@conference {cirakman2025IMAGEesp,
	title = {Efficient and scalable posterior surrogate for seismic inversion via wavelet score-based generative models},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2025},
	month = {8},
	keywords = {Bayesian inference, deep learning, generative model, IMAGE, Imaging, Inverse problems, power-scaling, RTM, score, SEG, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/cirakman2025IMAGEesp},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/cirakman2025IMAGEesp/IMAGE_Ege.pdf},
	author = {Ege Cirakman and Huseyin Tuna Erdinc and Felix J. Herrmann}
}
@unpublished {gahlot2025EAGEerd,
	title = {Enhancing robustness of Digital Shadow for CO 2  Storage Monitoring with Augmented Rock Physics Modeling},
	year = {2025},
	month = {6},
	keywords = {Amortized Variational Inference, augmentation, Bayesian inference, conditional normalizing flows, data assimilation, deep learning, FWI, GCS, Imaging, Inverse problems, rock physics, RTM, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Submitted/2025/gahlot2025EAGEerd/abstract.html},
	author = {Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@presentation {zeng2024ML4SEISMICefv,
	title = {Enhancing Full-Waveform Variational Inference through Stochastic Resampling},
	year = {2024},
	month = {11},
	abstract = {Recent developments in simulation-based inference, like the full-waveform variational inference via subsurface extensions (WISE), enable rapid online estimation of subsurface velocities by leveraging pre-trained models. To achieve this, WISE employs subsurface-offset common image gathers to convert shot data into physics-informed summary statistics. While common image gathers effectively retain critical information even when initial velocity estimates are inaccurate, WISE{\textquoteright}s performance still depends on the assumption that the initial migration-velocity model is a single 1D velocity model. In this work, we present experiments using both 1D and 2D velocity models and develop a stochastic resampling method to generate variations of initial migration-velocity models. This technique allows us to systematically infer alternative velocity models that are consistent with the observed data, while enhancing the posterior sample quality and reducing dependency on the initial velocity model compared to the standard WISE approach.},
	keywords = {Amortized Variational Inference, Bayesian inference, CIG, conditional normalizing flows, deep learning, FWI, Imaging, Inverse problems, MVA, RTM, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/zeng2024ML4SEISMICefv},
	author = {Yunlin Zeng and Ziyi Yin and Rafael Orozco and Mathias Louboutin and Felix J. Herrmann}
}
@conference {zeng2024IMAGEefw,
	title = {Enhancing Full-Waveform Variational Inference through Stochastic Resampling and Data Augmentation},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2024},
	month = {8},
	keywords = {Amortized Variational Inference, Bayesian inference, CIG, conditional normalizing flows, deep learning, FWI, IMAGE, Imaging, Inverse problems, MVA, RTM, SEG, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slimgroup.github.io/IMAGE2024/Yunlin_Zeng2024SEG/paper.html},
	author = {Yunlin Zeng and Rafael Orozco and Ziyi Yin and Felix J. Herrmann}
}
@presentation {zeng2024ML4SEISMICepu,
	title = {Enhancing Performance with Uncertainty Estimation in Geological Carbon Storage Leakage Detection from Time-Lapse Seismic Data},
	year = {2024},
	month = {11},
	abstract = {Ensuring CO 2  non-leakage is a critical aspect of Geological Carbon Storage (GCS). While previous approaches that develop deep neural networks demonstrate promising automatic leakage detection and potential cost reduction in dataset collection from time-lapse seismic images, they face challenges, such as a limited ability to reduce false alarms in CO 2  leakage instances and a lack of uncertainty analysis in detection results. This paper introduced a framework aimed at enhancing the deep neural network model{\textquoteright}s ability to detect GCS leakage risk through a multi-criteria decision-making (MCDM)-based ensemble algorithm. The proposed method can improve the detection ability of leakage cases while accurately distinguishing them from non-leakage instances. Furthermore, the proposed uncertainty analysis method utilizing Monte Carlo (MC) dropout technique efficiently identifies misclassified non-leakage cases and categorizes them as undetermined for further investigation. This comprehensive approach enhances both the reliability and performance of the model in detecting GCS leakage risks.},
	keywords = {CAM, CCS, classification, explainability, GCS, Imaging, Inverse problems, JRM, ML4SEISMIC, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/zeng2024ML4SEISMICepu},
	author = {Shiqin Zeng and Huseyin Tuna Erdinc and Ziyi Yin and Abhinav Prakash Gahlot and 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}
}
@conference {erdinc2023IMAGEecl,
	title = {Enhancing CO2 Leakage Detectability via Dataset Augmentation},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2023},
	note = {(IMAGE, Houston)},
	month = {08},
	abstract = {Previous work showed that neural classifiers can be trained to detect CO2 leakage from time-lapse seismic images. While this result is crucial to the global deployment of geological carbon storage (GCS), its success depends on relatively dense non-replicated time-lapse data acquisition. In this study, we show that by augmenting the training set with various coarse receiver samplings and corresponding seismic images, we can improve the leakage detection capabilities and accuracy while increasing the robustness with respect to low-cost coarse receiver samplings, e.g. ocean bottom nodes (OBNs).},
	keywords = {CCS, classifier, deep learning, leakage detection, SEG},
	url = {https://slimgroup.github.io/IMAGE2023/DetectabilityWithVision/abstract.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2023/erdinc2023IMAGEecl},
	author = {Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {grady2022ML4SEISMICesn,
	title = {Effective scaling of numerical surrogates via domain-decomposed Fourier neural operators},
	year = {2022},
	month = {11},
	abstract = {Numerical surrogates are models which learn to mimic a complex physical process (such as the solution to a PDE produced by a solver) from a set of input/output pairs. Fourier neural operators (FNOs) are a specific type of numerical surrogate which use a learned matched filter to quickly approximate solutions to relatively smooth complex physical processes. In the case of carbon capture sequestration (CCS) technology, FNOs have been shown to well-approximate solutions to the two-phase flow equations, with speedups of 1,000 to 10,000 times at inference time versus a tradtitional solver. This speed combined with the fact that FNOs are differentiable with respect to their input parameters allows for inverse and uncertainty quantification problems to theoretically be solved on real 3D data, a previously intractible task. However, due to the size of the input data, network weights, and optimizer state, FNOs have thus far been limited to small to medium 2D and 3D problems, well below the size of an industry standard such as the Sleipner benchmark. Here we alleviate this problem by proposing a model-parallel FNO which makes use of domain decomposition of the input data and network weights, and exploits architectural features of FNOs to also include a natural form of asynchronous pipeline parallelism. Our network can scale to arbitrary problem sizes on CPU and GPU systems.},
	keywords = {CCS, deep learning, Fourier neural operators, HPC, ML4SEISMIC, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/grady2022ML4SEISMICesn/index.html},
	author = {Thomas J. Grady II and Rishi Khan and Mathias Louboutin and Ziyi Yin and Philipp A. Witte and Ranveer Chandra and Russell J. Hewett 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 {kumar2021SEGeuq,
	title = {Enabling uncertainty quantification for seismic data pre-processing using normalizing flows (NF){\textemdash}an interpolation example},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2021},
	note = {(IMAGE, Denver)},
	month = {09},
	pages = {1515-1519},
	abstract = {Seismic data go through a sequence of pre-processing steps before being made into an image. Although some work has been done to assess the uncertainties in the final images, how the uncertainty in the pre-processing affects the results remains largely unexplored. We use Normalizing Flows (NF), a type of deep neural network, to interpolate seismic data and quantify the associated uncertainty. A big advantage of NFs, over the more commonly used Markov Chain Monte Carlo methods, is that they can successfully sample a complex and high-dimensional probability space with fewer assumptions. We present the first application of NF in interpolating (synthetic) seismic data. The statistical measurements retrieved from the network can be used to better characterize the data as it is passed to the post-processing phase.},
	keywords = {deep learning, normalizing flow, SEG, wavefield reconstruction},
	doi = {10.1190/segam2021-3583705.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2021/kumar2021SEGeuq/kumar2021SEGeuq.pdf},
	software = {https://github.com/slimgroup/Software.SEG2021},
	author = {Rajiv Kumar and Maria Kotsi and Ali Siahkoohi and Alison Malcolm}
}
@article {witte2019TPDedas,
	title = {An Event-Driven Approach to Serverless Seismic Imaging in the Cloud},
	journal = {IEEE Transactions on Parallel and Distributed Systems},
	volume = {31},
	number = {9},
	year = {2020},
	note = {(IEEE Transactions on Parallel and Distributed Systems)},
	month = {03},
	pages = {2032-2049},
	abstract = {Adapting the cloud for high-performance computing (HPC) is a challenging task, as software for HPC applications hinges on fast network connections and is sensitive to hardware failures. Using cloud infrastructure to recreate conventional HPC clusters is therefore in many cases an infeasible solution for migrating HPC applications to the cloud. As an alternative to the generic lift and shift approach, we consider the specific application of seismic imaging and demonstrate a serverless and event-driven pproach for running large-scale instances of this problem in the cloud. Instead of permanently running compute instances, our workflow is based on a serverless architecture with high throughput batch computing and event-driven computations, in which computational resources are only running as long as they are utilized. We demonstrate that this approach is very flexible and allows for resilient and nested levels of parallelization, including domain decomposition for solving the underlying partial differential equations. While the event-driven approach introduces some overhead as computational resources are repeatedly restarted, it inherently provides resilience to instance shut-downs and allows a significant reduction of cost by avoiding idle instances, thus making the cloud a viable alternative to on-premise clusters for large-scale seismic imaging.},
	keywords = {cloud, event-driven, Imaging, LSRTM, serverless},
	doi = {10.1109/TPDS.2020.2982626},
	url = {https://slim.gatech.edu/Publications/Public/Journals/IEEETPDS/2020/witte2019TPDedas/witte2019TPDedas.html},
	author = {Philipp A. Witte and Mathias Louboutin and Henryk Modzelewski and Charles Jones and James Selvage and Felix J. Herrmann}
}
@conference {yin2020SEGesi,
	title = {Extended source imaging {\textendash}- a unifying framework for seismic and medical imaging},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2020},
	note = {(SEG, virtual)},
	month = {09},
	pages = {3502-3506},
	abstract = {We present three imaging modalities that live on the crossroads of seismic and medical imaging. Through the lens of extended source imaging, we can draw deep connections among the fields of wave-equation based seismic and medical imaging, despite first appearances. From the seismic perspective, we underline the importance to work with the correct physics and spatially varying velocity fields. Medical imaging, on the other hand, opens the possibility for new imaging modalities where outside stimuli, such as laser or radar pulses, can not only be used to identify endogenous optical or thermal contrasts but that these sources can also be used to insonify the medium so that images of the whole specimen can in principle be created.},
	keywords = {medical imaging, SEG, seismic imaging, variable projection},
	doi = {10.1190/segam2020-3426999.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/yin2020SEGesi/yin2020SEGesi.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/yin2020SEGesi/yin2020SEGesi_pres.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/yin2020SEGesi/yin2020SEGesi_pres.mp4},
	software = {https://github.com/slimgroup/Software.SEG2020},
	author = {Ziyi Yin and Rafael Orozco and Philipp A. Witte and Mathias Louboutin and Gabrio Rizzuti and Felix J. Herrmann}
}
@conference {witte2019SEGedw,
	title = {Event-driven workflows for large-scale seismic imaging in the cloud},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2019},
	note = {(SEG, San Antonio)},
	month = {09},
	pages = {3984-3988},
	abstract = {Cloud computing has seen a large rise in popularity in recent years and is becoming a cost effective alternative to on-premise computing, with theoretically unlimited scalability. However, so far little progress has been made in adapting the cloud for high performance computing (HPC) tasks, such as seismic imaging and inversion. As the cloud does not provide the same type of fast and reliable connections as conventional HPC clusters, porting legacy codes developed for HPC environments to the cloud is ineffective and misses out on an opportunity to take advantage of new technologies presented by the cloud. We present a novel approach of bringing seismic imaging and inversion workflows to the cloud, which does not rely on a traditional HPC environment, but is based on serverless and event-driven computations. Computational resources are assigned dynamically in response to events, thus minimizing idle time and providing resilience to hardware failures. We test our workflow on two large-scale imaging examples and demonstrate that cost-effective HPC in the cloud is possible, but requires careful reconsiderations of how to bring software to the cloud.},
	keywords = {cloud, Imaging, large-scale, LS-RTM, RTM, SEG, workflow},
	doi = {10.1190/segam2019-3215069.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/witte2019SEGedw/witte2019SEGedw.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/witte2019SEGedw/witte2019SEGedw_pres.pdf},
	author = {Philipp A. Witte and Mathias Louboutin and Henryk Modzelewski and Charles Jones and James Selvage and Felix J. Herrmann}
}
@conference {louboutin2018SEGeow,
	title = {Effects of wrong adjoints for RTM in TTI media},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2018},
	note = {(SEG, Anaheim)},
	month = {10},
	pages = {331-335},
	abstract = {In order to obtain accurate images of the subsurface, anisotropic modeling and imaging is necessary. However, the twenty-one parameter complete wave-equation is too computationally expensive to be of use in this case. The transverse tilted isotropic wave-equation is then the next best feasible representation of the physics to use for imaging. The main complexity arising from transverse tilted isotropic imaging is to model the receiver wavefield (back propagation of the data or data residual) for the imaging condition. Unlike the isotropic or the full physics wave-equations, the transverse tilted isotropic wave-equation is not not self-adjoint. This difference means that time-reversal will not model the correct receiver wavefield and this can lead to incorrect subsurface images. In this work, we derive and implement the adjoint wave-equation to demonstrate the necessity of exact adjoint modeling for anisotropic modeling and compare our result with adjoint-free time-reversed imaging.},
	keywords = {Adjoint, anisotropy, finite-differences, Imaging, RTM, SEG, TTI},
	doi = {10.1190/segam2018-2996274.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/louboutin2018SEGeow/louboutin2018SEGeow.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/louboutin2018SEGeow/louboutin2018SEGeow_pres.pdf},
	author = {Mathias Louboutin and Philipp A. Witte and Felix J. Herrmann}
}
@mastersthesis {oghenekohwo2017THetl,
	title = {Economic time-lapse seismic acquisition and imaging{\textendash}-Reaping the benefits of randomized sampling with distributed compressive sensing},
	year = {2017},
	note = {(PhD)},
	month = {08},
	school = {The University of British Columbia},
	type = {phd},
	address = {Vancouver},
	abstract = {This thesis presents a novel viewpoint on the implicit opportunities randomized surveys bring to time-lapse seismic - which is a proven surveillance tool for hydrocarbon reservoir monitoring. Time-lapse (4D) seismic combines acquisition and processing of at least two seismic datasets (or vintages) in order to extract information related to changes in a reservoir within a specified time interval. The current paradigm places stringent requirements on replicating the 4D surveys, which is an expensive task often requiring uneconomical dense sampling of seismic wavefields. To mitigate the challenges of dense sampling, several advances in seismic acquisition have been made in recent years including the use of multiple sources firing at near simultaneous random times, and the adaptation of Compressive Sensing (CS) principles to design practical acquisition engines that improve sampling efficiency for seismic data acquisition. However, little is known regarding the implications of these developments for time-lapse studies. By conducting multiple experiments modelling surveys adhering to the principles of CS for 4D seismic, I propose a model that demonstrates the feasibility of randomized acquisitions for time-lapse seismic. The proposed joint recovery model (JRM), which derives from distributed CS, exploits the common information in time-lapse data during recovery of dense wavefields from measured subsampled data, providing highly repeatable and high-fidelity vintages. I show that we obtain better vintages when randomized surveys are not replicated, in contrast to standard practice, paving the way for an opportunity to relax the rigorous requirement to replicate surveys precisely. We assert that the vintages obtained using our proposed model are of sufficient quality to serve as inputs to processes that extract time-lapse attributes from which subsurface changes are deduced. Additionally, I show that recovery with the JRM is robust with respect to errors due to differences between actual and recorded postplot information. Finally, I present an opportunity to adapt our model to problems related to time-lapse seismic imaging where the main finding is that we can better delineate time-lapse changes by adapting the joint recovery model to wave-equation based inversion methods.},
	keywords = {Acquisition, Compressive Sensing, distributed compressive sensing, joint recovery, PhD, thesis, time lapse},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2017/oghenekohwo2017THetl/oghenekohwo2017THetl.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Thesis/2017/oghenekohwo2017THetl/oghenekohwo2017THetl_pres.pdf},
	author = {Felix Oghenekohwo}
}
@conference {fang2017AIPepm,
	title = {An efficient penalty method for PDE-constrained optimization problem with source estimation and stochastic optimization},
	booktitle = {Applied Inverse Problems Annual Conference Proceedings},
	year = {2017},
	note = {(AIP, Hangzhou)},
	month = {05-06},
	pages = {40},
	abstract = {Many inverse problems in applied science and engineering can be written as PDE-constrained optimization problems. Typically, one is interested in estimating the unknown medium parameters of the PDE describing the system, given multi-source measurements of the fields in the true medium. The objective function is often very oscillatory, which gives rise to problematic local minima, and merely evaluating one or more of its derivatives requires solving multiple PDEs, which are expensive. In many applications such as seismic exploration, only the source location is available and the source signature itself is unknown, which results in erroneous estimated parameters. In this work, we propose a penalty method to solve the PDE-constrained optimization problem, whereby we replace the PDE constraints by a two-norm penalty term, which leads to a bi-linear optimization problem with a much less oscillatory objective function. By applying the variable projection method, we develop an efficient method to invert the unknown parameters and source signatures simultaneously. To reduce the computational cost, we randomly select a subset of all data to update the parameters at each iteration. Numerical examples demonstrate that this method is less prone to becoming stuck in harmful local minima and is able to successfully invert the unknown parameters and source signature with less computational cost compared to using all the data.},
	keywords = {Optimization, penalty method, source estimation, Stochastic optimization},
	url = {http://aip2017.csp.escience.cn/dct/page/1},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/AIP/2017/fang2017AIPepm/fang2017AIPepm_pres.pdf},
	author = {Zhilong Fang and Curt Da Silva and Felix J. Herrmann}
}
@article {vanleeuwen2015GPWEMVA,
	title = {Enabling affordable omnidirectional subsurface extended image volumes via probing},
	journal = {Geophysical Prospecting},
	volume = {65},
	number = {2},
	year = {2017},
	note = {(Geophysical Prospecting)},
	month = {03},
	pages = {385-406},
	abstract = {Image gathers as a function of subsurface offset are an important tool for the inference of rock properties and velocity analysis in areas of complex geology. Traditionally, these gathers are thought of as multidimensional correlations of the source and receiver wavefields. The bottleneck in computing these gathers lies in the fact that one needs to store, compute, and correlate these wavefields for all shots in order to obtain the desired image gathers. Therefore, the image gathers are typically only computed for a limited number of subsurface points and for a limited range of subsurface offsets, which may cause problems in complex geological areas with large geologic dips. We overcome increasing computational and storage costs of extended image volumes by introducing a formulation that avoids explicit storage and removes the customary and expensive loop over shots found in conventional extended imaging. As a result, we end up with a matrix{\textendash}vector formulation from which different image gathers can be formed and with which amplitude-versus-angle and wave-equation migration velocity analysis can be performed without requiring prior information on the geologic dips. Aside from demonstrating the formation of two-way extended image gathers for different purposes and at greatly reduced costs, we also present a new approach to conduct automatic wave-equation-based migration-velocity analysis. Instead of focusing in particular offset directions and preselected subsets of subsurface points, our method focuses every subsurface point for all subsurface offset directions using a randomized probing technique. As a consequence, we obtain good velocity models at low cost for complex models without the need to provide information on the geologic dips.},
	keywords = {AVA, migration velocity analysis, Stochastic optimization},
	doi = {10.1111/1365-2478.12418},
	url = {https://slim.gatech.edu/Publications/Public/Journals/GeophysicalProspecting/2016/vanleeuwen2015GPWEMVA/vanleeuwen2015GPWEMVA.pdf},
	author = {Tristan van Leeuwen and Rajiv Kumar and Felix J. Herrmann}
}
@mastersthesis {kumar2017THels,
	title = {Enabling large-scale seismic data acquisition, processing and waveform-inversion via rank-minimization},
	year = {2017},
	note = {(PhD)},
	month = {08},
	school = {The University of British Columbia},
	type = {phd},
	address = {Vancouver},
	abstract = {In this thesis, I adapt ideas from the field of compressed sensing to mitigate the computational and memory bottleneck of seismic processing workflows such as missing-trace interpolation, source separation and wave-equation based inversion for large-scale 3- and 5-D seismic data. For interpolation and source separation using rank-minimization, I propose three main ingredients, namely a rank-revealing transform domain, a subsampling scheme that increases the rank in the transform domain, and a practical large-scale data-consistent rank-minimization framework, which avoids the need for expensive computation of singular value decompositions. We also devise a wave-equation based factorization approach that removes computational bottlenecks and provides access to the kinematics and amplitudes of full-subsurface offset extended images via actions of full extended image volumes on probing vectors, which I use to perform the amplitude-versus- angle analyses and automatic wave-equation migration velocity analyses on complex geological environments. After a brief overview of matrix completion techniques in Chapter 1, we propose a singular value decomposition (SVD)-free factorization based rank-minimization approach for large-scale matrix completion problems. Then, I extend this framework to deal with large-scale seismic data interpolation problems, where I show that the standard approach of partitioning the seismic data into windows is not required, which use the fact that events tend to become linear in these windows, while exploiting the low-rank structure of seismic data. Carefully selected synthetic and realistic seismic data examples validate the efficacy of the interpolation framework. Next, I extend the SVD-free rank-minimization approach to remove the seismic cross-talk in simultaneous source acquisition. Experimental results verify that source separation using the SVD-free rank-minimization approaches are comparable to the sparsity-promotion based techniques; however, separation via rank-minimization is significantly faster and memory efficient. We further introduce a matrix-vector formulation to form full-subsurface extended image volumes, which removes the storage and computational bottleneck found in the convention methods. I demonstrate that the proposed matrix-vector formulation is used to form different image gathers with which amplitude-versus-angle and wave-equation migration velocity analyses is performed, without requiring prior information on the geologic dips. Finally, I conclude the thesis by outlining potential future research directions and extensions of the thesis work.},
	keywords = {Acquisition, extended image volumes, migration velocity analysis, PhD, Processing, rank minimization, waveform inversion},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2017/kumar2017THels/kumar2017THels.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Thesis/2017/kumar2017THels/kumar2017THels_pres.pdf},
	author = {Rajiv Kumar}
}
@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}
}
@presentation {fang2016SINBADFeaq,
	title = {Efficient approach for quantifying uncertainty of wavefield reconstruction inversion},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Due to the noisy measurements, sparse observations, uncertain forward models, and uncertain prior parameter information, significant uncertainty arises in the inverted velocity model of full-waveform inversion. The uncertain velocity model leads to the uncertainty in the correct positions of events in the migrated images, which subsequently influences the important decision-makings in the oil and gas exploration and production (E\&P) business such as the drilling decisions and economic evaluations. Therefore, understanding the uncertainty in the inverted velocity model is essential for the mitigation of the E\&P risks. A common approach to accounting for uncertainty in seismic inverse problems like full-waveform inversion is to describe the unknown parameters in probabilistic by the Bayesian inference. The Bayesian inference aims at incorporating the information from the data, physics, and one{\textquoteright}s prior knowledge to formulate a posterior distribution function to characterize the statistical properties of the unknown parameters. However, characterizing uncertainties for the large-scale full-waveform inversion in seismic exploration is prohibitively expensive since it requires thousands of function evaluations to sample the parameter space (e.g. via Markov chain Monte Carlo sampling). Apart from this, the existing methods of uncertainty quantification for full-waveform inversion is through applying the Bayesian inference to the standard method such as the adjoint-state method with the assumption that no uncertainty arises from the wave-equation. However, the wave-equation may not be able to reflect all the physics, hence, incorporating uncertainty from wave-equation might benefit the quantification of uncertainty. In this work, we propose to perform uncertainty quantification using the recently proposed wavefield reconstruction inversion technique, which gives an appropriate framework to tackle the uncertainty in wave-equation solve by adding the misfit of the wave-equation to the likelihood distribution and relaxing the wave-equation constraint. Moreover, we propose a computationally feasible approach to analyzing the posterior distribution of the wavefield reconstruction inversion method, which incorporates a Gaussian distribution that approximates the posterior distribution with an optimization-driven Gaussian simulator to sample the Gaussian distribution efficiently. Numerical examples show that comparing to the McMC method, our approach is able to produce comparable results with less computational cost. This is joint work with Curt da Silva and Rachel Kuske.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/fang2016SINBADFeaq/fang2016SINBADFeaq.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/fang2016SINBADFeaq/fang2016SINBADFeaq.mov},
	author = {Zhilong Fang and Chia Ying Lee and Curt Da Silva and Tristan van Leeuwen and Felix J. Herrmann and Rachel Kuske}
}
@presentation {kumar2016SINBADFels,
	title = {Efficient large-scale 5D seismic data acquisition and processing using rank-minimization},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Seismic data collection is becoming challenging because of increased demands for high-quality, long-offset and wide-azimuth data. Leveraging ideas from CS, in this work we establish a cost effective acquisition and processing techniques, which are no longer dominated by survey area size but by the sparsity of seismic data volumes. In the first part of abstract, we establish connections between random time dithering and jittered sampling in space. Specifically, we recover high-quality 5D seismic data volumes from time-jittered marine acquisition where the average inter-shot time is significantly reduced, leading to cheaper surveys due to fewer overlapping shots. The time-jittered acquisition, in conjunction with the shot separation by Singular-Value Decomposition (SVD)-free factorization based rank-minimization approach, allows us to recover high quality 5D seismic data volumes. Results are illustrated for simulations of simultaneous time-jittered continuous recording for a 3D ocean-bottom cable survey, where we outperforms existing techniques, by an order of magnitude computational speedup and using 1/20th of the memory, that use sparsity in transforms domains. The second part of abstract focussed on leveraging low-rank structure in seismic data to solve extremely large data recovery (interpolation) problems. We introduced a large{\textendash}scale SVD-free optimization framework that is robust with respect to outliers and that uses information on the support. We test the efficacy of the proposed interpolation framework on a large-scale 5D seismic data, generated from the geologically complex synthetic 3D Compass velocity model, where 80\% of the data has been removed. Our findings show that major computational and memory gains are possible compared to curvelet-based reconstruction.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/kumar2016SINBADFels/kumar2016SINBADFels.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/kumar2016SINBADFels/kumar2016SINBADFels.mov},
	author = {Rajiv Kumar and Shashin Sharan and Haneet Wason and Felix J. Herrmann}
}
@article {lin2015scatterEPSI,
	title = {Estimation of primaries by sparse inversion with scattering-based multiple predictions for data with large gaps},
	journal = {Geophysics},
	volume = {81},
	number = {3},
	year = {2016},
	note = {(Geophysics)},
	month = {05},
	pages = {V183-V197},
	abstract = {We propose to solve the Estimation of Primaries by Sparse Inversion problem without any explicit data reconstruction, from a seismic record with missing near-offsets and large holes in the acquisition grid which does not produce aliasing but otherwise causes large errors in the multiple prediction. Exclusion of the unknown data as an inversion variable from the process is desirable, since it sidesteps possible issues arising from fitting observations that are also unknowns in the same inversion problem. Instead, we simulate their multiple contributions by augmenting the forward prediction model for the total wavefield with a scattering series that mimics the action of the free surface reflector within the confines of the unobserved trace locations. Each term in this scattering series simply involves convolution of the predicted wavefield once more with the current estimated surface-free Green{\textquoteright}s function at these unobserved locations. We investigate the necessary modifications to the primary estimation algorithm to account for the resulting nonlinearity in the modeling operator, and also demonstrate that just a few scattering terms are enough to satisfactorily mitigate the effects of near-offset data gaps during the inversion process. Numerical experiments on synthetic data show that the final derived method can significantly outperform explicit data reconstruction for large near-offset gaps. This is achieved with a similar computational cost and better memory efficiency compared to explicit data reconstruction. We also show on real data that our scheme outperforms pre-interpolation of the near-offset gap.},
	keywords = {EPSI, multiples, Optimization, REPSI, scattering, sparsity},
	doi = {10.1190/geo2015-0263.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2016/lin2015scatterEPSI/lin2015scatterEPSI.html},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@article {kumar2014GEOPemc,
	title = {Efficient matrix completion for seismic data reconstruction},
	journal = {Geophysics},
	volume = {80},
	number = {5},
	year = {2015},
	note = {(Geophysics)},
	month = {09},
	pages = {V97-V114},
	abstract = {Despite recent developments in improved acquisition, seismic data often remain undersampled along source and receiver coordinates, resulting in incomplete data for key applications such as migration and multiple prediction. We have interpreted the missing-trace interpolation problem in the context of matrix completion (MC), and developed three practical principles for using low-rank optimization techniques to recover seismic data. Specifically, we strive for recovery scenarios wherein the original signal is low rank and the subsampling scheme increases the singular values of the matrix. We use an optimization program that restores this low-rank structure to recover the full volume. Omitting one or more of these principles can lead to poor interpolation results, as we found experimentally. In light of this theory, we compensate for the high-rank behavior of data in the source-receiver domain by using the midpoint-offset transformation for 2D data and a source-receiver permutation for 3D data to reduce the overall singular values. Simultaneously, to work with computationally feasible algorithms for large-scale data, we use a factorization-based approach to MC, which significantly speeds up the computations compared with repeated singular value decompositions without reducing the recovery quality. In the context of our theory and experiments, we also find that windowing the data too aggressively can have adverse effects on the recovery quality. To overcome this problem, we carried out our interpolations for each frequency independently while working with the entire frequency slice. The result is a computationally efficient, theoretically motivated framework for interpolating missing-trace data. Our tests on realistic 2D and 3D seismic data sets show that our method compares favorably in terms of computational speed and recovery quality with existing curvelet- and tensor-based techniques.},
	keywords = {2D, 3D, algorithm, interpolation, low-rank, signal processing},
	doi = {10.1190/geo2014-0369.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2015/kumar2014GEOPemc/kumar2014GEOPemc.pdf},
	url2 = {http://library.seg.org/doi/abs/10.1190/geo2014-0369.1},
	author = {Rajiv Kumar and Curt Da Silva and Okan Akalin and Aleksandr Y. Aravkin and Hassan Mansour and Ben Recht and Felix J. Herrmann}
}
@presentation {louboutin2015SINBADFess,
	title = {Extending the search space of time-domain adjoint-state FWI w/ randomized implicit time shifts},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We introduce a modified adjoint-state method for time domain FWI that allows us to extend the research space. As a result, we arrive at a formulation where the sensitivity to cycle skipping is reduced. Our method obtains results with the same computational costs as FWI (The PDE solved is the same) but with significantly reduced memory costs. We use new results in non-convex optimization to justify the method as well as new regularization techniques and stochastic optimization to improve the behavior of the algorithm.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/louboutin2015SINBADFess/louboutin2015SINBADFess.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/louboutin2015SINBADFess/louboutin2015SINBADFess.mov},
	author = {Mathias Louboutin and Felix J. Herrmann}
}
@mastersthesis {miao2014THesi,
	title = {Efficient seismic imaging with spectral projector and joint sparsity},
	year = {2014},
	note = {(MSc)},
	month = {03},
	school = {The University of British Columbia},
	type = {masters},
	address = {Vancouver},
	abstract = {In this thesis, we investigate the potential of improving the eciency of seismic imaging with two advanced techniques: the spectral projector and the {\textquoteright}joint sparsity{\textquoteright}. The spectral projector offers an eigenvalue decomposition free computation routine that can filter out unstable evanescent wave com- ponents during wave equation based depth extrapolation. {\textquoteright}Joint sparsity{\textquoteright} aims to improve on the pure sparsity promoting recovery by making use of additional structure information of the signal. Besides, a new sparsity optimization algorithm - PQNL1 - is proposed to improve both theoretical convergence rate and practical performance for extremely large seismic imaging problems.},
	keywords = {MSc, thesis},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2014/miao2014THesi/miao2014THesi.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Thesis/2014/miao2014THesi/miao2014THesi_pres.pdf},
	author = {Lina Miao}
}
@presentation {zheglova2014SINBADead,
	title = {Exploring applications of depth stepping in seismic inverse problems},
	year = {2014},
	publisher = {SINBAD},
	abstract = {We are exploring applications of stable depth extrapolation with the full wave equation to imaging and inversion. Depth stepping with full wave equation can be advantageous to the time and frequency domain modelling if special care is taken to stabilize the depth exptrapolator efficiently, since it reduces the higher dimensional modelling problem to a number of lower dimensional subproblems. We are interested in exploring applications in inversion, modelling and imaging. For example, just as the reverse time migration can be shown to be the gradient of the reduced formulation of the full waveform inversion problem, it is interesting to explore whether a formulation of the inversion problem can be achieved whose gradient can be computed using depth stepping techniques. We are also interested in such applications as preconditioning of iterative methods for Helmholtz equation and imaging.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/zheglova2014SINBADead/zheglova2014SINBADead.pdf},
	author = {Polina Zheglova and Felix J. Herrmann}
}
@conference {kumar2014EAGEeia,
	title = {Extended images in action: efficient WEMVA via randomized probing},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2014},
	month = {06},
	abstract = {Image gathers as a function of subsurface offset are an important tool for velocity analysis in areas of complex geology. In this paper, we offer a new perspective on image gathers by organizing the extended image as a function of all subsurface offsets and all subsurface points into 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 automatic MVA.},
	keywords = {EAGE, extended imaging, MVA, probing},
	doi = {10.3997/2214-4609.20141492},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/kumar2014EAGEeia/kumar2014EAGEeia.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/kumar2014EAGEeia/kumar2014EAGEeia_pres.pdf},
	author = {Rajiv Kumar and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {kumar2014SINBADeia,
	title = {Extended images in action {\textendash}- image gather with surface-related multiples},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Common image gathers are used in building velocity models, invert anisotropy parameters and to analyze reservoir attributes. Often primary reflections are used to form the image gathers and multiples are typically attenuated in processing. However, researchers have shown that, if correctly used, they can provide useful information about the subsurface during reverse-time migration. In this work, i will show how we can use multiples along with primaries to form the image-gathers.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/kumar2014SINBADeia/kumar2014SINBADeia.pdf},
	author = {Rajiv Kumar and Ning Tu and Tristan van Leeuwen 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}
}
@presentation {oghenekohwo2013SINBADedt,
	title = {Estimating 4D differences in time-lapse using randomized sampling techniques},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Repeatability in the seismic survey and processing has been cited as the main reason for which 4D seismic technology work. In the last decade, concerted efforts have been spent to make the 4D seismic process highly repeatable, without significant success. On the contrary, Compressed Sensing, which is also a relatively new sampling paradigm proposes that one can recover an estimate of a fully sampled signal from a noisy, under-sampled measurements provided the acquisition architecture satisfies some properties. By observing different under-sampled and random measurements from each vintage, corresponding to different acquisition geometries, we show that one can still detect the 4D change in time using recent ideas from Compressed sensing. Using a realistic synthetic model, we show two methods of estimating the 4D difference and compare their relative performance to each other.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/oghenekohwo2013SINBADedt/oghenekohwo2013SINBADedt.pdf},
	author = {Felix Oghenekohwo and Ernie Esser and Felix J. Herrmann}
}
@presentation {peters2013SINBADepm,
	title = {Examples from the penalty-method},
	year = {2013},
	publisher = {SINBAD},
	abstract = {A novel penalty method for PDE-constrained optimization was recently proposed by van Leeuwen and Herrmann (2013). The conventional PDE-constrained optimization formulation used in seismic waveform inversion is based on calculating the gradient of the data misfit objective functional via the adjoint-state method, at the cost of two PDE solves. The penalty method requires only one solution of an overdetermined linear system, in the least-squares sense. In this talk some numerical properties of this linear system will be exposed. The penalty method for PDE-constrained optimization involves a parameter, balancing the data misfit and PDE-misfit parts of the objective functional. This talk will address how to select this very important parameter. Some examples will be shown in which the penalty method outperforms the conventional method in non-linear waveform inversion, as well as linearized seismic imaging by migration.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/peters2013SINBADepm/peters2013SINBADepm.pdf},
	author = {Bas Peters and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {herrmann2013SINBADeia,
	title = {Extended images in action},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Image gathers as a function of subsurface offset are an important tool for migration-velocity and amplitude-versus-angle analysis in areas of complex geology. Traditionally, these gathers are thought of as multidimensional correlations of the source and receiver wavefields. The bottleneck in computing the gathers lies in the fact that one needs to store and compute these wavefields and in correlating the wavefields to obtain the desired image gathers. Therefore, the image gathers are typically only computed for a limited number of subsurface points and for a limited range of subsurface offsets. In this presentation, we offer a new perspective on such gathers by organizing the extended image as a function of all subsurface offsets for all subsurface points in a matrix whose (i,j) entry captures the interaction between gridpoints i and j. Of course, it is infeasible to form and store this matrix. Instead, we propose an efficient algorithm to glean information from the image volume via the action of matrix-vector products with this matrix. We illustrate how this can be used to (i) form conventional image gathers and construct objective functions for automated MVA and (ii) calculate true two-way wave-equation angle gathers that allow us to carry out linearized angle-versus-offset inversion with geologic-dip correction. This is joint work with Tristan van Leeuwen and Rajiv Kumar.},
	keywords = {Presentation, SINBAD, SINBADSPRING2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/herrmann2013SINBADeia/herrmann2013SINBADeia.pdf},
	author = {Felix J. Herrmann}
}
@presentation {kumar2013SINBADava,
	title = {Extended images in action: efficient AVA via probing},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Common image gathers (CIG) are an important tool to perform AVA analysis in areas of complex geology. Unfortunately, it is prohibitively very expensive to compute these CIG for all the subsurface points. In this work, we present an efficient way to compute CIG for all subsurface offsets without explicitly calculating the source and receiver wavefields for all the sources. Because the CIG contain all possible subsurface offsets, we compute the angle-domain image gathers by selecting the subsurface offset that is aligned with the local geologic dip. We propose a method to compute the local dip information directly from common-image-point gathers. To assess the quality of the angle-domain common-image-points gathers we compute the angle-dependent reflectivity coefficients and compare them with theoretical reflectivity coefficients yielded by the (linearized) Zoeppritz equations for a few synthetic models.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/kumar2013SINBADava/kumar2013SINBADava.pdf},
	author = {Rajiv Kumar and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {kumar2013SINBADeia,
	title = {Extended images in action: efficient WEMVA via randomized probing},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Extended images as a function of the full subsurface offset are an important tool to perform wave-equation based migration velocity analysis (WEMVA) in areas of complex geology. Unfortunately, computation \& storage of these extended images is prohibitively expensive. In this work, we present an efficient way to compute extended images for all subsurface offsets without explicitly calculating the source and receiver wavefields for all the sources. Instead, we calculate actions of extended image volumes on probing vectors that live in the image space. The probing can either be defined as vectors from the Dirac basis, which allows us to form the extended image at the location of the point diffractor, or they can be defined in terms of Gaussian noise. The latter corresponds to sources with random weights firing simultaneously at every grid point. We demonstrate that this probing leads to a computationally efficient implementation of WEMVA. This is joint work with Tristan van Leeuwen.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/kumar2013SINBADeia/kumar2013SINBADeia.pdf},
	author = {Rajiv Kumar and Tristan van Leeuwen and Felix J. Herrmann}
}
@article {haber10TRemp,
	title = {An effective method for parameter estimation with PDE constraints with multiple right hand sides},
	journal = {SIAM Journal on Optimization},
	volume = {22},
	number = {3},
	year = {2012},
	month = {07},
	abstract = {Often, parameter estimation problems of parameter-dependent PDEs involve multiple right-hand sides. The computational cost and memory requirements of such problems increase linearly with the number of right-hand sides. For many applications this is the main bottleneck of the computation. In this paper we show that problems with multiple right-hand sides can be reformulated as stochastic programming problems by combining the right-hand sides into a few {\quotedblbase}simultaneous{\textquotedblright} sources. This effectively reduces the cost of the forward problem and results in problems that are much cheaper to solve. We discuss two solution methodologies: namely sample average approximation and stochastic approximation. To illustrate the effectiveness of our approach we present two model problems, direct current resistivity and seismic tomography.},
	keywords = {FWI, Optimization, SLIM},
	url = {http://dx.doi.org/10.1137/11081126X},
	author = {Eldad Haber and Matthias Chung and Felix J. Herrmann}
}
@conference {li2011CSEGefimag,
	title = {Efficient full-waveform inversion with marine acquisition geometry},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2012},
	month = {02},
	abstract = {Full-waveform inversion (FWI) is a nonlinear data fitting procedure based on seismic data to derive a accurate velocity model. With the increasing demand for high resolution images in complex geological settings, the importance of improvements in acquisition and inversion become more and more critical. However, these improvements will be obtained at high computational cost, as a typical marine survey contains thousands of shot and receiver positions, and FWI needs several passes through massive seismic data. Computational cost of FWI will grow exponentially as the size of seismic data and desired resolution increase. In this paper we present a modified Gauss-Newton (GN) method that borrows ideas from compressive sensing, where we compute the GN updates from a few randomly selected sequential shots. Each subproblem is solved by using a sparsity promoting algorithm. With this approach, we dramatically reduce the size and hence the computational costs of the problem, whilst we control information loss by redrawing a different set of sequential shots for each subproblem.},
	keywords = {CSEG},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2012/li2011CSEGefimag/li2011CSEGefimag.pdf},
	author = {Xiang Li and Felix J. Herrmann}
}
@article {herrmann11GPelsqIm,
	title = {Efficient least-squares imaging with sparsity promotion and compressive sensing},
	journal = {Geophysical Prospecting},
	volume = {60},
	number = {4},
	year = {2012},
	month = {07},
	pages = {696-712},
	address = {University of British Columbia, Vancouver},
	abstract = {Seismic imaging is a linearized inversion problem relying on the minimization of a least-squares misfit functional as a function of the medium perturbation. The success of this procedure hinges on our ability to handle large systems of equations{\textendash}-whose size grows exponentially with the demand for higher resolution images in more and more complicated areas{\textendash}-and our ability to invert these systems given a limited amount of computational resources. To overcome this "curse of dimensionality" in problem size and computational complexity, we propose a combination of randomized dimensionality-reduction and divide-and-conquer techniques. This approach allows us to take advantage of sophisticated sparsity-promoting solvers that work on a series of smaller subproblems each involving a small randomized subset of data. These subsets correspond to artificial simultaneous-source experiments made of random superpositions of sequential-source experiments. By changing these subsets after each subproblem is solved, we are able to attain an inversion quality that is competitive while requiring fewer computational, and possibly, fewer acquisition resources. Application of this concept to a controlled series of experiments showed the validity of our approach and the relationship between its efficiency{\textendash}-by reducing the number of sources and hence the number of wave-equation solves{\textendash}-and the image quality. Application of our dimensionality-reduction methodology with sparsity promotion to a complicated synthetic with well-log constrained structure also yields excellent results underlining the importance of sparsity promotion.},
	keywords = {Compressive Sensing, Imaging, Optimization, SLIM},
	doi = {10.1111/j.1365-2478.2011.01041.x},
	url = {https://slim.gatech.edu/Publications/Public/Journals/GeophysicalProspecting/2012/herrmann11GPelsqIm/herrmann11GPelsqIm.pdf},
	url2 = {http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2478.2011.01041.x/full},
	author = {Felix J. Herrmann and Xiang Li}
}
@article {aravkin2012IPNuisance,
	title = {Estimating nuisance parameters in inverse problems},
	journal = {Inverse Problems},
	volume = {28},
	number = {11},
	year = {2012},
	month = {10},
	abstract = {Many inverse problems include nuisance parameters which, while not of direct interest, are required to recover primary parameters. Structure present in these problems allows efficient optimization strategies - a well known example is variable projection, where nonlinear least squares problems which are linear in some parameters can be very efficiently optimized. In this paper, we extend the idea of projecting out a subset over the variables to a broad class of maximum likelihood (ML) and maximum a posteriori likelihood (MAP) problems with nuisance parameters, such as variance or degrees of freedom. As a result, we are able to incorporate nuisance parameter estimation into large-scale constrained and unconstrained inverse problem formulations. We apply the approach to a variety of problems, including estimation of unknown variance parameters in the Gaussian model, degree of freedom (d.o.f.) parameter estimation in the context of robust inverse problems, automatic calibration, and optimal experimental design. Using numerical examples, we demonstrate improvement in recovery of primary parameters for several large- scale inverse problems. The proposed approach is compatible with a wide variety of algorithms and formulations, and its implementation requires only minor modifications to existing algorithms.},
	keywords = {full waveform inversion, students t, variance},
	doi = {10.1088/0266-5611/28/11/115016},
	url = {http://arxiv.org/abs/1206.6532},
	url2 = {https://slim.gatech.edu/Publications/Public/Journals/InverseProblems/2012/aravkin2012IPNuisance/aravkin2012IPNuisance.pdf},
	author = {Aleksandr Y. Aravkin and Tristan van Leeuwen}
}
@presentation {aravkin2012SINBADenp,
	title = {Estimating nuisance parameters in inverse problems},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Many inverse problems include nuisance parameters which, while not of direct interest, are required to recover primary parameters. In this talk, we present the idea of "projecting out" these variables, and how this idea allows us to design methods for solving a broad class of problems with nuisance parameters, such as variance or degrees of freedom. We then discuss several geophysical applications, including including estimation of unknown variance parameters in the Gaussian model for full waveform inversion, degree of freedom (d.o.f.) parameter estimation in the context of robust imaging problems, and robust source estimation.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/aravkin2012SINBADenp/aravkin2012SINBADenp_pres.pdf},
	author = {Aleksandr Y. Aravkin}
}
@presentation {li2011SINBADels,
	title = {Efficient least-squares imaging with sparsity promotion and compressive sensing},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/li2011SINBADels/li2011SINBADels_pres.pdf},
	author = {Xiang Li}
}
@conference {herrmann2011EAGEefmsp,
	title = {Efficient least-squares migration with sparsity promotion},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2011},
	month = {05},
	abstract = {Seismic imaging relies on the collection of multi-experimental data volumes in combination with a sophisticated back-end to create high-fidelity inversion results. While significant improvements have been made in linearized inversion, the current trend of incessantly pushing for higher quality models in increasingly complicated regions reveals fundamental shortcomings in handling increasing problem sizes numerically. The so-called {\textquoteleft}{\textquoteleft}curse of dimensionality" is the main culprit because it leads to an exponential growth in the number of sources and the corresponding number of wavefield simulations required by {\textquoteleft}{\textquoteleft}wave-equation" migration. We address this issue by reducing the number of sources by a randomized dimensionality reduction technique that combines recent developments in stochastic optimization and compressive sensing. As a result, we replace the cur- rent formulations of imaging that rely on all data by a sequence of smaller imaging problems that use the output of the previous inversion as input for the next. Empirically, we find speedups of at least one order-of-magnitude when each reduced experiment is considered theoretically as a separate compressive-sensing experiment.},
	keywords = {EAGE, Imaging, Presentation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2011/herrmann11EAGEefmsp/herrmann11EAGEefmsp.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2011/herrmann11EAGEefmsp/herrmann11EAGEefmsp_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=50333},
	author = {Felix J. Herrmann and Xiang Li}
}
@article {kumar2010TNPecr,
	title = {Enhancing crustal reflection data through curvelet denoising},
	journal = {Technophysics},
	volume = {508},
	number = {1-4},
	year = {2011},
	month = {07},
	pages = {106-116},
	abstract = {Suppression of incoherent noise, which is present in the seismic signal and may often lead to ambiguous interpretation, is a key step in processing associated with crustal reflection data. In this paper, we make use of the parsimonious representation of seismic data in the curvelet domain to perform the noise attenuation while preserving the coherent energy and its amplitude information. Curvelets are a recently developed mathematical transform that has as one of its properties minimal overlap between seismic signal and noise in the transform domain, thereby facilitating signal-noise separation. The problem is cast as an inverse problem and the results are obtained by updating the solution at each iteration. We demonstrate the effectiveness of this procedure at removing noise on both synthetic shot gathers and a synthetic stacked seismic section. We then apply curvelet denoising to deep crustal seismic reflection data where the signal-to-noise ratio is low. The reflection data were recorded along Lithoprobe{\textquoteright}s SNORCLE Line 1 across Paleoproterozoic-Archean domains in Canada{\textquoteright}s Northwest Territories. After initial processing, we apply the iterative curvelet denoising to both pre-stack shot gathers and post-stack data. Ground roll, random noise and much of the anomalous vertical energy is removed from the pre-stack shot gathers, to the extent that crustal reflections, including those from the Moho, are clearly seen on individual gathers. Denoised stacked data show a series of dipping reflections in the lower crust that extend into the Moho. The Moho itself is relatively flat and characterized by a sharp, narrow band of reflections. Comparing the results for the stacked data with those from F-X deconvolution, curvelet denoising outperforms the latter by attenuating incoherent noise with minimal harm to the signal. Because curvelet denoising retains amplitude information, it provides opportunities for further studies of seismic sections through attribute analyses. Curvelet denoising provides an important new tool in the processing toolbox for crustal seismic reflection data.},
	keywords = {Processing, SLIM},
	doi = {10.1016/j.tecto.2010.07.01},
	url = {http://www.sciencedirect.com/science/article/pii/S0040195110003227},
	author = {Vishal Kumar and Jounada Oueity and Ron Clowes and Felix J. Herrmann}
}
@conference {lin2011EAGEepsic,
	title = {Estimating primaries by sparse inversion in a curvelet-like representation domain},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2011},
	month = {05},
	abstract = {We present an uplift in the fidelity and wavefront continuity of results obtained from the Estimation of Primaries by Sparse Inversion (EPSI) program by reconstructing the primary events in a hybrid wavelet-curvelet representation domain. EPSI is a multiple removal technique that belongs to the class of wavefield inversion methods, as an alternative to the traditional adaptive-subtraction process. The main assumption is that the correct primary events should be as sparsely-populated in time as possible. A convex reformulation of the original EPSI algorithm allows its convergence property to be preserved even when the solution wavefield is not formed in the physical domain. Since wavefronts and edge-type singularities are sparsely represented in the curvelet domain, sparse solutions formed in this domain will exhibit vastly improved continuity when compared to those formed in the physical domain, especially for the low-energy events at later arrival times. Further- more, a wavelet-type representation domain will preserve sparsity in the reflected events even if they originate from non-zero-order discontinuities in the subsurface, providing an additional level of robustness. This method does not require any changes in the underlying computational algorithm and does not explicitly impose continuity constraints on each update.},
	keywords = {EAGE, Presentation, Processing},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2011/lin11EAGEepsic/lin11EAGEepsic.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2011/lin11EAGEepsic/lin11EAGEepsic_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=50427},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@presentation {aravkin2011SINBADesp,
	title = {Extensions to sparsity promotion},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/aravkin2011SINBADesp/aravkin2011SINBADesp_pres.pdf},
	author = {Aleksandr Y. Aravkin}
}
@techreport {herrmann2010SEGerc,
	title = {Empirical recovery conditions for seismic sampling},
	number = {TR-EOAS-2010-2},
	year = {2010},
	institution = {Department of Earth and Ocean Sciences, UBC},
	abstract = {In this paper, we offer an alternative sampling method leveraging recent insights from compressive sensing towards seismic acquisition and processing for data that are traditionally considered to be undersampled. The main outcome of this approach is a new technology where acquisition and processing related costs are no longer determined by overly stringent sampling criteria, such as Nyquist. At the heart of our approach lies randomized incoherent sampling that breaks subsampling related interferences by turning them into harmless noise, which we subsequently remove by promoting transform-domain sparsity. Now, costs no longer grow with resolution and dimensionality of the survey area, but instead depend on transform-domain sparsity only. Our contribution is twofold. First, we demonstrate by means of carefully designed numerical experiments that compressive sensing can successfully be adapted to seismic acquisition. Second, we show that accurate recovery can be accomplished for compressively sampled data volumes sizes that exceed the size of conventional transform-domain data volumes by only a small factor. Because compressive sensing combines transformation and encoding by a single linear encoding step, this technology is directly applicable to acquisition and to dimensionality reduction during processing. In either case, sampling, storage, and processing costs scale with transform-domain sparsity.},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2010/herrmann10SEGerc/herrmann10SEGerc.pdf},
	author = {Felix J. Herrmann}
}
@conference {johnson2010EAGEeop,
	title = {Estimation of primaries via sparse inversion with reciprocity},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2010},
	abstract = {Accurate removal of surface related multiples is a key step in seismic data processing. The industry standard for removing multiples is SRME, which involves convolving the data with itself to predict the multiples, followed by an adaptive subtraction procedure to recover the primaries (Verschuur and Berkhout, 1997). Other methods involve multidimensional division of the up-going and down-going wavefields (Amundsen, 2001). However, this approach may suffer from stability problems. With the introduction of the {\textquoteleft}{\textquoteleft}estimation of primaries by sparse inversion{\textquoteright}{\textquoteright}(EPSI), van Groenestijn and Verschuur (2009) recentely reformulated SRME to jointly estimate the surface-free impulse response and the source signature directly from the data. The advantage of EPSI is that it recovers the primary response directly, and does not require a second processing step for the subtraction of estimated multiples from the original data. However, because it estimates both the primary impulse response and source signature from the data EPSI must be regularized. Motivated by recent successful application of the curvelet transform in seismic data processing (Herrmann et al., 2007), we formulate EPSI as a bi-convex optimization problem that seeks sparsity on the surface-free Green{\textquoteright}s function and Fourier-domain smoothness on the source wavelet. Our main contribution compared to previous work (Lin and Herrmann, 2009), and the contribution of that author to the proceedings of this meeting(Lin and Herrmann, 2010), is that we employ the physical principle of as source-receiver reciprocity to improve the inversion.},
	keywords = {EAGE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2010/johnson10EAGEeop/johnson10EAGEeop.pdf},
	author = {James Johnson and Tim T.Y. Lin and Felix J. Herrmann}
}
@mastersthesis {almatar10THesd,
	title = {Estimation of surface-free data by curvelet-domain matched filtering and sparse inversion},
	year = {2010},
	note = {(MSc)},
	month = {12},
	school = {The University of British Columbia},
	type = {masters},
	address = {Vancouver},
	abstract = {A recent robust multiple-elimination technique, based on the underlying principle that relates primary impulse response to total upgoing wavefield, tries to change the paradigm that sees surface-related multiples as noise that needs to be removed from the data prior to imaging. This technique, estimation of primaries by sparse inversion (EPSI), (van Groenestijn and Verschuur, 2009; Lin and Herrmann, 2009), proposes an inversion procedure during which the source function and surface- free impulse response are directly calculated from the upgoing wavefield using an alternating optimization procedure. EPSI hinges on a delicate interplay between surface-related multiples and pri- maries. Finite aperture and other imperfections may violate this relationship. In this thesis, we investigate how to make EPSI more robust by incorporating curvelet-domain matching in its formulation. Compared to surface-related multiple removal (SRME), where curvelet-domain matching was used successfully, incorporating this step has the additional advantage that matches multiples to multiples rather than predicated multiples to total data as in SRME.},
	keywords = {MSc},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2010/almatar10THesd.pdf},
	author = {Mufeed H. AlMatar}
}
@presentation {almatar2010SINBADesf,
	title = {Estimation of surface-free data by curvelet-domain matched filtering and sparse inversion},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/almatar2010SINBADesf/almatar2010SINBADesf_pres.pdf},
	author = {Mufeed H. AlMatar}
}
@techreport {almatar10SEGesfd,
	title = {Estimation of surface-free data by curvelet-domain matched filtering and sparse inversion},
	number = {TR-EOAS-2010-1},
	year = {2010},
	institution = {Department of Earth and Ocean Sciences},
	address = {University of British Columbia, Vancouver},
	abstract = {Matching seismic wavefields and images lies at the heart of many pre-post-processing steps part of seismic imaging- whether one is matching predicted wavefield components, such as multiples, to the actual to-be-separated wavefield components present in the data or whether one is aiming to restore migration amplitudes by scaling, using an image-to-remigrated- image matching procedure to calculate the scaling coefficients. The success of these wavefield matching procedures depends on our ability to (i) control possible overfitting, which may lead to accidental removal of energy or to inaccurate image-amplitude corrections, (ii) handle data or images with nonunique dips, and (iii) apply subsequent wavefield separations or migraton amplitude corrections stably. In this paper, we show that the curvelet transform allows us to address all these issues by im- posing smoothness in phase space, by using their capability to handle conflicting dips, and by leveraging their ability to represent seismic data and images sparsely. This latter property renders curvelet-domain sparsity promotion an effective prior.},
	keywords = {SEG},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2010/almatar10SEGesfd/almatar10SEGesfd.pdf},
	author = {Mufeed H. AlMatar and Tim T.Y. Lin and Felix J. Herrmann}
}
@presentation {aravkin2010SINBADesf,
	title = {Exploiting sparsity in full-waveform inversion: nonlinear basis pursuit denoise algorithm},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/aravkin2010SINBADesf/aravkin2010SINBADesf_pres.pdf},
	author = {Aleksandr Y. Aravkin}
}
@conference {vandenberg2008IAMesr,
	title = {Exact sparse reconstruction and neighbourly polytopes},
	booktitle = {IAM},
	year = {2008},
	keywords = {IAM, Presentation, private, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Private/Conferences/IAM/2008/vandenberg2008IAMesr/vandenberg2008IAMesr.pdf},
	author = {Ewout van den Berg}
}
@article {friedlander2007SJOero,
	title = {Exact regularization of convex programs},
	journal = {SIAM Journal on Optimization},
	volume = {18},
	number = {4},
	year = {2007},
	month = {05},
	pages = {1326-1350},
	abstract = {The regularization of a convex program is exact if all solutions of the regularized problem are also solutions of the original problem for all values of the regularization parameter below some positive threshold. For a general convex program, we show that the regularization is exact if and only if a certain selection problem has a Lagrange multiplier. Moreover, the regularization parameter threshold is inversely related to the Lagrange multiplier. We use this result to generalize an exact regularization result of Ferris and Mangasarian [Appl. Math. Optim., 23(1991), pp. 266{\textendash}273] involving a linearized selection problem. We also use it to derive necessary and sufficient conditions for exact penalization, similar to those obtained by Bertsekas [Math. Programming, 9(1975), pp. 87{\textendash}99] and by Bertsekas, Nedi , Ozdaglar [Convex Analysis and Optimization, Athena Scientific, Belmont, MA, 2003]. When the regularization is not exact, we derive error bounds on the distance from the regularized solution to the original solution set. We also show that existence of a {\textquoteleft}{\textquoteleft}weak sharp minimum{\textquoteright}{\textquoteright} is in some sense close to being necessary for exact regularization. We illustrate the main result with numerical experiments on the l1 regularization of benchmark (degenerate) linear programs and semidefinite/second-order cone programs. The experiments demonstrate the usefulness of l1 regularization in finding sparse solutions.},
	keywords = {Optimization, SLIM},
	doi = {10.1137/060675320},
	author = {Michael P. Friedlander and P. Tseng}
}
