@article {yang2020lrpo,
	title = {Low-rank representation of omnidirectional subsurface extended image volumes},
	journal = {Geophysics},
	volume = {86},
	number = {3},
	year = {2021},
	note = {(Geophysics)},
	month = {1},
	pages = {1MJ-WA152},
	abstract = {Subsurface-offset gathers play an increasingly important role in seismic imaging. These gathers are used during velocity model building and inversion of rock properties from amplitude variations. While powerful, these gathers come with high computational and storage demands to form and manipulate these high dimensional objects. This explains why only limited numbers of image gathers are computed over a limited offset range. We avoid these high costs by working with highly compressed low-rank factorizations. We arrive at these factorizations via a combination of probings with the double two-way wave equation and randomized singular value decompositions. In turn, the resulting factorizations give us access to all subsurface offsets without having to form the full extended image volumes. The latter is computationally prohibitive because extended image volumes are quadratic in image size. As a result, we can easily handle situations where conventional horizontal offset gathers are no longer focused. More importantly, the factorization also provides a mechanism to use the invariance relation of extended image volumes for velocity continuation. With this technique, extended image volumes for one background velocity model can directly be mapped to those of another background velocity model. Our low-rank factorization inherits this invariance property so we incur factorization costs only once when examining different imaging scenarios. Because all imaging experiments only involve the factors, they are computationally cheap with costs that scale with the rank of the factorization. We validate our methodology on 2D synthetics including a challenging imaging example with salt. Our experiments show that our low-rank factorization parameterizes extended image volumes naturally. Instead of brute force explicit cross-correlations between shifted source and receiver wavefields, our approach relies on the underlying linear-algebra structure that enables us to work with these objects without incurring unfeasible demands on computation and storage.},
	keywords = {extended image volumes, invariance relationship, low rank, power schemes, randomized linear algebra},
	doi = {10.1190/geo2020-0152.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2021/yang2020lrpo/Paper_final.html},
	author = {Mengmeng Yang and Marie Graff and Rajiv Kumar and Felix J. Herrmann}
}
@mastersthesis {yang2020THsiw,
	title = {Seismic imaging with extended image volumes and source estimation},
	year = {2020},
	note = {(PhD)},
	month = {03},
	school = {Georgia Institute of Technology},
	type = {phd},
	address = {Atlanta},
	abstract = {Seismic imaging is an important tool for the exploration and production of oil \& gas, carbon sequestration, and the mitigation of geohazards. Through the process of seismic migration, images of subsurface geological structures are created from data collected at the surface. These images reflect changes in the physical rock properties such as wave speed and density. While significant progress has been made in the development of 3D imaging technology for complex geological areas, several challenges remain, some of which are addressed in this thesis. The first main contribution of this thesis is in the area of creating so-called subsurface-offset gathers, which play an increasingly important role in seismic imaging because they provide a multitude of information ranging from the reflection mechanism itself to information of the dips of specific reflectors and the accuracy of the background velocity model. Unfortunately, the formation and manipulation of these gathers come with exceedingly high computational and storage costs because extended image volumes are quadratic in the image size. These high costs are avoided by using techniques from modern randomized linear algebra that allow for compression of extended image volumes into low-rank factorized form{\textemdash}i.e., the image volume is approximately written as an outer product of a tall and a wide matrix. It is demonstrated that this factorization provides access to different types of sub-surface offset gathers, including common-image (point) gathers, without the need to explicitly form this outer product. As a result, challenging steep dip imaging situations, where conventional horizontal offset gathers no longer focus, can be handled. Moreover, extended image volumes for one background velocity model can directly be mapped to those of another background velocity model. As a result, factorization costs are incurred only once when examining imaging scenarios for different background velocity models. The second main contribution of this thesis is on the development of computationally efficient sparsity-promoting imaging techniques and on-the-fly source estimation. In this work, an adaptive technique is proposed where the unknown time signature of the sources is estimated during imaging. Without accurate knowledge of these source signatures, seismic images can be wrongly positioned and can have the wrong amplitudes hampering subsequent geophysical and geological interpretations. With the presented technique, this problem is mitigated. Finally, a contribution is made to address the detrimental effects of surface-related multiples. If not handled correctly, these multiples give rise to unwanted artifacts in the image. A new technique is introduced to address this issue in realistic settings where there is a strong density contrast at the ocean bottom. As a result, the surface-related multiples are mapped to the reflectors. Because bounce points at the surface can be considered as sources, this mapping of the multiples rather than removal increases the subsurface illumination.},
	keywords = {extended image volumes, low rank, multiples, PhD, randomized linear algebra, source estimation, sparsity-promoting inversion},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2020/yang2020THsiw/yang2020THsiw.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Thesis/2020/yang2020THsiw/yang2020THsiw_pres.pdf},
	author = {Mengmeng Yang}
}
@article {yang2020tdsp,
	title = {Time-domain sparsity promoting least-squares reverse time migration with source estimation},
	journal = {Geophysical Prospecting},
	volume = {68},
	number = {9},
	year = {2020},
	note = {(Geophysical Prospecting)},
	month = {08},
	pages = {2697-2711},
	abstract = {Least-squares reverse time migration is well-known for its capability to generate artifact-free true-amplitude subsurface images through fitting observed data in the least-squares sense. However, when applied to realistic imaging problems, this approach is faced with issues related to overfitting and excessive computational costs induced by many wave-equation solves. The fact that the source function is unknown complicates this situation even further. Motivated by recent results in stochastic optimization and transform-domain sparsity-promotion, we demonstrate that the computational costs of inversion can be reduced significantly while avoiding imaging artifacts and restoring amplitudes. While powerfull, these new approaches do require accurate information on the source-time function, which is often lacking. Without this information, the imaging quality deteriorates rapidly. We address this issue by presenting an approach where the source-time function is estimated on the fly through a technique known as variable projection. Aside from introducing negligible computational overhead, the proposed method is shown to perform well on imaging problems with noisy data and problems that involve complex settings such as salt. In either case, the presented method produces high resolution high-amplitude fidelity images including an estimates for the source-time function. In addition, due to its use of stochastic optimization, we arrive at these images at roughly one to two times the cost of conventional reverse time migration involving all data.},
	keywords = {penalty, source estimation, sparsity inversion},
	doi = {10.1111/1365-2478.13021},
	url = {https://slim.gatech.edu/Publications/Public/Journals/GeophysicalProspecting/2020/yang2020tdsp/yang2020tdsp.html},
	author = {Mengmeng Yang and Zhilong Fang and Philipp A. Witte and Felix J. Herrmann}
}
@conference {yang2019SEGlrr,
	title = {Low-rank representation of subsurface extended image volumes with power iterations},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2019},
	note = {(SEG, San Antonio)},
	month = {09},
	pages = {4470-4474},
	abstract = {Full subsurface extended image volumes (EIVs) contain abundant information such as subsurface image gathers used in imaging, interpretation of rock properties or velocity analysis, but are expensive in term of computational cost and storage requirement due to their large size. Yet, due to their redundant information, monochromatic EIVs exhibit a low-rank structure that allows us to get a good approximation at a computational cost proportional to the rank k , while conventional techniques require at least the number of sources. Such low-rank factorized EIVs are computed thanks to the randomized singular values decomposition (SVD) algorithm. However, recent developments on low-rank approximations of EIVs raise two major questions. First, monochromatic EIVs rely on time-harmonic wave equation solvers, which do not scale well to realistic 3D models. Second, the rank of the monochromatic EIVs increases with the frequency, which yields increasing computational costs and storage. Here, we propose a construction approach based on a time-domain finite-difference wave-equation solver with time stepping, which is combined to power iteration schemes in the randomized SVD algorithm to accelerate the decay of the singular values. We compare the performances of simultaneous iterations, block-Krylov iterations and the original randomized SVD on the computation of the EIV for a small section of the Marmousi model. Then, we show further results on the full Marmousi model to validate our approach.},
	keywords = {full extended image volumes, power iterations, randomized SVD, SEG, time domain},
	doi = {10.1190/segam2019-3215961.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/yang2019SEGlrr/yang2019SEGlrr.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/yang2019SEGlrr/yang2019SEGlrr_pres.pdf},
	author = {Mengmeng Yang and Marie Graff and Rajiv Kumar and Felix J. Herrmann}
}
@conference {yang2018SEGrde,
	title = {Removing density effects in LS-RTM with a low-rank matched filter},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2018},
	note = {(SEG, Anaheim)},
	month = {10},
	pages = {4251-4255},
	abstract = {Least-squares reverse-time migration faces difficulties when it inverts the data containing strong components related to density variation with velocity-only Born modeling operator. The strong density perturbation will be inverted as strong dummy velocity perturbations, which influence the amplitudes and phase of the velocity perturbations in the inverted model. The traditional method is to invert the additional density variations by developing Born operator with respect to both density and velocity or modify the image condition. In this work, we develop a matched-filter based LS-RTM for velocity-only Born modeling operator, which removes the artifacts in the imaging created by the strong density variation. This method doesn{\textquoteright}t call for extra work of finite difference stencil and is more general. In the experiment part, we use a complex discontinuous layered medium with strong density variations at the ocean bottom, and show the efficacy of the propose formulation.},
	keywords = {density effect, Least square, low rank, SEG},
	doi = {10.1190/segam2018-2997814.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/yang2018SEGrde/yang2018SEGrde.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/yang2018SEGrde/yang2018SEGrde_pres.pdf},
	author = {Mengmeng Yang and Rajiv Kumar and Rongrong Wang and Felix J. Herrmann}
}
@techreport {witte2017TRcls,
	title = {Compressive least-squares migration with source estimation},
	number = {TR-EOAS-2017-3},
	year = {2017},
	institution = {UBC},
	abstract = {Least-squares reverse-time migration is a powerful approach for true-amplitude seismic imaging of complex geological structures. The successful application of this method is hindered by its exceedingly large computational cost and required prior knowledge of the generally unknown source wavelet. We address these problems by introducing an algorithm for low-cost sparsity-promoting least-squares migration with source estimation. We adapt a recent algorithm from sparse optimization, which allows to work with randomized subsets of shots during each iteration of least-squares migration, while still converging to an artifact-free solution. We modify the algorithm to incorporate on-the-fly source estimation through variable projection, which lets us estimate the wavelet without additional PDE solves. The algorithm is easy to implement and allows imaging at a fraction of the cost of conventional least squares reverse-time migration, requiring only around two passes trough the data, making the method feasible for large-scale imaging problems with unknown source wavelets.},
	keywords = {LSRTM, migration, source estimation, sparsity, time domain},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2017/witte2017TRcls/witte2017TRcls.html},
	author = {Philipp A. Witte and Mengmeng Yang and Felix J. Herrmann}
}
@conference {yang2017SEGfsp,
	title = {Fast sparsity-promoting least-squares migration with multiples in time domain},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2017},
	note = {(SEG, Houston)},
	month = {09},
	pages = {4828-4832},
	abstract = {Based on the latest developments of research in inversion technology with optimization, researchers have made significant progress in the implementation of least-squares reverse-time migration (LS-RTM) of primaries. In Marine data however, these applications rely on the success of a pre-imaging separation of primaries and multiples, which can be modeled as a multi-dimensional convolution between the vertical derivative of the surface-free Green{\textquoteright}s function and the down-going receiver wavefield. Instead of imaging the primaries and multiples separately, we implement the LS-RTM of the total down-going wavefield by combining areal source injection and linearized Born modelling, where strong surface related multiples are generated from a strong density variation at the ocean bottom. The advantage including surface related multiples in LS-RTM is the extra illumination we obtain from these multiples without incurring additional computational costs related to carrying out multi-dimensional convolutions part of conventional multiple prediction procedures. Even though we are able to avert these computational costs, our approach shares the large costs of LSRTM. We reduce these costs by combining randomized source subsampling with our sparsity-promoting imaging technology, which produces artifact-free, high-resolution images, with the surface-related multiples migrated properly.},
	keywords = {least-squares RTM, multiples, SEG, sparsity, time domain},
	doi = {10.1190/segam2017-17741608.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/yang2017SEGfsp/yang2017SEGfsp.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/yang2017SEGfsp/yang2017SEGfsp_pres.pdf},
	author = {Mengmeng Yang and Emmanouil Daskalakis and Felix J. Herrmann}
}
@presentation {yang2017SINBADFiwm,
	title = {Imaging with multiples in shallow water},
	year = {2017},
	publisher = {SINBAD},
	abstract = {Based on the latest developments of research in inversion technology with optimization, researchers have made significant progress in the implementation of least-squares reverse-time migration (LS-RTM) of primaries. In Marine data however, these applications rely on the success of a pre-imaging separation of primaries and multiples, which can be modeled as a multi-dimensional convolution between the vertical derivative of the surface-free Green{\textquoteright}s function and the down-going receiver wavefield. Instead of imaging the primaries and multiples separately, we implement the LS-RTM of the total down-going wavefield by combining areal source injection and linearized Born modelling, where strong surface related multiples are generated from a strong density variation at the ocean bottom. The advantage including surface related multiples in LS-RTM is the extra illumination we obtain from these multiples without incurring additional computational costs related to carrying out multi-dimensional convolutions part of conventional multiple prediction procedures. Even though we are able to avert these computational costs, our approach shares the large costs of LS-RTM. We reduce these costs by combining randomized source subsampling with our sparsity-promoting imaging technology, which produces artifact-free, high-resolution images, with the surface-related multiples migrated properly.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/yang2017SINBADFiwm/yang2017SINBADFiwm.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/yang2017SINBADFiwm/yang2017SINBADFiwm.mov},
	author = {Mengmeng Yang and Emmanouil Daskalakis and Felix J. Herrmann}
}
@conference {witte2017EAGEspl,
	title = {Sparsity-promoting least-squares migration with the linearized inverse scattering imaging condition},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2017},
	note = {(EAGE, Paris)},
	month = {06},
	abstract = {Reverse-time migration (RTM) with the conventional cross-correlation imaging condition suffers from low-frequency artifacts that result from backscattered energy in the background velocity models. This problem translates to least-squares reverse-time migration (LS-RTM), where these artifacts slow down the convergence, as many of the initial iterations are spent on removing them. In RTM, this problem has been successfully addressed by the introduction of the so-called inverse scattering imaging condition, which naturally removes these artifacts. In this work, we derive the corresponding linearized forward operator of the inverse scattering imaging operator and incorporate this forward/adjoint operator pair into a sparsity-promoting (SPLS-RTM) workflow. We demonstrate on a challenging salt model, that LS-RTM with the inverse scattering imaging condition is far less prone to low-frequency artifacts than the conventional cross-correlation imaging condition, improves the convergence and does not require any type of additional image filters within the inversion. Through source subsampling and sparsity promotion, we reduce the computational cost in terms of PDE solves to a number comparable to conventional RTM, making our workflow applicable to large-scale problems.},
	keywords = {EAGE, imaging condition, least-squares migration, linearized Bregman},
	doi = {10.3997/2214-4609.201701125},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2017/witte2017EAGEspl/witte2017EAGEspl.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2017/witte2017EAGEspl/witte2017EAGEspl_poster.pdf},
	author = {Philipp A. Witte and Mengmeng Yang and Felix J. Herrmann}
}
@presentation {daskalakis2017SINBADFsof,
	title = {Stochastic Optimization from the perspective of dynamical systems},
	year = {2017},
	publisher = {SINBAD},
	abstract = {We present improvements to a family of methods (Linearized Bregman, Kaczmarz and Stochastic Gradient Descent) that are often use in optimization problems. We explain the link of those methods with dynamical systems and we draw ideas for improving their performance. We use a simple idea to improve the stability and the performance of our family of optimization methods especially for ill-posed, inconsistent large-scale problems. Finally we present an application at a least squares migration problem, which highlight the importance of the suggested improvements on large scale Geophysical problems.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/daskalakis2017SINBADFsof/daskalakis2017SINBADFsof.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/daskalakis2017SINBADFsof/daskalakis2017SINBADFsof.m4v},
	author = {Emmanouil Daskalakis and Rachel Kuske and Mengmeng Yang and Felix J. Herrmann}
}
@conference {yang2017SEGWStds,
	title = {Time domain sparsity promoting LSRTM with surface-related multiples in shallow-water case},
	booktitle = {SEG Workshop on Multiples: Status, Progress, Challenges and Open issues; Houston},
	year = {2017},
	note = {(SEG Workshop, Houston)},
	month = {09},
	abstract = {In the SRME relation, multiples are expressed as the multi- dimensional convolution between the the vertical derivative of the surface-free Green{\textquoteright}s function and the down-going receiver wavefield. This relation leads to the methods that separate the surface-related multiples and primaries. Instead of imaging separated multiples trivially, we introduced the SRME relation into wave equation by areal source injection, which costs nothing extra to involve multiples into the forward wavefields. The related image contains the phantoms components from cross-correlation between different-orders of events. Especially in shallow water, due to the strong magnitudes of multiples and their overlap with primaries, the artifacts contain strong phantoms not only of ocean bottom but also subsurface layer interfaces. We use sparsity-promoting inversions where the nicely curved structure of the subsurface models are detected to help cleaning up the phantoms. We reduce the costs by combin- ing randomized source subsampling with linearized Bregman method and get cleaned image with one data pass.},
	keywords = {multiple, SEG, workshop},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/yang2017SEGWStds/yang2017SEGWStds.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/yang2017SEGWStds/yang2017SEGWStds_post.pdf},
	author = {Mengmeng Yang and Felix J. Herrmann}
}
@conference {chai2016SEGlbm,
	title = {A linearized Bregman method for compressive waveform inversion},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2016},
	note = {(SEG, Dallas)},
	month = {10},
	pages = {1449-1454},
	abstract = {We present an implementation of a recently developed and relatively simple linearized Bregman method to solve the large-scale $\ell_1$-norm sparsity-promoting Gauss-Newton (GN) full-waveform inversion (FWI) problem. Numerical experiments demonstrate that: the much simpler linearized Bregman method does a comparable and even superior job compared to a state-of-the-art $\ell_1$-norm solver SPGL1, which is previously used in the modified Gauss-Newton FWI; the linearized Bregman method is also more efficient (faster) than SPGL1; the FWI result with the linearized Bregman method solving $\ell_1$-norm sparsity-promoting problems to get the model updates is better than that obtained by solving $\ell_2$-norm least-squares problems to get the model updates.},
	keywords = {Bregman method, SEG, waveform inversion},
	doi = {10.1190/segam2016-13848105.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/chai2016SEGlbm/chai2016SEGlbm.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/chai2016SEGlbm/chai2016SEGlbm_pres.pdf},
	author = {Xintao Chai and Mengmeng Yang and Philipp A. Witte and Rongrong Wang and Zhilong Fang and Felix J. Herrmann}
}
@presentation {yang2016SINBADFtds,
	title = {Time-domain sparsity-promoting least-squares migration with source estimation},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Compared to traditional reverse-time migration (RTM), least-squares RTM (LS_RTM) is able to obtain true amplitude images as solutions of $\ell_2$-norm minimization problems by fitting the synthetic and observed reflection data. The shortcoming is that solutions of these $\ell_2$ problems tend to be overfitted and computationally too expensive. By working with randomized subsets of data only, the computational costs of LS-RTM can be brought down to an acceptable level, producing artifact-free high-resolution images. By including on-the-fly source-time function estimation into the method of Linearized Bregman (LB), we tackle the open issues of these "compressive imaging" in the aspects of algorithmic complexity of solver, guaranteed convergence and source estimation. We also inbvestigate whether the algorithm can be accelerated when we combine LB with the Nesterov method. Application of our algorithm on a 2D synthetic shows that we are able to get high-resolution images, with accurate estimates of the wavelet, for one single data pass.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/yang2016SINBADFtds/yang2016SINBADFtds.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/yang2016SINBADFtds/yang2016SINBADFtds.m4v},
	author = {Mengmeng Yang and Philipp A. Witte and Zhilong Fang and Felix J. Herrmann}
}
@conference {yang2016SEGtds,
	title = {Time-domain sparsity-promoting least-squares migration with source estimation},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2016},
	note = {(SEG, Dallas)},
	month = {10},
	pages = {4225-4229},
	abstract = {Traditional reverse-time migration (RTM) gives images with wrong amplitudes and low resolution. Least-squares RTM (LS-RTM) on the other hand, is capable of obtaining true-amplitude images as solutions of $\ell_2$-norm norm minimization problems by fitting the synthetic and observed reflection data. The shortcoming of this approach is that solutions of these $\ell_2$ problems are typically smoothed, tend to be overfitted, and computationally too expensive because it requires compared to standard RTM too many iterations. By working with randomized subsets of data only, the computational costs of LS-RTM can be brought down to an acceptable level while producing artifact-free high-resolution images without overfitting the data. While initial results of these "compressive imaging" methods were encouraging various open issues remain including guaranteed convergence, algorithmic complexity of the solver, and lack of on-the-fly source estimation for LS-RTMs with wave-equation solvers based on time-stepping. By including on-the-fly source-time function estimation into the method of Linearized Bregman (LB), on which we reported before, we tackle all these issues resulting in a easy-to-implement algorithm that offers flexibility in the trade-off between the number of iterations and the number of wave-equation solves per iteration for a fixed total number of wave-equation solves. Application of our algorithm on a 2D synthetic shows that we are able to obtain high-resolution images, including accurate estimates of the wavelet, for a single pass through the data. The produced image, which is by virtue of the inversion deconvolved with respect to the wavelet, is roughly of the same quality as the image obtained given the correct source function.},
	keywords = {least squares, RTM, SEG, source estimation, time domain},
	doi = {10.1190/segam2016-13850609.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/yang2016SEGtds/yang2016SEGtds.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/yang2016SEGtds/yang2016SEGtds_pres.pdf},
	author = {Mengmeng Yang and Philipp A. Witte and Zhilong Fang and Felix J. Herrmann}
}
@presentation {yang2015SINBADFfis,
	title = {Fast imaging with surface-related multiples: {\textendash} shallow water multiples},
	year = {2015},
	publisher = {SINBAD},
	abstract = {In this talk, we will show surface-related multiples imaging with source estimation. Here the algorithms are based on linearized bregman, and we give the source estimation formulation in this case. With the featured blocked structure of linearized bregman, we can do efficient imaging in straightforward way by randomly choosing sources and frequencies. Another benefit is the simplicity to delivery the algorithm.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/yang2015SINBADFfis/yang2015SINBADFfis.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/yang2015SINBADFfis/yang2015SINBADFfis.mov},
	author = {Mengmeng Yang and Ning Tu and Felix J. Herrmann}
}
@conference {witte2015IIPFWIspl,
	title = {Sparsity-promoting least-square migration with linearized Bregman and compressive sensing},
	booktitle = {Inaugural Full-Waveform Inversion Workshop},
	year = {2015},
	note = {(Natal, Brazil)},
	month = {08-09},
	abstract = {We present a novel adaptation of a recently developed relatively simple iterative algorithm to solve large-scale sparsity-promoting optimization problems. Our algorithm is particularly suitable to large-scale geophysical inversion problems, such as sparse least-squares reverse-time migration or Kirchoff migration since it allows for a tradeoff between parallel computations, memory allocation, and turnaround times, by working on subsets of the data with different sizes. Comparison of the proposed method for sparse least-squares imaging shows a performance that rivals and even exceeds the performance of state-of-the art one-norm solvers that are able to carry out least-squares migration at the cost of a single migration with all data.},
	keywords = {Bregman, compressed sensing, FWI, migration, sparsity promotion, time domain},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/IIPFWI/witte2015IIPFWIspl/witte2015IIPFWIspl_pres.pdf},
	author = {Philipp A. Witte and Ning Tu and Ernie Esser and Mengmeng Yang and Mathias Louboutin and Felix J. Herrmann}
}
