@presentation {louboutin2017SINBADFddg,
	title = {Data driven Gradient Sampling for seismic inversion},
	year = {2017},
	publisher = {SINBAD},
	abstract = {We present in this work an extension of the Gradient Sampling algorithm presented at the last EAGE in Paris. We previously showed the potential of this algorithm playing with implicit time-shifts to represent the wavefield of a slightly perturbed velocity model. We introduce an extension where the weights of the Gradient Sampling algorithm are obtained with the solve of data-based quadratic subproblem instead of at random. The update direction is the a more accurate representation of the true Gradient Sampling update direction.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/louboutin2017SINBADFddg/louboutin2017SINBADFddg.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/louboutin2017SINBADFddg/louboutin2017SINBADFddg.mov},
	author = {Mathias Louboutin and Felix J. Herrmann}
}
@presentation {kumar2017SINBADFfas,
	title = {Full-azimuth seismic data processing w/ coil acquisition},
	year = {2017},
	publisher = {SINBAD},
	abstract = {In this work, we will demonstrate the performance of our in-house 5D low-rank based interpolation method on a seismic data acquired using coil shooting full-azimuth acquisition. We will show that we can recover full-azimuthal interpolated data from highly subsampled data, where the subsampling ratio is 4\%. This is the first time, we are testing our interpolation ideas on real 3D marine seismic data acquisition. Our findings show that we can avoid the general practice of windowing the data while performing the interpolation, specially using rank-minimization based framework.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/kumar2017SINBADFfas/kumar2017SINBADFfas.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/kumar2017SINBADFfas/kumar2017SINBADFfas.mov},
	author = {Rajiv Kumar and Nick Moldoveanu and Keegan Lensink and Felix J. Herrmann}
}
@presentation {lopez2017SINBADFagf,
	title = {A Guide for Successful Low-Rank Matrix Recovery in Seismic Applications},
	year = {2017},
	publisher = {SINBAD},
	abstract = {This talk presents recent results in the theory of low-rank matrix recovery as heuristics for seismic practitioners. In the theory of matrix completion, we discuss the spectral gap as a means to quantify how successful a given sub sampling scheme will be for trace interpolation. Additionally, we consider previously proposed random sampling techniques and develop conditions on the sampling distribution that guarantees successful low-rank matrix recovery. The results apply to time-jittered acquisition, off-the-grid trace interpolation and source separation for simultaneous towed-streamer marine acquisition. Put together, the talk provides practical instruments that help design acquisition schemes in favor of rank penalization techniques.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/lopez2017SINBADFagf/lopez2017SINBADFagf.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/lopez2017SINBADFagf/lopez2017SINBADFagf.mov},
	author = {Oscar Lopez and Rajiv Kumar}
}
@presentation {witte2017SINBADFals,
	title = {A large-scale framework in Julia for fast prototyping of seismic inversion algorithms},
	year = {2017},
	publisher = {SINBAD},
	abstract = {We present our progress on a large-scale seismic modeling workflow in Julia for wave-equation based inversion. The software offers a range of high-level abstractions to easily express PDE constrained optimization problems in terms of linear algebra expressions, while utilizing the DSL Devito to symbolically express the underlying PDEs and to generate fast and parallel code for solving them. Data containers and linear operators can be set up without much effort from input SEG-Y data and scale to large-scale 3D applications. This talk provides an overview of the basic functionalities of our software and applications to least squares imaging and 3D FWI.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/witte2017SINBADFals/witte2017SINBADFals.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/witte2017SINBADFals/witte2017SINBADFals.mov},
	author = {Philipp A. Witte and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {louboutin2017SINBADFldi,
	title = {Latest developments in Devito},
	year = {2017},
	publisher = {SINBAD},
	abstract = {We present an overview of the latest developments in Devito. We introduced Devito in the previous meeting as a prototype finite-difference DSL for seismic modelling and inversion. We are presenting here the latest improvements and functionalities of Devito. We will also discuss the current future plans as well as non-supported features that the audience may be interested in. This presentation will be followed by/mixed with a hands-in tutorial if the time and resources allows it.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/louboutin2017SINBADFldi/louboutin2017SINBADFldi.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/louboutin2017SINBADFldi/louboutin2017SINBADFldi.mov},
	author = {Mathias Louboutin and Michael Lange and Fabio Luporini and Navjot Kurjeka and Jan Hueckelheim and Gerard Gorman and Philipp A. Witte and Felix J. Herrmann}
}
@presentation {lopez2017SINBADFmci,
	title = {Matrix Completion in Parallel Architectures: Julia Implementation},
	year = {2017},
	publisher = {SINBAD},
	abstract = {Matrix completion techniques offer potential tools for frugal seismic data acquisition, where dense acquisition is replaced by optimization. This shift of focus means that efficient numerical methods are critical for the implementation of these techniques in large-scale seismic applications. To this end, this talk modifies rank-penalization methodologies to suit parallel architectures. By adopting factorization-based alternating minimization schemes, each program can be decoupled into independent sub-problems handled in parallel. We showcase a distributed parallel execution in Julia and explore the scalability of the approach.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/lopez2017SINBADFmci/lopez2017SINBADFmci.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/lopez2017SINBADFmci/lopez2017SINBADFmci.mov},
	author = {Oscar Lopez and Keegan Lensink and Rajiv Kumar and Henryk Modzelewski}
}
@presentation {wang2017SINBADFnra,
	title = {Noise robust and time-domain formulations of Wavefield Reconstruction Inversion},
	year = {2017},
	publisher = {SINBAD},
	abstract = {We propose a wave-equation-based subsurface inversion method that in many cases is more robust than conventional Full-Waveform Inversion. The new formulation is written in a denoising form that allows the synthetic data to match the observed ones up to a small error. Compared to regular Full-Waveform Inversion, our method treats the noise arising from the data meassuring/recording process and that from the synthetic modelling process separately. Compared to Wavefields Reconstruction Inversion, the new formulation mitigates the difficulty of choosing the penalty parameter λ. To solve the proposed optimization problem, we develop an efficient frequency domain algorithm that alternatively updates the model and the data. Numerical experiments confirm strong stability of the proposed method by comparisons between the results of our algorithm with that from both plain FWI and a weighted formulation of the FWI. We also discuss a new memory efficient time-domain formulation for Wavefield Reconstruction Inversion based on duality.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/wang2017SINBADFnra/wang2017SINBADFnra.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/wang2017SINBADFnra/wang2017SINBADFnra.mov},
	author = {Rongrong Wang and Mathias Louboutin and Bas Peters and Emmanouil Daskalakis 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 {louboutin2016SINBADFhps,
	title = {High-performance seismic applications of OPESCI},
	year = {2016},
	publisher = {SINBAD},
	abstract = {We present our latest geophysical applications built on OPESCI. By using a high-level symbolic API, we allow for fast development and easy implementation of various (acoustic, VT, TTI) wave propagators relevant to exploration geophysics. We start by highlighting possibilities in an acoustic setting including classical operators such as forward modelling and linearised forward (Born) modelling as well as more advanced operators deriving from wave equations with double dipoles and the application of the PDE to a wavefield instead of applying its inverse. We will also show that the performance (time to solution) of this code is on par with industrial software libraries (10\% faster on the full SEAM model). We finally present our implementation of 3D TTI modelling and its adjoint including out comprehensive testing framework. This is joint work with Gerard Gorman},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/louboutin2016SINBADFhps/louboutin2016SINBADFhps.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/louboutin2016SINBADFhps/louboutin2016SINBADFhps.mov},
	author = {Mathias Louboutin and Felix J. Herrmann}
}
@presentation {lopez2016SINBADFlrm,
	title = {Low-rank matrix recovery for parallel architectures},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Low-rank matrix recovery (LRMR) techniques offer potential tools for frugal seismic data acquisition, where dense acquisition is replaced by large-scale optimization. This shift of focus means that efficient numerical methods are critical for successful LRMR implementation in seismic applications. In this talk, we extend the rank-penalization methodology to a parallelizable framework. We adopt a factorization-based alternating minimization scheme and decouple it into an independent system of simpler sub-problems that can be handled in parallel. The methodology is flexible, where the approach can be adapted to the number of workers available. Numerical experiments are conducted to demonstrate that the quality of reconstruction is comparable to existing methods at a fraction of the computational time.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/lopez2016SINBADFlrm/lopez2016SINBADFlrm.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/lopez2016SINBADFlrm/lopez2016SINBADFlrm.mov},
	author = {Oscar Lopez and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {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}
}
@presentation {lopez2015SINBADdmc,
	title = {Deterministic matrix completion: applications to seismic trace interpolation},
	year = {2015},
	publisher = {SINBAD},
	abstract = {This talk discusses recent developments in the theory of matrix completion that allow us to analyze how successful a given sub sampling scheme will be for seismic trace interpolation via nuclear norm minimization. By adopting a graph theory perspective, we utilize the spectral gap of our sampling operator as a means to quantify our acquisition design. This approach offers a heuristic technique that is practical and with the potential to achieve trace interpolation via rank penalization in a deterministic manner.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/lopez2015SINBADdmc/lopez2015SINBADdmc.pdf},
	author = {Oscar Lopez 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}
}
@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}
}
@presentation {peters2015SINBADFrwi,
	title = {Regularizing waveform inversion by projections onto intersections of convex sets},
	year = {2015},
	publisher = {SINBAD},
	abstract = {Common strategies to regularize waveform inversion (and other geophysical inverse problems) are adding quadratic penalty terms to the objective function or filtering the gradients used to update the model estimate. An example are penalties or filters to prevent/filter spurious high spatial frequency oscillations in the model while working with low frequency data. We present an alternative way of regularization, which works by projecting the model onto an intersection of convex sets, where each sets encodes certain desired model properties. This approach has certain theoretical and practical advantages over quadratic penalties or gradient filters. Some examples of useful convex sets in various challenging waveform inversion settings are shown on both real and synthetic data.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/peters2015SINBADFrwi/peters2015SINBADFrwi.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/peters2015SINBADFrwi/peters2015SINBADFrwi.mov},
	author = {Bas Peters and Brendan R. Smithyman and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {esser2015SINBADFrsa,
	title = {Resolving scaling ambiguities with the L1/L2 norm in a blind deconvolution problem with feedback},
	year = {2015},
	publisher = {SINBAD},
	abstract = {Compared to more mundane blind deconvolution problems, blind deconvolution in seismic applications involves a feedback mechanism related to the free surface. The presence of this feedback mechanism gives us an unique opportunity to remove ambiguities that have plagued blind deconvolution for a long time. While beneficial, this feedback by itself is insufficient to remove the ambiguities even with L1 constraints. However, when paired with an L1/L2 constraint the feedback allows us to resolve the scaling ambiguity under relatively mild assumptions. Inspired by lifting approaches, we propose to split the sparse signal into positive and negative components and apply an \ell_1/\ell_2 constraint to the difference, thereby obtaining a constraint that is easy to implement. Numerical experiments demonstrate robustness to the initialization as well as to noise in the data.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/esser2015SINBADFrsa/esser2015SINBADFrsa.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/esser2015SINBADFrsa/esser2015SINBADFrsa.mov},
	author = {Ernie Esser and Rongrong Wang and Tim T.Y. Lin and Felix J. Herrmann}
}
@presentation {dasilva2015SINBADFsss,
	title = {Scaling SINBAD software to 3-D on Yemoja},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We present early results on the scalability of SINBAD{\textquoteright}s wavefield reconstruction and wave-equation based inversion technologies on Yemoja, a 17k core cluster made available to us by BG Group at SENAI CIMATEC Supercomputing Centre in Brazil.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/dasilva2015SINBADFsss/dasilva2015SINBADFsss.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/dasilva2015SINBADFsss/dasilva2015SINBADFsss.mov},
	author = {Curt Da Silva and Haneet Wason and Mathias Louboutin and Bas Peters and Shashin Sharan and Zhilong Fang and Felix J. Herrmann}
}
@presentation {fang2015SINBADFuqw,
	title = {Uncertainty quantification for Wavefield-Reconstruction Inversion using a positive-definite approximated Hessian},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We analyze the Hessian of wavefield reconstruction inversion (WRI) and propose a new approximated Hessian. Instead of requiring PDE solves, the matrix-vector multiplication action of the approximate Hessian can be achieved with several matrix-vector multiplications. The diagonal part of the approximated Hessian can be also calculated without additional PDE solves. We apply this approximated Hessian to uncertainty quantification and obtain statistical parameters such as standard deviation and confidence interval. Numerical example illustrate the accuracy of the estimated Hessian and the feasibility of the method to quantify uncertainties.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/fang2015SINBADFuqw/fang2015SINBADFuqw.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/fang2015SINBADFuqw/fang2015SINBADFuqw.mov},
	author = {Zhilong Fang and Chia Ying Lee and Curt Da Silva and Felix J. Herrmann and Rachel Kuske}
}
@presentation {lopez2015SINBADFumc,
	title = {Universal matrix completion: applications to seismic data acquisition},
	year = {2015},
	publisher = {SINBAD},
	abstract = {This talk discusses the potential applications of universal matrix completion for infill management and acquisition design. We consider recent developments in the theory of matrix completion that produce practical tools to quantify how successful a given sub sampling mask will be for seismic trace interpolation via nuclear norm minimization. This approach provides an on the fly infill management system as well as instruments to design acquisition schemes that favor interpolation via rank penalization techniques.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/lopez2015SINBADFumc/lopez2015SINBADFumc.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/lopez2015SINBADFumc/lopez2015SINBADFumc.mov},
	author = {Oscar Lopez and Haneet Wason and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {li2014SINBADtsi,
	title = {2D/3D time-stepping for imaging and inversion},
	year = {2014},
	publisher = {SINBAD},
	abstract = {In this talk, we present a SLIM version of 2D/3D time-stepping modeling kernel for inversion and imaging. One key aspect of this framework is that the implementation follows some mathematic principles, such as linear operator test. Aside from obeying mathematic principles, we also developed a strategy that allows us to compute the gradient of conventional FWI objective or RTM imaging without storing extra wavefield. We also show some applications of this framework on the Chevron 2014 benchmark elastic synthetic data set.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/li2014SINBADtsi/li2014SINBADtsi.pdf},
	author = {Xiang Li and Felix J. Herrmann}
}
@presentation {lin2014SINBADdag,
	title = {Dealing with acquisition gaps in Robust EPSI without interpolation using wavefield auto-convolution},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Straightforward modifications to the Estimation of Primaries by Sparse Inversion (EPSI) problem allows us to mitigate acquisition holes without any reconstruction of the missing traces, neither prior to nor during the inversion process. This is achieved by simulating the missing multiple contributions with terms involving auto-convolutions of the primary wavefield. In this formulation we no longer need to treat the missing data as another unknown in the inversion process, which is important in eliminating a significant source of possible local minima from attempting to invent data using incorrect primary and multiple models. In this talk we investigate the necessary modifications to the Robust EPSI problem, as well as algorithms that account for the resulting non-linear modelling operator. We will also investigate the reconstruction limits of this approach in relation to acquisition geometry and subsurface characteristics.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/lin2014SINBADdag/lin2014SINBADdag.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@presentation {tu2014SINBADfis2,
	title = {Fast imaging with source estimation},
	year = {2014},
	publisher = {SINBAD},
	abstract = {In seismic imaging, an inaccurate estimate of the source wavelet may result in degraded seismic images. While conventional reverse-time migration requires the knowledge of the source wavelet as prior information, we propose to include source estimation in the fast compressive imaging procedure. Using the proposed method, we can obtain seismic images that are comparable to those imaged with the true source wavelet, with computational costs that are comparable to conventional RTM images with all the data. We also extend the proposed method to image data with surface-related multiples, where these multiples help to mitigate the amplitude ambiguity in source estimation. We verify the proposed method using realistic synthetic examples.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/tu2014SINBADfis2/tu2014SINBADfis2.pdf},
	author = {Ning Tu and Aleksandr Y. Aravkin and Tristan van Leeuwen and Tim T.Y. Lin and Felix J. Herrmann}
}
@presentation {lago2014SINBADhfw,
	title = {Heuristics in full-waveform inversion},
	year = {2014},
	publisher = {SINBAD},
	abstract = {For many full-waveform inversion techniques, the most computationally intensive step is the computation of a numerical solution for the wave equation on every iteration. In the frequency domain approach, this requires the solution of very large, complex, sparse, ill-conditioned linear systems. In this abstract we bring out attention specifically to CGMN method for solving PDEs, known for being flexible (i.e. it is able to treat equally acoustic data as well as visco-elastic or more complex scenarios) efficient with respect both to memory and computation time, and controllable accuracy of the final approximation. We propose an improvement for the known CGMN method by imposing a minimal residual condition, which incurs in one extra model vector storage. The resulting algorithm called CRMN enjoys several interesting properties as monotonically nonincreasing behaviour of the norm of the residual and minimal residual, guaranteeing optimal convergence for the relative residual criterion. We discuss numerical experiments both in an isolated PDE solve and also within the inversion procedure, showing that in a realistic scenario we can expect a speedup around 25\% when using CRMN rather than CGMN. Joint work with Art Petrenko, Zhilong Fang, Felix J. Herrmann.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/lago2014SINBADhfw/lago2014SINBADhfw.pdf},
	author = {Rafael Lago and Felix J. Herrmann}
}
@presentation {lin2014SINBADiit,
	title = {Implicit interpolation of trace gaps in REPSI using auto-convolution terms},
	year = {2014},
	publisher = {SINBAD},
	abstract = {It is possible to solve the Estimation of Primaries by Sparse Inversion problem from a sesimic record with large holes without any explicit data reconstruction, by instead simulating the missing multiple contributions with terms involving auto-convolutions of the primary wavefield. Exclusion of the unknown data as an inversion variable from the REPSI process is desireable, since it eliminates a significant source of local minima that arises from attempting to invert for the unobserved traces using primary and multiple models that may be far-away from the true solution. In this talk we investigate the necessary modifications to the REPSI algorithm to account for the resulting non-linear modeling operator, and demonstrate that just a few auto-convolution terms are enough to satisfactorily mitigate the effects of data gaps during the inversion process.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/lin2014SINBADiit/lin2014SINBADiit.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@presentation {esser2014SINBADlcs,
	title = {A lifted $\ell_1$/$\ell_2$ constraint for sparse blind deconvolution},
	year = {2014},
	publisher = {SINBAD},
	abstract = {We propose a modification to a sparsity constraint based on the ratio of l1 and l2 norms for solving blind seismic deconvolution problems in which the data consist of linear convolutions of different sparse reflectivities with the same source wavelet. No assumptions are made about the location of the support of either the wavelet or the sparse signals. Minimizing the ratio of l1 and l2 norms has been previously shown to promote sparsity in a variety of applications including blind deconvolution. Most existing implementations are heuristic or require smoothing the l1/l2 penalty. Lifted versions of l1/l2 constraints have also been proposed but are still challenging to implement. Inspired by the lifting approach, we propose to split the sparse signals into positive and negative components and apply an l1/l2 constraint to the difference, thereby obtaining a constraint that is easy to implement without smoothing the l1 or l2 norms. We show that a method of multipliers implementation of the resulting model can recover source wavelets that are not necessarily minimum phase and approximately reconstruct the sparse reflectivities. It appears to be robust to the initialization and to small amounts of noise in the data. We also discuss extensions to the Estimation of Primaries by Sparse Inversion (EPSI) convolution model.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/esser2014SINBADlcs/esser2014SINBADlcs.pdf},
	author = {Ernie Esser and Tim T.Y. Lin and Rongrong Wang and Felix J. Herrmann}
}
@presentation {lago2014SINBADmri,
	title = {Minimal-residual iterative methods for time-harmonic wave-equation},
	year = {2014},
	publisher = {SINBAD},
	abstract = {In this work we aim at comparing the performance of some iterative methods for solving the time-harmonic acoustic wave-equation, one of the most challenging problems in the numerical linear algebra community. Widely used in frequency domain full waveform inversion, the discrete wave-equation yields very large, complex, sparse, ill-conditioned linear systems. In this extended abstract we aim at further comparing our recently proposed method, CRMN, against CGMN and another method renown for being very memory efficient, the two-level preconditioner with shifted Laplacian operator. We propose several improvements for the two-level preconditioner by combining this method with CRMN in several senses.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/lago2014SINBADmri/lago2014SINBADmri.pdf},
	author = {Rafael Lago and Felix J. Herrmann}
}
@presentation {lin2014SINBADmas,
	title = {Multilevel acceleration strategy for REPSI},
	year = {2014},
	publisher = {SINBAD},
	abstract = {This talk discusses a multilevel inversion strategy that aims to substantially reduce the computational costs of the Robust Estimation of Primaries by Sparse Inversion algorithm. The proposed method solves early iterations of REPSI at very coarse spatial sampling grids while gradually ramping-up the spatial sampling when more accuracy is desired. No changes to the the core implementation of the original algorithm are necessary while in addition only requiring trace decimation, low-pass filtering, and rudimentary interpolation techniques.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/lin2014SINBADmas/lin2014SINBADmas.pdf},
	author = {Tim T.Y. Lin 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}
}
@presentation {lopez2014SINBADrma,
	title = {Rank minimization via alternating optimization},
	year = {2014},
	publisher = {SINBAD},
	abstract = {This talk discusses the benefits of an alternating optimization approach for seismic trace interpolation via rank minimization. We consider recent factorization approaches to rank minimization problems where we write our low rank matrix in bi-linear form, and modify this workflow by alternating our optimization to handle a single matrix factor at a time. This allows for a more tractable procedure that can better handle highly sub sampled data sets without increasing the time complexity. We demonstrate the potential of this approach with several seismic trace interpolation experiments.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/lopez2014SINBADrma/lopez2014SINBADrma.pdf},
	author = {Oscar Lopez and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {fang2014SINBADsew,
	title = {Source estimation for WRI and its application},
	year = {2014},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/fang2014SINBADsew/fang2014SINBADsew.pdf},
	author = {Zhilong Fang and Xiang Li and Bas Peters and Felix J. Herrmann}
}
@presentation {fang2014SINBADsqn,
	title = {A stochastic quasi-Newton McMC method for uncertainty quantification of full-waveform inversion},
	year = {2014},
	publisher = {SINBAD},
	abstract = {In this work, we present a fast McMC method using the stochastic l-BFGS Hessian to quantify the uncertainty of full-waveform inversion. Using the stochastic l-BFGS Hessian, we do not need the assumption that the Hessian of data misfit is low rank and we also reduce the computational cost of estimating the Hessian. Numerical result shows the capability of this fast McMC method.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/fang2014SINBADsqn/fang2014SINBADsqn.pdf},
	author = {Zhilong Fang and Chia Ying Lee and Felix J. Herrmann}
}
@presentation {kumar2014SINBADsfm,
	title = {SVD-free matrix completion for seismic data reconstruction},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Seismic data interpolation via rank-minimization techniques has been recently introduced in the seismic community. All the existing rank-minimization techniques assumes the underlying grid to be regular. Irregularity is one of the common impediments in acquisition. In this work, we studied the effect of irregularity on structured and show that how we can modify the existing techniques to handle it. Other then irregularity, we often have missing data. We also show that we can tackle both regularization and interpolation issue simultaneously. The objective of this work is to extend our existing method of interpolation on structured grid to unstructured grid. We illustrate the advantages of the modification in existing methodology using a seismic line from Gulf of Suez to obtain high quality results for regularization and interpolation, a key application in exploration geophysics.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/kumar2014SINBADsfm/kumar2014SINBADsfm.pdf},
	author = {Rajiv Kumar and Oscar Lopez and Ernie Esser and Felix J. Herrmann}
}
@presentation {fang2014SINBADuqw,
	title = {Uncertainty quantification for Wavefield Reconstruction Inversion},
	year = {2014},
	publisher = {SINBAD},
	abstract = {This talk discusses the uncertainty quantification for the wavefield reconstruction inversion. The Hessian and gradient of the WRI can be easily obtained once the wave field is generated. As a result, we use a Newton type McMC method to sample the posterior distribution. Statistical property such as mean, standard deviation, confidence interval are calculated based on the sampled distribution.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/fang2014SINBADuqw/fang2014SINBADuqw.pdf},
	author = {Zhilong Fang and Chia Ying Lee and Curt Da Silva and Felix J. Herrmann and Rachel Kuske}
}
@presentation {li2014SINBADwmg,
	title = {Why the modified Gauss-Newton method?},
	year = {2014},
	publisher = {SINBAD},
	abstract = {In our earlier work, we develop the modified Gauss-Newton method for FWI, which requires each update of FWI to be sparse in the curvelet domain. Our empirical observation of the MGN method is that it can find us a solution for FWI problem with sparse perturbation of the initial guess without changing the underlying objective. In this talk, we will analyze the MGN method to find out the reason why it can generate a sparse perturbation of the initial model, because sum of spares updates could easily generate non-sparse perturbations. Moreover, we will illustrate when do we expect the modified Gauss-Newton method to yield a solution with sparse perturbation and in what circumstances should we use it in place of other algorithms like standard Gauss-Newton.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/li2014SINBADwmg/li2014SINBADwmg.pdf},
	author = {Xiang Li and Ernie Esser and Felix J. Herrmann}
}
@presentation {lin2013SINBADbre,
	title = {Bootstrapping robust EPSI with coarsely sampled data},
	year = {2013},
	publisher = {SINBAD},
	abstract = {The EPSI method of surface multiple removal directly inverts for the free-surface operator, i.e., the multiple-free Greens function of the subsurface seismic response. One peculiar feature of this approach is the theoretical independence of the spectrum of the free-surface operator from the spectrum of the observed data. The SRME approach requires coarsely sampled data to be low-pass filtered sufficiently in order to avoid aliasing in multiple contribution gathers, which in turn limits the temporal resolution of the demultipled result. Conversely, such limitations in temporal resolution do not directly apply to the inversion solution of EPSI. This property can in turn be exploited both to significantly lower the cost of EPSI and to mitigate the effect of under sampled data in a controlled way.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/lin2013SINBADbre/lin2013SINBADbre.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@presentation {lin2013SINBADcsd,
	title = {Cosparse seismic data interpolation},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Over the years we have investigated seismic data interpolation and redatuming algorithms rely on on the assumption that seismic records and images permit sparse approximations under certain representations, such as Curvelet coefficients. Recent findings have suggested that for redundant representations (of which Curvelet is an example), the analysis operator that maps the physical signal to coefficients may also play a crucial role in recovering data from incomplete observations. This insight elevates the significance of a question that often goes unaddressed: is it better for the transform-domain sparsity to be achieved through explicit construction of sparse representations (e.g., by thresholding of small transform-domain coefficients), or by demanding that the algorithm return physical signals which produces sparse coefficients when hit with the forward transform? Recent results show that the two approaches give rise to different solutions when the transform is redundant, and that the latter approach imposes a whole new class of constraints. In particular, the number of zero-valued coefficients given by the analysis operator acting on the signal, referred to as its "cosparsity", have an analogous role to the sparsity of the signal in terms of the coefficients. From this framework, a new reconstruction algorithm is proposed which may allow better reconstruction from subsampled signaled than what the sparsity assumption alone would predict. In this work we apply the new framework and algorithm to the case of seismic data interpolation under the curvelet domain, and show that it admits better reconstruction than some existing L1 sparsity-based methods derived from compressive sensing for a range of subsampling factors. We will also investigate different analysis operators and their impact on both sparsity and cosparsity-based algorithms.},
	keywords = {Presentation, SINBAD, SINBADSPRING2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/lin2013SINBADcsd/lin2013SINBADcsd.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@presentation {lago2013SINBADksf,
	title = {Krylov solvers in frequency domain FWI},
	year = {2013},
	publisher = {SINBAD},
	abstract = {We briefly discuss here several aspects rising from the use of Krylov solvers for frequency domain FWI. Although several powerful preconditioners are in constant development by the linear algebra community targeting this application, some issues as the multishot and multifrequency scenarios as well as advanced Krylov method techniques in combination with these powerful preconditioners are rarely addressed. We provide an overview of some of the recent research on this regard and discuss possibility of use of some of these techniques in the context of an inversion.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/lago2013SINBADksf/lago2013SINBADksf.pdf},
	author = {Rafael Lago and Felix J. Herrmann}
}
@presentation {li2013SINBADgom,
	title = {Lessons learned from Chevron Gulf of Mexico data set},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Chevron Gulf of Mexico data set is very challenging for FWI because of elastic phases, limited offset, lack of low frequencies and salt structure. To overcome these issue, we first use ray-based tomography on the hand-picked first breaks to generate initial model for FWI, and then we apply curvelet-denosing techniques to improve the poor signal-to-noise ratio of the observed data at low frequencies. Finally, Curvelet domain sparsity promoting Gauss-Newton FWI helps to suppress model space artifacts caused by elastic phases. This is joint work with Andrew J. Calvert, Ian Hanlon, Mostafa Javanmehri, Rajiv Kumar, Tristan van Leeuwen, Brendan R. Smithyman, Haneet Wason and Felix J. Herrmann},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/li2013SINBADgom/li2013SINBADgom.pdf},
	author = {Xiang Li and Andrew J. Calvert and Ian Hanlon and Mostafa Javanmehri and Rajiv Kumar and Tristan van Leeuwen and Brendan R. Smithyman and Haneet Wason and Felix J. Herrmann}
}
@presentation {li2013SINBADmsd,
	title = {Model-space versus data-space FWI with the acoustic wave equation},
	year = {2013},
	publisher = {SINBAD},
	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 data- 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 presentation, 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 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 = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/li2013SINBADmsd/li2013SINBADmsd.pdf},
	author = {Xiang Li and Anais Tamalet and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {fang2013SINBADsfwi,
	title = {Swift FWI},
	year = {2013},
	publisher = {SINBAD},
	abstract = {In 3D case, there is much more data and modeling is much more expensive. As a result, parallel computing is very important for 3D full-waveform inversion. Both domain decomposition and data decomposition need a large number of parallel computing resources. However, programs based on parallel MATLAB suffer from the limitation of licenses. In order to obtain a MATLAB licenses free solution, we use SWIFT, which is a fast and easy parallel scripting language. Once the original Mat file is complied to an executable file, SWIFT can run the code inside the executable file in parallel without using parallel MATLAB. We use the SWIFT to compute the object functions and gradients of different shots in parallel, and test the parallel 3D FWI code with overthrust data. This is joint work with Thomas Lai, Harsh Juneja, Bas Peters.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/fang2013SINBADsfwi/fang2013SINBADsfwi.pdf},
	author = {Zhilong Fang and Henryk Modzelewski and Tristan van Leeuwen and Thomas Lai and Harsh Juneja and Bas Peters and Felix J. Herrmann}
}
@presentation {xiang2012SINBADfgn,
	title = {Fast Gauss-Newton full-waveform inversion with sparsity regularization},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Full-waveform inversion (FWI) can be considered as a controlled data fitting process, in which we approximately fit observed data by iteratively updating the initial velocity model, we expect the final model can reveal subsurface structure till the wavefield misfit can converge to designed tolerance. The conventional FWI approach is expensive since it requires the inversion of a linear system, which involves extremely large multi-experiment data volumes. To overcome this issue we percent a curvetlet based sparsity-promoting Gauss-Newton inversion method. In this presentation we invert for the model updates by replacing the normal Gauss-Newton linearized subproblem for subsampled FWI with a sparsity promoting FWI formulation. We speed up the algorithm and avoid over fitting the data by solving the problem approximately. Aside from this, we control wavefield dispersion by gradually increasing grid size as we move to higher frequencies. Our approach is successful because it reduces the size of seismic data volumes without loss of information. With this reduction, we can compute a Newton-like update with the reduced data volume at the cost of roughly one gradient update for the fully sampled wavefield.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/xiang2012SINBADfgn/xiang2012SINBADfgn_pres.pdf},
	author = {Xiang Li}
}
@presentation {lin2012SINBADics,
	title = {An introduction to cosparse signal reconstruction},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Undersampling techniques in exploration seismology usually relies on the assumption that seismic records and images permit sparse approximations under certain representations, such as Curvelet coefficients. Recent findings have suggested that for redundant representations (of which Curvelet is an example), the analysis operator that maps the physical signal to coefficients may also play a crucial role in recovering data from incomplete observations. In particular, the number of zero-valued coefficients given by the analysis operator acting on the signal, referred to as its "cosparsity", have an analogous role to the sparsity of the signal in terms of the coefficients. The cosparsity of the signal permits recovery guarantees that are completely separate from sparsity-based models, and gives rise to distinct sets of reconstruction algorithms and performances compared to sparsity-based approaches. We present in this talk some initial findings on the viability of cosparse reconstruction for a variety of seismic applications that previously relied on sparse signal reconstruction, such as data interpolation and source separation.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/lin2012SINBADics/lin2012SINBADics_pres.pdf},
	author = {Tim T.Y. Lin}
}
@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 {Lin2012SINBADrdr,
	title = {Recent developments on the robust estimation of primaries by sparse inversion},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Robust estimation of primaries by sparse inversion is a next-generation surface multiple removal technique with an objective to truly invert an operator that models the free-surface. Key to the success of this approach is the imposition of a sparsity constraint on the primary impulse response in the time domain. This is accomplished by carefully applying large-scale convex optimization techniques on an extended L1 minimization problem. One of the benefits of our approach is that many extensions to the algorithm can be devised under this optimization framework to improve the quality of the solution given fixed computational costs and mitigating various shortcomings in field data. This talk will first review the basic technique of Robust EPSI and follow with some highlights on recent further developments of the algorithm, including a discussion on the role of regularization by reciprocity and the interpolation of near-offset data, as well as investigations into optimality and robustness to data outliers.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/Lin2012SINBADrdr/Lin2012SINBADrdr_pres.pdf},
	author = {Tim T.Y. Lin}
}
@presentation {herrmann2012SINBADhpc,
	title = {SLIM{\textquoteright}s perspective on HPC \& big data},
	year = {2012},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/herrmann2012SINBADhpc/herrmann2012SINBADhpc_pres.pdf},
	author = {Tim T.Y. Lin and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {xiang2012SINBADweb,
	title = {Wave-equation based inversion with joint sparsity promotion},
	year = {2012},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/xiang2012SINBADweb/xiang2012SINBADweb_pres.pdf},
	author = {Xiang Li}
}
@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}
}
@presentation {lin2011SINBADirEPSI,
	title = {Inside the robust EPSI formulation},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/lin2011SINBADirEPSI/lin2011SINBADirEPSI_pres.pdf},
	author = {Tim T.Y. Lin}
}
@presentation {aravkin2011SINBADrsp,
	title = {A randomized, sparsity promoting, Gauss-Newton algorithm for seismic waveform inversion},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/aravkin2011SINBADrsp/aravkin2011SINBADrsp_pres.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/aravkin2011SINBADrsp/li2011SINBADrsp_pres.pdf},
	author = {Aleksandr Y. Aravkin and Xiang Li}
}
@presentation {lin2011SINBADrep,
	title = {Robust EPSI in a curvelet-like representation domain},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADSPRING2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Spring/lin2011SINBADrep/lin2011SINBADrep_pres.pdf},
	author = {Tim T.Y. Lin}
}
@presentation {lin2011SINBADslim,
	title = {SLIM{\textquoteright}s software design principles},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/lin2011SINBADslim/lin2011SINBADslim_pres.pdf},
	author = {Tim T.Y. Lin}
}
@presentation {li2010SINBADci,
	title = {Compressive imaging},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/li2010SINBADci/li2010SINBADci_pres.pdf},
	author = {Xiang Li}
}
@presentation {li2010SINBADfwi,
	title = {Full-waveform inversion with randomized L1 recovery for the model updates},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/li2010SINBADfwi/li2010SINBADfwi_pres.pdf},
	author = {Xiang Li}
}
@presentation {lin2010SINBADlib,
	title = {Leveraging informed blind deconvolution techniques for the estimation of primaries by sparse inversion},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/lin2010SINBADlib/lin2010SINBADlib_pres.pdf},
	author = {Tim T.Y. Lin}
}
@presentation {lin2010SINBADsre,
	title = {Software release: estimation of primaries by L1 inversion},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	author = {Tim T.Y. Lin}
}
@presentation {lin2010SINBADsol,
	title = {Sparse optimization and the L1 norm},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/lin2010SINBADsol/lin2010SINBADsol_pres.pdf},
	author = {Tim T.Y. Lin}
}
@conference {lebed2008SINBADaoc1,
	title = {Applications of curvelets/surfacelets to seismic data processing},
	booktitle = {SINBAD},
	year = {2008},
	abstract = {In this talk we explore several applications of the curvelet and surfacelet transforms to seismic data processing. The first application is stable signal recovery in the physical domain - seismic data acquisition is often limited by physical and economic constraints, and the goal is to interpolate the data from a given subset of seismic traces. The second application is signal recovery in a transform domain - we assume that our data comes in a form of a random subset of temporal frequencies and the goal is to recover the missing frequencies from this data. Since seismic signals are generally not bandwidth limited, this in fact becomes an anti-aliasing problem. In both these problems the recovery is resolved via a robust $\ell$_1 solver that exploits the sparsity of the signals in curvelet/surfacelet domains. In the last application we explore the problem of primary-multiple separation by simple thresholding.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/lebed2008SINBADaoc1/lebed2008SINBADaoc1.pdf},
	author = {Evgeniy Lebed}
}
@conference {lin2008SINBADcwe,
	title = {Compressed wavefield extrapolation},
	booktitle = {SINBAD},
	year = {2008},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/sites/data/Papers/lin08cwe.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@conference {lebed2008SINBADaoc,
	title = {Curvelet / Surfacelet comparison},
	booktitle = {SINBAD},
	year = {2008},
	abstract = {Curvelets and Surfacelets are two transforms that aim to achieve a multiscale and a multidirectional decomposition of arbitrary N-dimensional ($N>=2$) signals. While both transforms are Fourier-based, their construction is intrinsically different. In this talk we will give and overview of the construction of the two transforms, and explore their properties such as frequency domain / spatial domain coherence, sparsity, redundancy and computational complexity.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/lebed2008SINBADaoc/lebed2008SINBADaoc.pdf},
	author = {Evgeniy Lebed}
}
@conference {herrmann2008SINBADitc,
	title = {Introduction to compressive (wavefield) computation},
	booktitle = {SINBAD 2008},
	year = {2008},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/herrmann2008SINBADitc/herrmann2008SINBADitc.pdf},
	author = {Felix J. Herrmann and Yogi A. Erlangga and Tim T.Y. Lin and Cody R. Brown}
}
@conference {lin2006SINBADci,
	title = {Compressed imaging},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {In 1998 Grimbergen et. al. introduced a new method for computing wavefield propagation which improved on the previously employed local explicit operator method in that it exhibited no dip limitation, accurately handled laterally varying background ground velocity models, and is unconditionally stable. These desirable properties are mainly attributed to bringing the propagation problem into an eigenvector basis that diagonalizes the propagation operators. This modal-transform method, however, requires at each depth-level the solution of a large-scale sparse eigenvalue problem to compute the square-root of the Helmholtz operator. By using recent results from compressed sensing, we hope to reduce these computational costs that typically involve the synthesizes of the imaging operators and the cost of matrix-vector products. To reduce these costs, we compress the extrapolation operators by using only a fraction of the positive eigenvalues and temporal frequencies. This reduction not only leads to smaller matrices but also to reduced synthesis costs. These reductions go at the expense of solving a recovery problem from incomplete data. During the presentation, we show that wavefields can accurately be extrapolated with a compressed operators and competitive costs.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/lin2006SINBADci/lin2006SINBADci.pdf},
	author = {Tim T.Y. Lin}
}
