIrmb_pres.pdf},
	author = {Rajiv Kumar and Curt Da Silva and Oscar Lopez and Aleksandr Y. Aravkin and Hassan Mansour and Haneet Wason and Ernie Esser and Felix J. Herrmann}
}
@article {kumar2015sss,
	title = {Source separation for simultaneous towed-streamer marine acquisition {\textendash}- a compressed sensing approach},
	journal = {Geophysics},
	volume = {80},
	number = {6},
	year = {2015},
	note = {(Geophysics)},
	month = {11},
	pages = {WD73-WD88},
	abstract = {Simultaneous marine acquisition is an economic way to sample seismic data and speed up acquisition, wherein single or multiple source vessels fire sources at near-simultaneous or slightly random times, resulting in overlapping shot records. The current paradigm for simultaneous towed-streamer marine acquisition incorporates {\textquotedblleft}low variability{\textquotedblright} in source firing times, i.e., 0 <= 1 or 2 s because the sources and receivers are moving. This results in a low degree of randomness in simultaneous data, which is challenging to separate (into its constituent sources) using compressed-sensing-based separation techniques because randomization is key to successful recovery via compressed sensing. We have addressed the challenge of source separation for simultaneous towed-streamer acquisitions via two compressed-sensing-based approaches, i.e., sparsity promotion and rank minimization. We have evaluated the performance of the sparsity-promotion- and rank-minimization-based techniques by simulating two simultaneous towed-streamer acquisition scenarios, i.e., over/under and simultaneous long offset. A field data example from the Gulf of Suez for the over/under acquisition scenario was also developed. We observed that the proposed approaches gave good and comparable recovery qualities of the separated sources, but the rank-minimization technique outperformed the sparsity-promoting technique in terms of the computational time and memory. We also compared these two techniques with the normal-moveout-based median-filtering-type approach, which had comparable results.},
	keywords = {2D, Acquisition, inversion, marine, Optimization, Rank, source separation, sparsity},
	doi = {10.1190/geo2015-0108.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2015/kumar2015sss/kumar2015sss_revised.pdf},
	url2 = {http://library.seg.org/doi/abs/10.1190/geo2015-0108.1},
	author = {Rajiv Kumar and Haneet Wason and Felix J. Herrmann}
}
@conference {tu2015PIMSsls,
	title = {Sparse least-squares seismic imaging with source estimation utilizing multiples},
	booktitle = {PIMS Workshop on Advances in Seismic Imaging and Inversion},
	year = {2015},
	note = {(PIMS Workshop on Advances in Seismic Imaging and Inversion, University of Alberta, Edmonton)},
	month = {05},
	abstract = {We present an least-squares seismic imaging method that (1) is efficient by subsampling the monochromatic source experiments; (2) does not require the prior knowledge of the source wavelet, by inverting for the seismic image and the source wavelet simultaneously using variable projection; and (3) makes active use of surface-related multiples to increase the illumination and to resolve the scaling ambiguity in source estimation, by tightly integrating the well-established surface-related-multiple-prediction into the wave-equation based modelling. As a result, we are able to obtain ambiguity-resolved, high-fidelity least-squares inverted seismic images with computational costs that are comparable to conventional reverse-time migrations with all the data. We demonstrate the efficacy of the proposed method using synthetic examples.},
	keywords = {inversion, least-squares migration, multiples, PIMS, seismic imaging, source estimation, workshop},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/PIMS/2015/tu2015PIMSsls/tu2015PIMSsls_pres.pdf},
	author = {Ning Tu and Felix J. Herrmann}
}
@conference {fang2015IIPFWIsoa,
	title = {Stochastic optimization and its application to seismic inversion},
	booktitle = {Inaugural Full-Waveform Inversion Workshop},
	year = {2015},
	note = {(Natal, Brazil)},
	month = {08-09},
	keywords = {FWI, inversion, Optimization, stochastic},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/IIPFWI/fang2015IIPFWIsoa/fang2015IIPFWIsoa_pres.pdf},
	author = {Zhilong Fang and Tim Burgess and Felix J. Herrmann}
}
@conference {louboutin2015SEGtcs,
	title = {Time compressively sampled full-waveform inversion with stochastic optimization},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2015},
	note = {(SEG, New Orleans)},
	month = {10},
	pages = {5153-5157},
	abstract = {Time-domain Full-Waveform Inversion (FWI) aims to image the subsurface of the earth accurately from field recorded data and can be solved via the reduced adjoint-state method. However, this method requires access to the forward and adjoint wavefields that are meet when computing gradient updates. The challenge here is that the adjoint wavefield is computed in reverse order during time stepping and therefore requires storage or other type of mitigation because storing the full time history of the forward wavefield is too expensive in realistic 3D settings. To overcome this challenge, we propose an approximate adjoint-state method where the wavefields are subsampled randomly, which drastically the amount of storage needed. By using techniques from stochastic optimization, we control the errors induced by the subsampling. Examples of the proposed technique on a synthetic but realistic 2D model show that the subsampling-related artifacts can be reduced significantly by changing the sampling for each source after each model update. Combination of this gradient approximation with a quasi-Newton method shows virtually artifact free inversion results requiring only 5\% of storage compared to saving the history at Nyquist. In addition, we avoid having to recompute the wavefields as is required by checkpointing.},
	keywords = {acoustic, Full-waveform inversion, inversion, SEG, Stochastic optimization, Subsampling, Time-domain},
	doi = {10.1190/segam2015-5924937.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2015/louboutin2015SEGtcs/louboutin2015SEGtcs.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2015/louboutin2015SEGtcs/louboutin2015SEGtcs_poster.pdf},
	author = {Mathias Louboutin and Felix J. Herrmann}
}
@conference {herrmann2015IIPFWIwri,
	title = {Wavefield-Reconstruction Inversion {\textemdash} WRI},
	booktitle = {Inaugural Full-Waveform Inversion Workshop},
	year = {2015},
	note = {(Natal, Brazil)},
	month = {08-09},
	abstract = {We discuss a recently proposed novel method for waveform inversion: Wavefield Reconstruction Inversion (WRI). As opposed to conventional FWI {\textemdash} which attempts to minimize the error between observed and predicted data obtained by solving a wave equation {\textendash} WRI reconstructs a wave-field from the data and extracts a model-update from this wavefield by minimizing the wave-equation residual. The method does not require explicit computation of an adjoint wavefield as all the necessary information is contained in the reconstructed wavefield. We show how the corresponding model updates can be interpreted physically analogously to the conventional imaging-condition-based approach.},
	keywords = {FWI, inversion, WRI},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/IIPFWI/herrmann2015IIPFWIwri/herrmann2015IIPFWIwri_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2014SEGWSfli,
	title = {Fast linearized inversion with surface-related multiples with source estimation},
	booktitle = {SEG Workshop on Using Multiples as Signal for Imaging; Denver},
	year = {2014},
	note = {(SEG Workshop, Denver)},
	month = {10},
	abstract = {In the well-known SRME relation, multiples are expressed as the multi-dimensional convolution between the subsurface Green{\textquoteright}s function and the downgoing receiver wavefield. Therefore these multiples can be considered as the response of the subsurface to an "areal" source term given by the same downgoing receiver wavefield. This relation can be used to treat these multiples as signals instead of considering them as noise that must removed before seismic imaging. However, when we use conventional reverse-time migration to image these multiple events, it results in acausal imaging artifacts caused by cross-correlations between wrong pairs of up- and downgoing wavefields. We find that these artifacts can be removed by adopting a sparsity-promoting least-squares inversion approach. However, iterative inversions go at the expense of excessive computational costs. By combining the SRME relation and wave-equation based linearized modelling, we are able to significantly reduce the costs by avoiding the dense matrix-matrix multiplications of SRME and the number of wave-equation solves via source subsampling. As a result, we arrive at a method with a cost comparable to that of a single reverse-time migration with all shots.},
	keywords = {inversion, multiples, SEG, workshop},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/herrmann2014SEGWSfli/herrmann2014SEGWSfli.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/herrmann2014SEGWSfli/herrmann2014SEGWSfli_pres.pdf},
	author = {Ning Tu and Felix J. Herrmann}
}
@conference {herrmann2014SEGAGUWSiii,
	title = {Imaging/Inversion with irregular/random sampled spatial arrays},
	booktitle = {SEG-AGU Workshop on Advances in Active + Passive "Full Wavefield" Seismic Imaging: From Reservoirs to Plate Tectonics},
	year = {2014},
	note = {(SEG-AGU Workshop, 22-24 July, Vancouver)},
	month = {07},
	keywords = {Imaging, inversion, randomization, SEG-AGU, workshop},
	author = {Felix J. Herrmann}
}
@conference {lin2014SEGmdg,
	title = {Mitigating data gaps in the estimation of primaries by sparse inversion without data reconstruction},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2014},
	note = {(SEG)},
	month = {10},
	pages = {4157-4161},
	abstract = {We propose to solve the Estimation of Primaries by Sparse Inversion problem from a sesimic record with missing near-offsets and large holes without any explicit data reconstruction, by instead simulating the missing multiple contributions with terms involving auto-convolutions of the primary wavefield. Exclusion of the unknown data as an inversion variable from the REPSI process is desireable, since it eliminates a significant source of local minima that arises from attempting to invert for the unobserved traces using primary and multiple models that may be far-away from the true solution. In this talk we investigate the necessary modifications to the Robust EPSI algorithm to account for the resulting non-linear modeling operator, and demonstrate that just a few auto-convolution terms are enough to satisfactorily mitigate the effects of data gaps during the inversion process.},
	keywords = {algorithm, EPSI, inversion, multiples, REPSI, SEG},
	doi = {http://dx.doi.org/10.1190/segam2014-1680.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/lin2014SEGmdg/lin2014SEGmdg.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/lin2014SEGmdg/lin2014SEGmdg_pres.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@conference {tu2013SEGcle,
	title = {Controlling linearization errors in $\ell_1$ regularized inversion by rerandomization},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {32},
	year = {2013},
	month = {09},
	pages = {4640-4644},
	abstract = {Linearized inversion is a data fitting procedure that tries to match the observed seismic data with data predicted by linearized modelling. In practice, the observed data is not necessarily in the column space of the linearized modelling operator. This can be caused by lack of an accurate background velocity model or by coherent noises not explained by linearized modelling. Through carefully designed experiments, we ob- serve that a moderate data mismatch does not pose an issue if we can use all the data in the inversion. However, artifacts do arise from the mismatch when randomized dimensionality reduction techniques are adopted to speed up the inversion. To stabilize the inversion for dimensionality reduction with randomized source aggregates, we propose to rerandomize by drawing independent simultaneous sources occasionally during the inversion. The effect of this rerandomization is remarkable because it results in virtually artifact-free images at a cost comparable to a single reverse-time migration. Implications of our method are profound because we are now able to resolve fine-scale steep subsalt features in a computationally feasible manner.},
	keywords = {inversion, message passing, rerandomization, SEG, sparsity},
	doi = {10.1190/segam2013-1302.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2013/tu2013SEGcle/tu2013SEGcle.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2013/tu2013SEGcle/tu2013SEGcle_pres.pdf},
	author = {Ning Tu and Xiang Li and Felix J. Herrmann}
}
@conference {kumar2013SEGMVA,
	title = {Efficient WEMVA using extended images},
	booktitle = {SEG Workshop on Advances in Model Building, Imaging, and FWI; Houston},
	year = {2013},
	month = {09},
	abstract = {Image gathers as a function of subsurface offset are an important tool for velocity analysis in areas of complex geology. Here, we offer a new perspective on image gathers by organizing the extended image as a function of all subsurface offsets and all subsurface points in to a matrix whose (i,j)th entry captures the interaction between gridpoints i and j. For even small problems, it is infeasible to form and store this matrix. Instead, we propose an efficient algorithm to glean information from the image volume via efficient matrix-vector products. We illustrate how this can be used to construct objective functions for automated MVA.},
	keywords = {inversion, MVA, poster, SEG, workshop},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2013/kumar2013SEGMVA/kumar2013SEGMVA_poster.pdf},
	author = {Rajiv Kumar and Tristan van Leeuwen 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}
}
@techreport {vanleeuwen2012SEGparallel,
	title = {A parallel, object-oriented framework for frequency-domain wavefield imaging and inversion.},
	number = {TR-EOAS-2012-2},
	year = {2012},
	month = {04},
	institution = {Department of Earth and Ocean Sciences},
	address = {University of British Columbia, Vancouver},
	abstract = {We present a parallel object-oriented matrix-free framework for frequency-domain seismic modeling, imaging and inversion. The key aspects of the framework are its modularity and level of abstraction, which allows us to write code that reflects the underlying mathematical structure and develop unit-tests that guarantee the fidelity of the code. By overloading standard linear-algebra operations, such as matrix-vector multiplications, we can use standard optimization packages to work with our code without any modification. This leads to a scalable testbed on which new methods can be rapidly prototyped and tested on medium-sized 2D problems. Although our current implementation uses (parallel) Matlab, all of these design principles can also be met by using lower-level languages which is important when we want to scale to realistic 3D problems. We present some numerical examples on synthetic data.},
	keywords = {Imaging, inversion, Modeling, SEG},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2012/vanleeuwen2012SEGparallel/vanleeuwen2012SEGparallel.pdf},
	author = {Tristan van Leeuwen and Felix J. Herrmann}
}
@conference {li2012SEGspmamp,
	title = {Sparsity-promoting migration accelerated by message passing},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {31},
	year = {2012},
	month = {11},
	pages = {1-6},
	publisher = {SEG},
	organization = {SEG},
	abstract = {Seismic imaging via linearized inversion requires multiple iterations to minimize the least-squares misfit as a function of the medium perturbation. Unfortunately, the cost for these iterations are prohibitive because each iteration requires many wave-equation simulations, which without direct solvers require an expensive separate solve for each source. To overcome this problem, we use dimensionality-reduction to decrease the size of seismic imaging problem by turning the large number of sequential shots into a much small number of simultaneous shots. In our approach, we take advantage of sparsifying transforms to remove source crosstalk resulting from randomly weighting and stacking sequential shots into a few super shots. We also take advantage of the fact that the convergence of large-scale sparsity-promoting solvers can be improved significantly by borrowing ideas from message passing, which are designed to break correlation built up between the linear system and the model iterate. In this way, we arrive at a formulation where we run the sparsity-promoting solver for a relatively large number of very iterations. Aside from leading to a significant speed up, our approach had the advantage of greatly reducing the memory imprint and IO requirements. We demonstrate this feature by solving a sparsity-promoting imaging problem with operators of reverse-time migration, which is computationally infeasible without the dimensionality reduction.},
	keywords = {Imaging, inversion, SEG},
	doi = {10.1190/segam2012-1500.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2012/li2012SEGspmamp/li2012SEGspmamp.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2012/li2012SEGspmamp/li2012SEGspmamp_pres.pdf},
	author = {Xiang Li and Felix J. Herrmann}
}
