@unpublished {bhar2026IMAGEolr,
	title = {OpenSeisML: Open Large-Scale Real Seismic and well-log Dataset for Generative AI},
	year = {2026},
	month = {3},
	abstract = {The advent of machine learning (ML) and computer vision has significantly accelerated seismic inversion workflows by reducing the computational cost of traditionally expensive iterative methods. However, the development and evaluation of ML methods remains limited by the scarcity of realistic velocity models, as most high-quality data are privately owned by oil and gas companies. To address this gap, we present *OpenSeisML*, a collection of real seismic datasets designed to support generative AI (Gen-AI) workflows for seismic inversion. The datasets are curated from publicly available surveys in the UK National Data Repository (NDR). When seismic volumes are in the time domain and wells are in depth, a time-to-depth conversion is required. We use checkshot data to establish the time{\textendash}depth relationship and construct a velocity model through interpolation for accurate conversion of post-stack seismic data. Here, we present an automated data curation pipeline that enables seismic data preparation while ensuring reproducibility. The objective is to train a generative model that captures the statistical distribution of subsurface properties, enabling the synthesis of multiple statistically consistent realizations for uncertainty quantification which can act as a prior for seismic inversion.},
	keywords = {deep learning, generative model, IMAGE, Imaging, Inverse problems, real seismic data, SEG, time-to-depth conversion, well logs, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Submitted/2026/bhar2026IMAGEolr/abstract.html},
	url2 = {https://slim.gatech.edu/Publications/Public/Submitted/2026/2026IMAGEolr/IMAGE_Ipsita.pdf},
	author = {Ipsita Bhar and Huseyin Tuna Erdinc and Thales Souza and Charles Jones and Felix J. Herrmann}
}
@conference {gahlot2025CCUSocs,
	title = {Optimizing CO2 Storage Monitoring with Enhanced Rock Physics Modeling and Optimal Pressure Control},
	booktitle = {CCUS 2025 - Carbon Capture, Utilization, and Storage Conference},
	year = {2025},
	month = {3},
	abstract = {Based on the latest data-assimilation and machine-learning techniques, Digital Twins (DTs) have shown promise for high-fidelity monitoring and control of underground CO2​ storage. While the use of these techniques have important advantages, they do rely on certain assumptions. If these assumptions are not met, the DT{\textquoteright}s neural networks may no longer infer the state of the CO2 plume (pressure/saturation) accurately. By augmenting the forecast ensemble, we address this issue.},
	keywords = {Bayesian inference, CCUS, conditional normalizing flows, control, deep learning, digital twin, GCS, rock physics, sequential Bayes, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CCUS/2025/gahlot2025CCUSocs},
	author = {Abhinav Prakash Gahlot and Haoyun Li and Felix J. Herrmann}
}
@presentation {gahlot2024ML4SEISMICosm,
	title = {Optimizing CO2 Storage Monitoring with Enhanced Rock Physics Modeling},
	year = {2024},
	month = {11},
	abstract = {Based on the latest data-assimilation and machine-learning techniques, Digital Twins (DTs) have shown promise for high-fidelity monitoring and control of underground CO2​ storage. While the use of these techniques have important advantages, they do rely on certain assumptions. If these assumptions are not met, the DT{\textquoteright}s neural networks may no longer infer the state of the CO2 plume (pressure/saturation) accurately. By augmenting the forecast ensemble, we address this issue.},
	keywords = {Amortized Variational Inference, augmentation, Bayesian inference, conditional normalizing flows, data assimilation, deep learning, FWI, Imaging, Inverse problems, ML4SEISMIC, rock physics, RTM, SLIM, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/gahlot2024ML4SEISMICosm},
	author = {Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@article {zhang2023otl,
	title = {Optimized time-lapse acquisition design via spectral gap ratio minimization},
	journal = {Geophysics},
	volume = {88},
	number = {4},
	year = {2023},
	note = {(Geophysics)},
	month = {07},
	pages = {A19-A23},
	abstract = {Modern-day reservoir management and monitoring of geological carbon storage increasingly call for costly time-lapse seismic data collection. In this letter, we show how techniques from graph theory can be used to optimize acquisition geometries for low-cost sparse 4D seismic. Based on midpoint-offset domain connectivity arguments, the proposed algorithm automatically produces sparse non-replicated time-lapse acquisition geometries that favor wavefield recovery.},
	keywords = {Acquisition, JRM, matrix factorization, spectral gap, survey design, time-lapse, wavefield reconstruction},
	doi = {10.1190/geo2023-0024.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2023/zhang2023otl/Spectral_Gap_Paper.html},
	author = {Yijun Zhang and Ziyi Yin and Oscar Lopez and Ali Siahkoohi and Mathias Louboutin and Rajiv Kumar and Felix J. Herrmann}
}
@conference {lange2017SCIPYofd,
	title = {Optimised finite difference computation from symbolic equations},
	booktitle = {Python in Science Conference Proceedings},
	year = {2017},
	note = {(SciPy, Texas)},
	month = {07},
	pages = {89-96},
	abstract = {Domain-specific high-productivity environments are playing an increasingly important role in scientific computing due to the levels of abstraction and automation they provide. In this paper we introduce Devito, an open-source domain-specific framework for solving partial differential equations from symbolic problem definitions by the finite difference method. We highlight the generation and automated execution of highly optimized stencil code from only a few lines of high-level symbolic Python for a set of scientific equations, before exploring the use of Devito operators in seismic inversion problems.},
	keywords = {domain-specific languages, finite difference, symbolic Python},
	url = {http://conference.scipy.org/proceedings/scipy2017/michael_lange.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SciPy/2017/lange2017SCIPYofd/lange2017SCIPYofd_pres.pdf},
	author = {Michael Lange and Navjot Kukreja and Fabio Luporini and Mathias Louboutin and Charles Yount and Jan H{\"u}ckelheim and Gerard Gorman}
}
@techreport {louboutin2016OGHPCocp,
	title = {Optimizing the computational performance of time-domain modelling{\textendash}-leveraging multiple right-hand-sides},
	number = {TR-EOAS-2017-2},
	year = {2017},
	institution = {UBC},
	abstract = {Exploration geophysics heavily relies upon fast solvers for the wave-equation and its adjoint. The main computational cost of a wave-equation solver is to compute the Laplacian, or more complex finite-difference operators, at every time step. The performance of many discretizations is limited by the relatively low operational intensity (number of floating point operations divided by memory traffic) of the finite-difference stencil. Solving the wave-equation for multiple sources/right-hand-sides (RHSs) at once mitigates this problem by increasing the operational intensity. This is implemented by rewriting the classical matrix-vector product into a matrix-matrix product where each column of the second matrix represent the solution wavefield for each given source. This minor modification to the solver is shown to achieve a 2-4 times speedup compared to a single source solver. We concentrate in this paper on acoustic modelling, but our approach can easily be extended to anisotropic or elastic cases for both forward and adjoint modelling.},
	keywords = {finite differences, HPC, Modelling, time domain},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2017/louboutin2016OGHPCocp/louboutin2016OGHPCocp.pdf},
	author = {Mathias Louboutin and Gerard Gorman and Felix J. Herrmann}
}
@presentation {herrmann2017SINBADFofp,
	title = {Overview \& Future Plans SINBAD Consortium},
	year = {2017},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/herrmann2017SINBADFofp/herrmann2017SINBADFofp.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/herrmann2017SINBADFofp/herrmann2017SINBADFofp.mov},
	author = {Felix J. Herrmann}
}
@article {lopez2015IEEEogl,
	title = {Off-the-grid low-rank matrix recovery and seismic data reconstruction},
	journal = {IEEE Journal of Selected Topics in Signal Processing},
	volume = {10},
	number = {4},
	year = {2016},
	note = {(IEEE Journal of Selected Topics in Signal Processing)},
	month = {06},
	pages = {658-671},
	abstract = {Matrix sensing problems capitalize on the knowledge that a data matrix of interest exhibits low rank properties. This low dimensional structure often arises because the data matrix is obtained by sampling a smooth function on a regular (or structured) grid. However, in many practical situations the measurements are taken on an irregular grid (that is accurately known). This results in an "unstructured data matrix" that is less fit for the low rank model in comparison to its regular counterpart and therefore subject to degraded reconstruction via rank penalization techniques. In this paper, we propose and analyze a modified low-rank matrix recovery work-flow that admits unstructured observations. By incorporating a regularization operator which accurately maps structured data to unstructured data, into the nuclear-norm minimization problem, we are able to compensate for data irregularity. Furthermore, by construction our formulation yields output that is supported on a structured grid. We establish recovery error bounds for our methodology and offer matrix sensing and matrix completion numerical experiments including applications to seismic trace interpolation to demonstrate the potential of the approach.},
	keywords = {data regularization, matrix completion, matrix sensing, nonuniform discrete Fourier transform, nuclear-norm relaxation, seismic data, seismic trace interpolation},
	doi = {10.1109/JSTSP.2016.2555482},
	url = {https://slim.gatech.edu/Publications/Public/Journals/IEEESignalProcessingMagazine/2016/lopez2015IEEEogl/lopez2015IEEEogl.pdf},
	url2 = {http://ieeexplore.ieee.org/xpl/login.jsp?tp=\&arnumber=7454674},
	author = {Oscar Lopez and Rajiv Kumar and Ozgur Yilmaz and Felix J. Herrmann}
}
@conference {lopez2016CMSogl,
	title = {Off-the-grid low-rank matrix recovery: seismic data reconstruction},
	booktitle = {Canadian Mathematical Society Summer Meeting},
	year = {2016},
	note = {(CMS, Edmonton, Alberta)},
	month = {06},
	abstract = {This talk discusses a modified low-rank matrix recovery work-flow that admits unstructured observations. By incorporating a regularization operator which accurately maps structured data to unstructured data, into the nuclear-norm minimization problem, this approach is able to compensate for data irregularity. Furthermore, by construction this formulation yields output that is supported on a structured grid. Recovery error bounds are established for the methodology with matrix sensing and matrix completion numerical experiments including applications to seismic trace interpolation to demonstrate the potential of the approach.},
	keywords = {CMS, matrix completion, matrix sensing, nonuniform discrete Fourier transform, nuclear-norm, Seismic data Interpolation},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CMS/2016/lopez2016CMSogl/lopez2016CMSogl_pres.pdf},
	author = {Oscar Lopez and Rajiv Kumar and Ozgur Yilmaz and Felix J. Herrmann}
}
@presentation {gorman2016SINBADFopp,
	title = {Open Performance portablE SeismiC Imaging{\textendash}-OPESCI},
	year = {2016},
	publisher = {SINBAD},
	abstract = {In this project, we introduce OPESCI-FD, a Python package built on symbolic mathematics to automatically generate Finite Difference models from a high-level description of the model equations. We investigate applying this framework to generate the propagator program used in seismic imaging. We implement the 3D acoustic and anisotropic FD scheme as an example and demonstrate the advantages of usability, flexibility and accuracy of the framework. The design of OPESCI-FD aims to allow rapid development, analysis and optimisation of Finite Difference programs. OPESCI-FD is the foundation for continuing development by the OPESCI project team, building on the research presented in this report. This talk concludes by reviewing the further developments that are already under way, as well as the scope for extension to cater for other equations and numerical schemes. This joint work with SINBAD and SENAI CIMATEC and has received additional funding from Intel.},
	keywords = {Presentation, private, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/gorman2016SINBADFopp/gorman2016SINBADFopp.pdf},
	url2 = {https://slim.gatech.edu/Publications/Private/Conferences/SINBAD/2016/Fall/gorman2016SINBADFopp/gorman2016SINBADFopp.mov},
	author = {Gerard Gorman and Marcos de Aguiar and David Ham and Felix J. Herrmann and Paul H. J. Kelly and Navjot Kukreja and Michael Lange and Mathias Louboutin and Fabio Luporini and Paulius Velesko and Vincenzo Pandolfo and Felippe Vieira Zacarias}
}
@techreport {louboutin2016SEGocp,
	title = {Optimizing the computational performance and maintainability of time-domain modelling{\textendash}-leveraging multiple right-hand-sides},
	number = {TR-EOAS-2016-2},
	year = {2016},
	month = {06},
	institution = {UBC},
	abstract = {Numerical solvers for the wave equation are a key component of Full-Waveform Inversion (FWI) and Reverse-Time Migration (RTM). The main computational cost of a wave-equation solver stems from the computation of the Laplacian at each time step. When using a finite difference discretization this can be characterized as a structured grid computation within Colella{\textquoteright}s Seven Dwarfs. Independent of the degree of parallelization the performance will be limited by the relatively low operational intensity (number of operations divided by memory traffic) of finite-difference stencils, that is so say that the method is memory bandwidth bound. For this reason many developers have focused on porting their code to platforms that have higher memory bandwidth, such as GPU{\textquoteright}s, or put significant effort into highly intrusive optimisations. However, these optimisations rarely strike the right performance vs productivity balance as the software becomes less maintainable and extensible. By solving the wave equation for multiple sources/right-hand-sides (RHSs) at once, we overcome this problem arriving at a time-stepping solver with higher operational intensity. In essence, we arrive at this result by turning the usual matrix-vector products into a matrix-matrix products where the first matrix implements the discretized wave equation and each column of the second matrix contain separate wavefields for each given source. By making this relatively minor change to the solver we readily achieved a $\times2$ speedup. While we limit ourselves to acoustic modeling, our approach can easily be extended to the anisotropic or elastic cases.},
	keywords = {3D, Modelling, time domain},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2016/louboutin2016SEGocp/louboutin2016SEGocp.html},
	author = {Mathias Louboutin and Gerard Gorman and Felix J. Herrmann}
}
@techreport {herrmann2016SLBors,
	title = {Overview research at the SINBAD Consortium},
	number = {TR-EOAS-2016-1},
	year = {2016},
	note = {Presented at a seminar at Schlumberger Gould, Cambridge on March 17, 2016.},
	month = {03},
	institution = {UBC},
	keywords = {Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2016/SLB/herrmann2016SLBors/herrmann2016SLBors.pdf},
	author = {Felix J. Herrmann}
}
@conference {dasilva2015EAGEogt,
	title = {Off the grid tensor completion for seismic data interpolation},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2015},
	note = {(EAGE, Madrid)},
	month = {06},
	abstract = {The practical realities of acquiring seismic data in a realistic survey are often at odds with the stringent requirements of Shannon-Nyquist-based sampling theory. The unpredictable movement of the ocean{\textquoteleft}s currents can be detrimental in acquiring exactly equally-spaced samples while sampling at Nyquist-rates are expensive, given the huge dimensionality and size of the data volume. Recent work in matrix and tensor completion for seismic data interpolation aim to alleviate such stringent Nyquist-based sampling requirements but are fundamentally posed on a regularly-spaced grid. In this work, we extend our previous results in using the so-called Hierarchical Tucker (HT) tensor format for recovering seismic data to the irregularly sampled case. We introduce an interpolation operator that resamples our tensor from a regular grid (in which we impose our low-rank constraints) to our irregular sampling grid. Our framework is very flexible and efficient, depending primarily on the computational costs of this operator. We demonstrate the superiority of this approach on a realistic BG data set compared to using low-rank tensor methods that merely use binning.},
	keywords = {EAGE, hierarchical tucker, irregular sampling, off the grid, structured tensor, tensor interpolation},
	doi = {10.3997/2214-4609.201412978},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/dasilva2015EAGEogt/dasilva2015EAGEogt.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/dasilva2015EAGEogt/dasilva2015EAGEogt_pres.pdf},
	author = {Curt Da Silva and Felix J. Herrmann}
}
@presentation {dasilva2015SINBADogt,
	title = {Off the grid tensor completion for seismic data interpolation},
	year = {2015},
	publisher = {SINBAD},
	abstract = {In this work, we extend our previous low-rank tensor techniques for seismic data interpolation to the {\textquotedblleft}off-the-grid{\textquotedblright} case, wherein the data is not acquired on a regularly-spaced grid. This irregular sampling scheme causes the underlying tensor to become high-rank, thus preventing our previous Hierarchical Tucker tensor algorithms from successfully recovering the volume. Instead of applying these techniques naively, we introduce a fictitious, regular grid on which the volume exhibits low-rank behaviour and a resampling operator that interpolates the data from the regular, low-rank domain to the irregular sampling domain. By incorporating these changes in to our previous framework, we can interpolate irregularly sampled data volumes with costs determined primarily by the choice of resampling transform. In our case, we use a non-uniform Fast Fourier Transform, but other choices are possible.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/dasilva2015SINBADogt/dasilva2015SINBADogt.pdf},
	author = {Curt Da Silva and Felix J. Herrmann}
}
@article {dasilva2014htuck,
	title = {Optimization on the Hierarchical Tucker manifold - applications to tensor completion},
	journal = {Linear Algebra and its Applications},
	volume = {481},
	year = {2015},
	note = {(Linear Algebra and its Applications)},
	month = {09},
	pages = {131-173},
	abstract = {In this work, we develop an optimization framework for problems whose solutions are well-approximated by Hierarchical Tucker (HT) tensors, an efficient structured tensor format based on recursive subspace factorizations. By exploiting the smooth manifold structure of these tensors, we construct standard optimization algorithms such as Steepest Descent and Conjugate Gradient for completing tensors from missing entries. Our algorithmic framework is fast and scalable to large problem sizes as we do not require SVDs on the ambient tensor space, as required by other methods. Moreover, we exploit the structure of the Gramian matrices associated with the HT format to regularize our problem, reducing overfitting for high subsampling ratios. We also find that the organization of the tensor can have a major impact on completion from realistic seismic acquisition geometries. These samplings are far from idealized randomized samplings that are usually considered in the literature but are realizable in practical scenarios. Using these algorithms, we successfully interpolate large-scale seismic data sets and demonstrate the competitive computational scaling of our algorithms as the problem sizes grow.},
	keywords = {differential geometry, Gauss{\textendash}Newton, hierarchical tucker tensors, low-rank tensor, Riemannian manifold optimization, tensor completion},
	doi = {10.1016/j.laa.2015.04.015},
	url = {https://slim.gatech.edu/Publications/Public/Journals/LinearAlgebraAndItsApplications/2015/dasilva2014htuck/dasilva2014htuck.pdf},
	url2 = {http://www.sciencedirect.com/science/article/pii/S0024379515002530},
	author = {Curt Da Silva and Felix J. Herrmann}
}
@presentation {lin2015SINBADFosd,
	title = {Our student-driven HPC environment},
	year = {2015},
	publisher = {SINBAD},
	abstract = {A major role of academic environments is to provide learning experiences for students to critically analyze and develop methods, both at a high-level of mathematical rigour, and at a low-enough level of implementation in order to yield experimental results on real datasets. Often these two goals are in conflict with each other, in terms of both learning time and attention. At SLIM, we strive to strike a balance between the two by abstracting away many of the low-level aspects of distributed HPC under a framework that matches syntactically with the mathematics of our field, while exposing enough control parameters for tuning performance characteristics. This talk will touch on the history of our efforts at SLIM, and culminate in an overview of our current method of interacting with the in-house compute cluster.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/lin2015SINBADFosd/lin2015SINBADFosd.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@techreport {witte2015TRoam,
	title = {Overview on anisotropic modeling and inversion},
	number = {TR-EOAS-2015-6},
	year = {2015},
	month = {08},
	institution = {UBC},
	abstract = {This note provides an overview on strategies for modeling and inversion with the anisotropic wave equation. Since linear and non-linear inversion methods like least squares RTM and Full Waveform Inversion depend on matching observed field data with synthetically modelled data, accounting for anisotropy effects is necessary in order to accurately match waveforms at long offsets and propagation times. In this note, the two main strategies for anisotropic modelling by solving either a pseudo acoustic wave equation or a pure quasi-P-wave equation are discussed and an inversion workflow using the pure quasi-P-wave equation is provided. In particular, we derive the exact adjoint of the anisotropic forward modelling and jacobian operator and give a detailled describtion of their implementation. The anistropic FWI workflow is tested on a sythetic data example.},
	keywords = {anisotropy, full waveform inversion, Modeling},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2015/witte2015TRoam/witte2015TRoam.html},
	author = {Philipp A. Witte and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {lopez2014SINBADogm,
	title = {Off-the-grid matrix completion},
	year = {2014},
	publisher = {SINBAD},
	abstract = {This talk discusses a modified rank minimization workflow designed to handle unstructured matrix completion problems. We extend current rank minimization based trace interpolation techniques to optimally handle undersampled irregular data, in such a way that allows us to recover regularized densely sampled data. This is achieved by introducing a regularization operator, that accurately allows us to simulate the effect of an unstructured grid. We provide reconstruction error bounds to demonstrate the potential of our procedure.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/lopez2014SINBADogm/lopez2014SINBADogm.pdf},
	author = {Oscar Lopez and Rajiv Kumar and Ernie Esser and Felix J. Herrmann}
}
@conference {wason2013EAGEobs,
	title = {Ocean bottom seismic acquisition via jittered sampling},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2013},
	month = {06},
	abstract = {We present a pragmatic marine acquisition scheme where multiple source vessels sail across an ocean-bottom array firing at airgunsjittered source locations and instances in time. Following the principles of compressive sensing, we can significantly impact the reconstruction quality of conventional seismic data (from jittered data) and demonstrate successful recovery by sparsity promotion. In contrast to random (under)sampling, acquisition via jittered (under)sampling helps in controlling the maximum gap size, which is a practical requirement of wavefield reconstruction with localized sparsifying transforms. Results are illustrated with simulations of time-jittered marine acquisition, which translates to jittered source locations for a given speed of the source vessel, for two source vessels.},
	keywords = {Acquisition, blended, deblending, EAGE, interpolation, marine},
	doi = {10.3997/2214-4609.20130379},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2013/wason2013EAGEobs/wason2013EAGEobs.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2013/wason2013EAGEobs/wason2013EAGEobs_pres.pdf},
	author = {Haneet Wason and Felix J. Herrmann}
}
@conference {li2013SEGodmvdaiedwawe,
	title = {Optimization driven model-space versus data-space approaches to invert elastic data with the acoustic wave equation},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {32},
	year = {2013},
	month = {09},
	pages = {986-990},
	abstract = {Inverting data with elastic phases using an acoustic wave equation can lead to erroneous results, especially when the number of iterations is too high, which may lead to over fitting the data. Several approaches have been proposed to address this issue. Most commonly, people apply "data-independent" filtering operations that are aimed to deemphasize the elastic phases in the data in favor of the acoustic phases. Examples of this approach are nested loops over offset range and Laplace parameters. In this paper, we discuss two complementary optimization-driven methods where the minimization process decides adaptively which of the data or model components are consistent with the objective. Specifically, we compare the Student{\textquoteright}s t misfit function as the data-space alternative and curvelet-domain sparsity promotion as the model-space alternative. Application of these two methods to a realistic synthetic lead to comparable results that we believe can be improved by combining these two methods.},
	keywords = {elastic, Full-waveform inversion, least-squares, SEG},
	doi = {10.1190/segam2013-1375.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2013/li2013SEGodmvdaiedwawe/li2013SEGodmvdaiedwawe.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2013/li2013SEGodmvdaiedwawe/li2013SEGodmvdaiedwawe_pres.pdf},
	author = {Xiang Li and Anais Tamalet and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {vanleeuwen2012SINBADoof,
	title = {An object-oriented framework for frequency-domain FWI},
	year = {2012},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADSPRING2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Spring/vanleeuwen2012SINBADoof/vanleeuwen2012SINBADoof_pres.pdf},
	author = {Tristan van Leeuwen}
}
@presentation {wason2012SINBADobs,
	title = {Ocean bottom seismic acquisition via jittered sampling},
	year = {2012},
	publisher = {SINBAD},
	abstract = {We present a pragmatic marine acquisition scheme where a single (or multiple) vessel sails across an ocean-bottom array firing airguns at ? optimally jittered source locations and instances in time. Following the principles of compressive sensing, we can significantly impact the reconstruction quality of conventional seismic data (from jittered data) and demonstrate successful recovery by sparsity promotion. In contrast to random (under)sampling, acquisition via jittered (under)sampling helps in controlling the maximum gap size, which is a practical requirement of wavefield reconstruction with localized sparsifying transforms. Results are illustrated with simulations of optimally jittered marine acquisition, and periodic time-dithering marine acquisition.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/wason2012SINBADobs/wason2012SINBADobs_pres.pdf},
	author = {Haneet Wason}
}
@conference {wason2012CSEGode,
	title = {Only dither: efficient simultaneous marine acquisition},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2012},
	month = {02},
	abstract = {Simultaneous-source acquisition is an emerging technology that is stimulating both geophysical research and commercial efforts. Simultaneous marine acquisition calls for the development of a new set of design principles and post-processing tools. The focus here is on simultaneous-source marine acquisition design and sparsity-promoting sequential-source data recovery. We propose a pragmatic simultaneous-source, randomized marine acquisition scheme where multiple vessels sail across an ocean-bottom array firing airguns at {\textendash}- sequential locations and randomly time-dithered instances. By leveraging established findings from the field of compressive sensing, where the choice of the sparsifying transform needs to be incoherent with the compressive sampling matrix, we can significantly impact the reconstruction quality, and demonstrate that the compressive sampling matrix resulting from the proposed sampling scheme is sufficiently incoherent with the curvelet transform to yield successful recovery by sparsity promotion. Results are illustrated with simulations of {\textquotedblleft}purely{\textquotedblright} random marine acquisition, which requires an airgun to be located at each source location, and random time-dithering marine acquisition with one and two source vessels. Size of the collected data volumes in all cases is the same. Compared to the recovery from the former acquisition scheme (SNR = 10.5dB), we get good results by dithering with only one source vessel (SNR = 8.06dB) in the latter scheme, which improve at the cost of having an additional source vessel (SNR = 9.85dB).},
	keywords = {Acquisition, CSEG, marine, simultaneous},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2012/wason2012CSEGode/wason2012CSEGode.pdf},
	author = {Haneet Wason and Felix J. Herrmann}
}
@conference {wason2012EAGEode,
	title = {Only dither: efficient simultaneous marine acquisition},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2012},
	month = {06},
	abstract = {Simultaneous-source acquisition is an emerging technology that is stimulating both geophysical research and commercial efforts. The focus here is on simultaneous-source marine acquisition design and sparsity-promoting sequential-source data recovery. We propose a pragmatic simultaneous-source, randomized marine acquisition scheme where multiple vessels sail across an ocean-bottom array firing airguns at {\textendash}- sequential locations and randomly time-dithered instances. Within the context of compressive sensing, where the choice of the sparsifying transform needs to be incoherent with the compressive sampling matrix, we can significantly impact the reconstruction quality, and demonstrate that the compressive sampling matrix resulting from the proposed sampling scheme is sufficiently incoherent with the curvelet transform to yield successful recovery by sparsity promotion. Results are illustrated with simulations of {\textquoteleft}{\textquoteleft}purely" random marine acquisition, which requires an airgun to be located at each source location, and random time-dithering marine acquisition with one and two source vessels. Size of the collected data volumes in all cases is the same. Compared to the recovery from the former acquisition scheme (SNR = 10.5dB), we get good results by dithering with only one source vessel (SNR = 8.06dB) in the latter scheme, which improve at the cost of having an additional source vessel (SNR = 9.44dB).},
	keywords = {Acquisition, EAGE, marine, simultaneous},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2012/wason2012EAGEode/wason2012EAGEode.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2012/wason2012EAGEode/wason2012EAGEode_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=58915},
	author = {Haneet Wason and Felix J. Herrmann}
}
@presentation {herrmann2012SINBADfwi,
	title = {Our findings on the Chevron benchmark dataset},
	year = {2012},
	publisher = {SINBAD},
	abstract = {During this presentation, we will review our findings working with the synthetic GOM data released as part of the post SEG workshop:"Gulf of Mexico Imaging Challenges: What Can Full Waveform Inversion Achieve?". This is joint work with Andrew J. Calvert, Ian Hanlon, Mostafa Javanmehri, Rajiv Kumar, Tristan van Leeuwen, Xiang Li, Brendan R. Smithyman, Eric Takam Takougang, and Haneet Wason.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/herrmann2012SINBADfwi/herrmann2012SINBADfwi_pres.pdf},
	author = {Andrew J. Calvert and Ian Hanlon and Mostafa Javanmehri and Rajiv Kumar and Tristan van Leeuwen and Xiang Li and Brendan R. Smithyman and Eric Takam Takougang and Haneet Wason and Felix J. Herrmann}
}
@presentation {wason2011SINBADode,
	title = {Only dither: efficient marine acquisition {\textquoteleft}{\textquoteleft}without" simultaneous sourcing},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/wason2011SINBADode/wason2011SINBADode_pres.pdf},
	author = {Haneet Wason}
}
@conference {vandenberg2009SLIMocf,
	title = {Optimizing costly functions with simple constraints: a limited-memory projected Quasi-Newton algorithm},
	booktitle = {SLIM},
	series = {Twelfth International Conference on Artificial Intelligence and Statistics},
	volume = {12},
	year = {2009},
	month = {04},
	abstract = {An optimization algorithm for minimizing a smooth function over a convex set is described. Each iteration of the method computes a descent direction by minimizing, over the original constraints, a diagonal-plus low-rank quadratic approximation to the function. The quadratic approximation is constructed using a limited-memory quasi-Newton update. The method is suitable for large-scale problems where evaluation of the function is substan- tially more expensive than projection onto the constraint set. Numerical experiments on one-norm regularized test problems indicate that the proposed method is competitve with state-of-the-art methods such as bound-constrained L-BFGS and orthant-wise descent. We further show that the method generalizes to a wide class of problems, and substantially improves on state-of-the-art methods for problems such as learning the structure of Gaussian graphi- cal models (involving positive-definite matrix constraints) and Markov random fields (involving second-order cone constraints).},
	keywords = {SLIM},
	url = {http://www.cs.ubc.ca/ mpf/papers/SchmidtBergFriedMurph09.pdf},
	author = {Ewout van den Berg and Mark Schmidt and Michael P. Friedlander and K. Murphy}
}
@conference {hennenfent2008SEGonri,
	title = {One-norm regularized inversion: learning from the Pareto curve},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2008},
	publisher = {SEG},
	organization = {SEG},
	abstract = {Geophysical inverse problems typically involve a trade off between data misfit and some prior. Pareto curves trace the optimal trade off between these two competing aims. These curves are commonly used in problems with two-norm priors where they are plotted on a log-log scale and are known as L-curves. For other priors, such as the sparsity-promoting one norm, Pareto curves remain relatively unexplored. First, we show how these curves provide an objective criterion to gauge how robust one-norm solvers are when they are limited by a maximum number of matrix-vector products that they can perform. Second, we use Pareto curves and their properties to define and compute one-norm compressibilities. We argue this notion is key to understand one-norm regularized inversion. Third, we illustrate the correlation between the one-norm compressibility and the perfor- mance of Fourier and curvelet reconstructions with sparsity promoting inversion.},
	keywords = {SEG},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/hennenfent08SEGonri/hennenfent08SEGonri.pdf},
	author = {Gilles Hennenfent and Felix J. Herrmann}
}
@techreport {hennenfent08TRori,
	title = {One-norm regularized inversion: learning from the Pareto curve},
	number = {TR-EOAS-2008-5},
	year = {2008},
	institution = {UBC Earth and Ocean Sciences Department},
	abstract = {Geophysical inverse problems typically involve a trade off between data misfit and some prior. Pareto curves trace the optimal trade off between these two competing aims. These curves are commonly used in problems with two-norm priors where they are plotted on a log-log scale and are known as L-curves. For other priors, such as the sparsity-promoting one norm, Pareto curves remain relatively unexplored. First, we show how these curves provide an objective criterion to gauge how robust one-norm solvers are when they are limited by a maximum number of matrix-vector products that they can perform. Second, we use Pareto curves and their properties to define and compute one-norm compressibilities. We argue this notion is key to understand one-norm regularized inversion. Third, we illustrate the correlation between the one-norm compressibility and the performance of Fourier and curvelet reconstructions with sparsity promoting inversion.},
	keywords = {SLIM},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2008/hennenfent08TRori/hennenfent08TRori.pdf},
	author = {Gilles Hennenfent and Felix J. Herrmann}
}
@article {tang2008CITEocs,
	title = {Optimized Compressed Sensing for Curvelet-based Seismic Data Reconstruction},
	journal = {CiteSeer},
	year = {2008},
	abstract = {Compressed sensing (CS) or compressive sampling provides a new sampling theory to reduce data acquisition, which says that compressible signals can be exactly reconstructed from highly incomplete sets of measurements. Very recently, the CS has been applied for seismic exploration and started to compact the traditional data acquisition. In this paper, we present an optimized sampling strategy for the CS data acquisition, which leads to better performance by the curvelet sparsity-promoting inversion in comparison with random sampling and jittered sampling scheme. One of motivation is to reduce the mutual coherence between measurement sampling schemes and curvelet sparse transform in the CS framework. The basic idea of our optimization is to directly redistribute the energy in frequency domain making original spectrum easily discriminated from the random noise induced by random undersampling, while offering control on the maximum gap size. Numerical experiments on synthetic and real seismic data show good performances of the proposed optimized CS for seismic data reconstruction.},
	keywords = {curvelet transform, Optimized Compressive Sensing},
	url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.154.7994},
	url2 = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.154.7994\&rep=rep1\&type=pdf},
	author = {Wen Tang and Jianwei Ma and Felix J. Herrmann}
}
@conference {herrmann2006SINBADom,
	title = {Opening meeting},
	booktitle = {SINBAD 2006},
	year = {2006},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/herrmann2006SINBADom/herrmann2006SINBADom.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2005EAGEosf,
	title = {Optimization strategies for sparseness- and continuity-enhanced imaging: theory},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2005},
	month = {06},
	abstract = {Two complementary solution strategies to the least-squares migration problem with sparseness- and continuity constraints are proposed. The applied formalism explores the sparseness of curvelets on the reflectivity and their invariance under the demigration-migration operator. Sparseness is enhanced by (approximately) minimizing a (weighted) l1-norm on the curvelet coefficients. Continuity along imaged reflectors is brought out by minimizing the anisotropic diffusion or total variation norm which penalizes variations along and in between reflectors. A brief sketch of the theory is provided as well as a number of synthetic examples. Technical details on the implementation of the optimization strategies are deferred to an accompanying paper: implementation.},
	keywords = {EAGE, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2005/Herrmann05EAGEosf/Herrmann05EAGEosf.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=1343},
	author = {Felix J. Herrmann and Peyman P. Moghaddam and R. Kirlin}
}
@conference {herrmann2003SEGoiw,
	title = {"Optimal" imaging with curvelets},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {22},
	year = {2003},
	pages = {997-1000},
	abstract = {In this paper we present a non-linear edge-preserving solution to linear inverse scattering problems based on optimal basis-function decompositions. Optimality of the basis functions allow us to (i) reduce the dimensionality of the inverse problem; (ii) devise non-linear thresholding operators that approximate minimax (minimize the maximal mean square error given the worst possible prior) and that significantly improve the signal-to-noise ratio on the image. We present a reformulation of the standard generalized least-squares formulation of the seismic inversion problem into a formulation based on thresholding, where the singular values, vectors and linear estimators are replaced by quasi-singular values, basis-functions and thresholding. To limit the computational burden we use a Monte-Carlo sampling method to compute the quasi-singular values. With the proposed method, we aim to significantly improve the signal-to-noise ratio (SNR) on the model space and hence the resolution of the seismic image. While classical Tikhonov-regularized methods only gain the square-root of the SNR on the data for the SNR on the model our method scales almost linearly. This significant improvement of the SNR allows us to discern events at high frequencies which would normally be in the noise.},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.1818117},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2003/Herrmann03SEGoiw/Herrmann03SEGoiw_pres.pdf},
	author = {Felix J. Herrmann}
}
