@presentation {Herrmann2019SEGDL,
	title = {Sometimes it pays to be cheap {\textendash} Compressive time-lapse seismic data acquisition},
	year = {2019},
	note = {(SEG Distinguished Lecture)},
	publisher = {Society of Exploration Geophysicists},
	abstract = {During these times of sustained low oil prices, it is essential to look for new innovative ways to collect (time-lapse) seismic data at reduced costs and preferably also at reduced environmental impact. By now, there is an increasing body of corroborating evidence {\textemdash} whether these are simulated case studies or actual acquisitions on land and marine {\textemdash} that seismic acquisition based on the principles of compressive sensing delivers on this premise by removing the need to acquire replicated dense surveys. Up to ten-fold increases in acquisition efficiency have been reported by industry while there are indications that this breakthrough is only the beginning of a paradigm shift where full-azimuth time-lapse processing will become a reality. To familiarize the audience with this new technology, I will first describe the basics of compressive sensing, how it relates to missing-trace interpolation and simultaneous source acquisition, followed by how this technology is driving innovations in full-azimuth (time-lapse) acquisition, yielding high-fidelity data with a high degree of repeatability and at a fraction of the costs.},
	keywords = {Compressive Sensing, Presentation, Time-lapse Marine Acquisition},
	url = {https://slim.gatech.edu/Publications/Public/Lectures/SEG-DL/2019/Herrmann2019SEGDL/Herrmann2019SEGDL/},
	url2 = {https://slim.gatech.edu/Publications/Public/Lectures/SEG-DL/2019/Herrmann2019SEGDL/Herrmann2019SEGDL.pdf},
	author = {Felix J. Herrmann}
}
@presentation {peters2017SINBADFaaj,
	title = {Algorithms and Julia software for FWI with multiple constraints},
	year = {2017},
	publisher = {SINBAD},
	abstract = {We present a framework to add multiple convex and non-convex constraints to nonlinear inverse problems, specifically FWI. The constraints mitigate problems related to noisy data, artifacts arising from working with very few simultaneous sources, inaccurate starting models and using approximate physical forward models. Compared to earlier work at SLIM, the current framework is algorithmically simpler and computationally more efficient. We show examples where the model estimation is improved when we use very limited prior knowledge directly as constraints. We also present the software implementation in Julia and how it is used together with other software that compute data-misfit values and gradients w.r.t. the model parameters.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/peters2017SINBADFaaj/peters2017SINBADFaaj.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/peters2017SINBADFaaj/peters2017SINBADFaaj.m4v},
	author = {Bas Peters and Felix J. Herrmann}
}
@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 {herrmann2017SINBADFhrc,
	title = {Highly repeatable 3D compressive full-azimuth towed-streamer time-lapse acquisition {\textendash}- a numerical feasibility study at scale},
	year = {2017},
	publisher = {SINBAD},
	abstract = {Most conventional 3D time-lapse (or 4D) acquisitions are ocean-bottom cable (OBC) or ocean-bottom node (OBN) surveys since these surveys are relatively easy to replicate compared to towed-streamer surveys. To attain high degrees of repeatability, survey replicability and dense periodic sampling has become the norm for 4D surveys that renders this technology expensive. Conventional towed-streamer acquisitions suffer from limited illumination of subsurface due to narrow azimuth. Although, acquisition techniques such as multi-azimuth, wide-azimuth, rich-azimuth acquisition, etc., have been developed to illuminate the subsurface from all possible angles, these techniques can be prohibitively expensive for densely sampled surveys. This leads to uneven sampling, i.e., dense receiver and coarse source sampling or vice-versa, in order to make these acquisitions more affordable. Motivated by the design principles of Compressive Sensing (CS), we acquire economic, randomly subsampled (or compressive) and simultaneous towed-streamer time-lapse data without the need of replicating the surveys. We recover densely sampled time-lapse data on one and the same periodic grid by using a joint-recovery model (JRM) that exploits shared information among different time-lapse recordings, coupled with a computationally cheap and scalable rank-minimization technique. The acquisition is low cost since we have subsampled measurements (about 70\% subsampled), simulated with a simultaneous long-offset acquisition configuration of two source vessels travelling across a survey area at random azimuths. We analyze the performance of our proposed compressive acquisition and subsequent recovery strategy by conducting a synthetic, at scale, seismic experiment on a 3D time-lapse model containing geological features such as channel systems, dipping and faulted beds, unconformities and a gas cloud. Our findings indicate that the insistence on replicability between surveys and the need for OBC/OBN 4D surveys can, perhaps, be relaxed. Moreover, this is a natural next step beyond the successful CS acquisition examples discussed during this session.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/herrmann2017SINBADFhrc/herrmann2017SINBADFhrc.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/herrmann2017SINBADFhrc/herrmann2017SINBADFhrc.mov},
	author = {Felix J. Herrmann and Rajiv Kumar and Haneet Wason and Shashin Sharan and Felix Oghenekohwo}
}
@presentation {yang2017SINBADFiwm,
	title = {Imaging with multiples in shallow water},
	year = {2017},
	publisher = {SINBAD},
	abstract = {Based on the latest developments of research in inversion technology with optimization, researchers have made significant progress in the implementation of least-squares reverse-time migration (LS-RTM) of primaries. In Marine data however, these applications rely on the success of a pre-imaging separation of primaries and multiples, which can be modeled as a multi-dimensional convolution between the vertical derivative of the surface-free Green{\textquoteright}s function and the down-going receiver wavefield. Instead of imaging the primaries and multiples separately, we implement the LS-RTM of the total down-going wavefield by combining areal source injection and linearized Born modelling, where strong surface related multiples are generated from a strong density variation at the ocean bottom. The advantage including surface related multiples in LS-RTM is the extra illumination we obtain from these multiples without incurring additional computational costs related to carrying out multi-dimensional convolutions part of conventional multiple prediction procedures. Even though we are able to avert these computational costs, our approach shares the large costs of LS-RTM. We reduce these costs by combining randomized source subsampling with our sparsity-promoting imaging technology, which produces artifact-free, high-resolution images, with the surface-related multiples migrated properly.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/yang2017SINBADFiwm/yang2017SINBADFiwm.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/yang2017SINBADFiwm/yang2017SINBADFiwm.mov},
	author = {Mengmeng Yang and Emmanouil Daskalakis and Felix J. Herrmann}
}
@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 {graff2017SINBADFlrp,
	title = {Low-rank representation of omnidirectional subsurface extended image volumes},
	year = {2017},
	publisher = {SINBAD},
	abstract = {Extended image volumes are an important migration tool in seismic exploration. However the computation and the storage of omnidirectional subsurface extended image volumes are usually prohibitive. That is why some solutions have been already proposed for instance by focusing on horizontal offsets only. In our work, we will consider a linear algebra approach to deal with the low-rank representation of extended image volumes with full offsets. We will never build entirely the resulting matrix but get only actions of it on well-chosen probing vectors, based on Low-Rank decomposition or randomized SVD. This representation allows us to have access to all the energy of the extended image volume matrix and still limits the storage of the information and the computational cost.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/graff2017SINBADFlrp/graff2017SINBADFlrp.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/graff2017SINBADFlrp/graff2017SINBADFlrp.mov},
	author = {Marie Graff-Kray and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {zhang2017SINBADFmsd,
	title = {Massive seismic data compression \& recovery w/ on-the-fly data extraction},
	year = {2017},
	publisher = {SINBAD},
	abstract = {Industrial seismic exploration has moved towards complex geological areas, which requires typically long-offset and dense sampling data in order to avoid aliasing and inaccuracy in wave-equation based inversion algorithms. These strict requirements lead to massive data volume size and prohibitive demands on computational resources. In this work, we propose to compress our dense data in hierarchical Tucker tensor format by exploiting the low-rank structure of the data in a transformed domain. Then, we devise on-the-fly common shot or receiver gather extraction directly via the highly compressed factors. In subsampling scenarios, by interpolating this novel tensor format, we can also reconstruct the shot or receiver gather on a per-query basis rather than expanding the data to its fully-sampled form. We demonstrate the effective performance of our proposed technique on 3D stochastic full-waveform inversion, which allows the stochastic algorithm to extract shot gathers as it requires them throughout the inversion process. Moreover, we finally show how to computational effectively generate the CIGs from this compressed low-rank tensor representation of the data with the help of fast simultaneous shot or receiver gather generation.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/zhang2017SINBADFmsd/zhang2017SINBADFmsd.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/zhang2017SINBADFmsd/zhang2017SINBADFmsd.mov},
	author = {Yiming Zhang and Curt Da Silva and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {kumar2017SINBADFmdt,
	title = {Multi-domain target-oriented imaging using extreme-scale matrix factorization},
	year = {2017},
	publisher = {SINBAD},
	abstract = {In this work, we present an alternative approach to redatum both source and receivers at depth, under the framework of reflectivity-based extended images with two-way wave propagation in the background medium. We propose a randomized svd based probing scheme that takes advantage of the algebraic structure of the extended imaging system to overcome the computational cost and memory usage associated with the number of wave-equation solutions and explicit storage employed by conventional migration methods. Experimental results on complex geological models demonstrate the efficacy of proposed methodology in performing multi-domain target imaging.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/kumar2017SINBADFmdt/kumar2017SINBADFmdt.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/kumar2017SINBADFmdt/kumar2017SINBADFmdt.mov},
	author = {Rajiv Kumar and Marie Graff-Kray and Ivan Vasconcelos and Felix J. Herrmann}
}
@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 {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}
}
@presentation {fang2017SINBADFpfg,
	title = {PDE-free Gauss-Newton Hessian for Wavefield Reconstruction Inversion},
	year = {2017},
	publisher = {SINBAD},
	abstract = {In this work, we present a PDE-free Gauss-Newton Hessian for Wavefield Reconstruction Inversion. With this PDE-free Gauss-Newton Hessian, we can compute matrix-vector products without additional PDE solves. Thus, we are able to use the second order optimization method Gauss-Newton method with a roughly equal computational cost of first-order methods such as the gradient-descent method.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/fang2017SINBADFpfg/fang2017SINBADFpfg.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/fang2017SINBADFpfg/fang2017SINBADFpfg.mov},
	author = {Zhilong Fang and Felix J. Herrmann}
}
@presentation {alfaraj2017SINBADFros,
	title = {Reconstruction of S-waves from low-cost randomized acquisition},
	year = {2017},
	publisher = {SINBAD},
	abstract = {Due to the lower shear wave velocity compared with compressional waves, finer spatial sampling is required to properly record the earlier according to the Nyquist sampling criterion. To avoid higher acquisition costs and to utilize the multicomponent data to its available full extent, we propose acquiring randomly undersampled ocean bottom seismic data. We present two up- and down-going shear wave reconstruction methods: (i) rank minimization reconstruction followed by elastic wavefield decomposition, and (ii) sparsity promoting joint interpolation decomposition using all the multicomponent data in one optimization problem.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/alfaraj2017SINBADFros/alfaraj2017SINBADFros.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/alfaraj2017SINBADFros/alfaraj2017SINBADFros.mov},
	author = {Ali M. Alfaraj and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {siahkoohi2017SINBADFsdi,
	title = {Seismic data interpolation with Generative Adversarial Networks},
	year = {2017},
	publisher = {SINBAD},
	abstract = {In this project we implement an algorithm to predict the missing traces in the seismic shot gathers. The missing traces can be either regular or irregular. Any interpolation scheme assumes a prior knowledge on the data. Here the prior information used to interpolate the data is obtained from interaction of two trained deep neural networks, namely Generator and Discriminator. The combination of these two neural networks is called Generative Adversarial Network (GAN). GAN is trained on finely sampled seismic shot gathers. By employing the trained GAN we can project shot gathers with missing traces into the domain of the generator network. Then by computing the output of generator given the found projection, we can fill in the initial gather.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/siahkoohi2017SINBADFsdi/siahkoohi2017SINBADFsdi.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/siahkoohi2017SINBADFsdi/siahkoohi2017SINBADFsdi.mov},
	author = {Ali Siahkoohi and Felix J. Herrmann}
}
@presentation {daskalakis2017SINBADFsof,
	title = {Stochastic Optimization from the perspective of dynamical systems},
	year = {2017},
	publisher = {SINBAD},
	abstract = {We present improvements to a family of methods (Linearized Bregman, Kaczmarz and Stochastic Gradient Descent) that are often use in optimization problems. We explain the link of those methods with dynamical systems and we draw ideas for improving their performance. We use a simple idea to improve the stability and the performance of our family of optimization methods especially for ill-posed, inconsistent large-scale problems. Finally we present an application at a least squares migration problem, which highlight the importance of the suggested improvements on large scale Geophysical problems.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/daskalakis2017SINBADFsof/daskalakis2017SINBADFsof.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/daskalakis2017SINBADFsof/daskalakis2017SINBADFsof.m4v},
	author = {Emmanouil Daskalakis and Rachel Kuske and Mengmeng Yang and Felix J. Herrmann}
}
@presentation {sharan2017SINBADFtts,
	title = {Tracking the spatial-temporal evolution of fractures by microseismic source collocation},
	year = {2017},
	publisher = {SINBAD},
	abstract = {Unlike conventional reservoirs, unconventional plays are not naturally viable for economical production of oil and gas. They require stimulation by injecting high-pressure fluid causing fractures in the rocks. These fractures make the medium more permeable, hence, the extraction of oil and gas becomes feasible. For drilling purposes and to prevent potentially hazardous situations, we need to have good knowledge of the location of these fractures. Also, we need to have good knowledge about how these fractures originated in time. Hydraulic fracturing changes stress in rocks, which results in the emission of microseismic waves. The opening of cracks due to high pressure fluid injection during hydraulic fracturing mainly causes this change in stress in the rocks. Therefore, microseismic events are mostly localized along these fractures and have finite energy along time. To accurately track the evolution of fractures in both space and time, we need to locate closely spaced microseismic events along these fractures activating at very small time intervals. A naive approach can be the back propagation of the observed data to find out a point in space and time where maximum focusing of back propagating energy occurs. This point corresponds to the location and origin time of a microseismic source. This approach, although simpler, suffers from low resolution and requires scanning of complete 4D volume (3D in space and 1D in time). Hence, this method can be challenging when there are multiple closely spaced microseismic sources originating at different times. We in this work propose a sparsity promotion based method that can locate closely spaced microseismic events, with spatial separation as low as within half a wavelength, activating at small time intervals. We simultaneously estimate the origin time of microseismic events by estimating their source time functions. Our method exploits the fact that microseismic events are localized in space and have finite energy. We use accelerated Linearized Bregman algorithm with a preconditioning operator to arrive at a computationally feasible scheme.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/sharan2017SINBADFtts/sharan2017SINBADFtts.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/sharan2017SINBADFtts/sharan2017SINBADFtts.mov},
	author = {Shashin Sharan and Rongrong Wang and Felix J. Herrmann}
}
@presentation {bougher2016SINBADFaaa,
	title = {Amplitude vs. angle analysis as an unsupervised learning problem},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Amplitude vs. angle analysis (AVA) of pre-stack seismic data is a commonly used method for inferring petrophysical information from seismic data. Conventionally, a two-term linearized rock physics model (Shuey equation) is used to invert angle-domain common-image gathers. Multivariate analysis of the inverted terms leads to a background of siliciclastic interfaces, where outlying points are associated with hydrocarbon saturated sands. The acquisition and processing of seismic data does not result in highly-calibrated measurements that adhere to the rock physics model, which often inhibits the success of AVA analysis. We offer an alternative approach that uses PCA-based methods to learn projections directly from the data without the need of a physical model. Results on synthetic and field data show that PCA-based projections can improve segmentation of potential reservoirs in seismic data.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/bougher2016SINBADFaaa/bougher2016SINBADFaaa.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/bougher2016SINBADFaaa/bougher2016SINBADFaaa.mov},
	author = {Ben B. Bougher and Felix J. Herrmann}
}
@presentation {oghenekohwo2016SINBADFast,
	title = {Asymmetric sampling for time-lapse surveys},
	year = {2016},
	publisher = {SINBAD},
	abstract = {In this talk, we use the joint recovery model (JRM), which derives from distributed compressed sensing, to reconstruct time-lapse data acquired via time-jittered marine sources that are significantly subsampled in the monitor survey(s) in comparison to the baseline survey, hence, saving acquisition cost. We analyze the effects of two or more asymmetrically subsampled monitor surveys on wavefield reconstruction via JRM. We leverage the common information shared amongst the vintages during our sparsity-promoting recovery.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/oghenekohwo2016SINBADFast/oghenekohwo2016SINBADFast.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/oghenekohwo2016SINBADFast/oghenekohwo2016SINBADFast.mov},
	author = {Felix Oghenekohwo and Haneet Wason and Felix J. Herrmann}
}
@presentation {dasilva2016SINBADFccs,
	title = {Composite convex smooth optimization with seismic data processing applications},
	year = {2016},
	publisher = {SINBAD},
	abstract = {In this work, we show a general technique for solving optimization problems that are comprised of minimizing a composition of a convex, non-smooth function with a smooth function. We demonstrate this technique in the seismic data processing context applied to robust missing trace interpolation in the low-rank Hierarchical Tucker tensor format as well as cosparsity-based missing trace interpolation.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/dasilva2016SINBADFccs/dasilva2016SINBADFccs.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/dasilva2016SINBADFccs/dasilva2016SINBADFccs.mov},
	author = {Curt Da Silva and Felix J. Herrmann}
}
@presentation {herrmann2016SINBADFcvp,
	title = {Constraints versus penalties for edge-preserving full-waveform inversion},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Full-waveform inversion is challenging in complex geological areas. Even when provided with an accurate starting model, the inversion algorithms often struggle to update the velocity model. Contrary to other areas in applied geophysics, including prior information in full-waveform inversion is still relatively in its infancy. In part this is due to the fact that incorporating prior information that relates to geological settings where strong discontinuities in the velocity model dominate is difficult, because these settings call for non-smooth regularizations. We tackle this problem by including constraints on the spatial variations and value ranges of the inverted velocities, as opposed to adding penalties to the objective as is more customary in main stream geophysical inversion. By demonstrating the lack of predictability of edge-preserving inversion when the regularization is in the form of an added penalty term, we advocate the inclusion of constraints instead. Our examples show that the latter lead to more predictable results and to significant improvements in the delineation of Salt bodies. This is joint work with Bas Peters.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/herrmann2016SINBADFcvp/herrmann2016SINBADFcvp.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/herrmann2016SINBADFcvp/herrmann2016SINBADFcvp.mov},
	author = {Felix J. Herrmann and Bas Peters}
}
@presentation {peters2016SINBADFcnc,
	title = {Convex \& non-convex constraint sets for full-waveform inversion},
	year = {2016},
	publisher = {SINBAD},
	abstract = {We extend some of our previous work on constrained formulations of full-waveform inversion. Some motivating examples illustrate why solving constrained problems directly is a simpler approach for solving non-linear inverse problems than penalized problem formulations. So far we used constraints which can be represented as convex sets, for which most optimization theory is developed. Some non-convex counterparts of convex sets are introduced and examples show that the algorithms developed for convex sets also work well with non-convex sets. The motivation to also explore non-convex sets, is that they sometimes translate prior geological knowledge into mathematical constraints in a more direct way. All presented material is available as a user-friendly toolbox. The toolbox works with any code which can provide a data-misfit function value and a gradient direction.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/peters2016SINBADFcnc/peters2016SINBADFcnc.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/peters2016SINBADFcnc/peters2016SINBADFcnc.mov},
	author = {Bas Peters and Felix J. Herrmann}
}
@presentation {fang2016SINBADFeaq,
	title = {Efficient approach for quantifying uncertainty of wavefield reconstruction inversion},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Due to the noisy measurements, sparse observations, uncertain forward models, and uncertain prior parameter information, significant uncertainty arises in the inverted velocity model of full-waveform inversion. The uncertain velocity model leads to the uncertainty in the correct positions of events in the migrated images, which subsequently influences the important decision-makings in the oil and gas exploration and production (E\&P) business such as the drilling decisions and economic evaluations. Therefore, understanding the uncertainty in the inverted velocity model is essential for the mitigation of the E\&P risks. A common approach to accounting for uncertainty in seismic inverse problems like full-waveform inversion is to describe the unknown parameters in probabilistic by the Bayesian inference. The Bayesian inference aims at incorporating the information from the data, physics, and one{\textquoteright}s prior knowledge to formulate a posterior distribution function to characterize the statistical properties of the unknown parameters. However, characterizing uncertainties for the large-scale full-waveform inversion in seismic exploration is prohibitively expensive since it requires thousands of function evaluations to sample the parameter space (e.g. via Markov chain Monte Carlo sampling). Apart from this, the existing methods of uncertainty quantification for full-waveform inversion is through applying the Bayesian inference to the standard method such as the adjoint-state method with the assumption that no uncertainty arises from the wave-equation. However, the wave-equation may not be able to reflect all the physics, hence, incorporating uncertainty from wave-equation might benefit the quantification of uncertainty. In this work, we propose to perform uncertainty quantification using the recently proposed wavefield reconstruction inversion technique, which gives an appropriate framework to tackle the uncertainty in wave-equation solve by adding the misfit of the wave-equation to the likelihood distribution and relaxing the wave-equation constraint. Moreover, we propose a computationally feasible approach to analyzing the posterior distribution of the wavefield reconstruction inversion method, which incorporates a Gaussian distribution that approximates the posterior distribution with an optimization-driven Gaussian simulator to sample the Gaussian distribution efficiently. Numerical examples show that comparing to the McMC method, our approach is able to produce comparable results with less computational cost. This is joint work with Curt da Silva and Rachel Kuske.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/fang2016SINBADFeaq/fang2016SINBADFeaq.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/fang2016SINBADFeaq/fang2016SINBADFeaq.mov},
	author = {Zhilong Fang and Chia Ying Lee and Curt Da Silva and Tristan van Leeuwen and Felix J. Herrmann and Rachel Kuske}
}
@presentation {kumar2016SINBADFels,
	title = {Efficient large-scale 5D seismic data acquisition and processing using rank-minimization},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Seismic data collection is becoming challenging because of increased demands for high-quality, long-offset and wide-azimuth data. Leveraging ideas from CS, in this work we establish a cost effective acquisition and processing techniques, which are no longer dominated by survey area size but by the sparsity of seismic data volumes. In the first part of abstract, we establish connections between random time dithering and jittered sampling in space. Specifically, we recover high-quality 5D seismic data volumes from time-jittered marine acquisition where the average inter-shot time is significantly reduced, leading to cheaper surveys due to fewer overlapping shots. The time-jittered acquisition, in conjunction with the shot separation by Singular-Value Decomposition (SVD)-free factorization based rank-minimization approach, allows us to recover high quality 5D seismic data volumes. Results are illustrated for simulations of simultaneous time-jittered continuous recording for a 3D ocean-bottom cable survey, where we outperforms existing techniques, by an order of magnitude computational speedup and using 1/20th of the memory, that use sparsity in transforms domains. The second part of abstract focussed on leveraging low-rank structure in seismic data to solve extremely large data recovery (interpolation) problems. We introduced a large{\textendash}scale SVD-free optimization framework that is robust with respect to outliers and that uses information on the support. We test the efficacy of the proposed interpolation framework on a large-scale 5D seismic data, generated from the geologically complex synthetic 3D Compass velocity model, where 80\% of the data has been removed. Our findings show that major computational and memory gains are possible compared to curvelet-based reconstruction.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/kumar2016SINBADFels/kumar2016SINBADFels.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/kumar2016SINBADFels/kumar2016SINBADFels.mov},
	author = {Rajiv Kumar and Shashin Sharan and Haneet Wason and Felix J. Herrmann}
}
@presentation {sharan2016SINBADFhrm,
	title = {High resolution microseismic source collocation},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Microseismic waves generated by hydraulic fracturing are used to locate the fractures created in the reservoir. Most of the existing source collocation methods have their limitations in terms of resolution of very closely spaced microseismic events. In this work we propose a method to collocate microseismic events with high resolution along with simultaneous estimation of source time function. In the context of waves, the finite-difference modelling operator acts as an analysis operator for wavefields. This implies that wavefields under the action of a finite-difference modelling kernel can be focused to sources generating these wavefields. We exploit this property of wavefields to locate microseismic events and estimate the source time function jointly. To arrive at a computational feasible scheme, we use the accelerated version of Linearized Bregman (LBR) algorithm to solve the above mentioned problem. This is joint work with Rongrong Wang.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/sharan2016SINBADFhrm/sharan2016SINBADFhrm.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/sharan2016SINBADFhrm/sharan2016SINBADFhrm.mov},
	author = {Shashin Sharan and Rongrong Wang and Felix J. Herrmann}
}
@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 {witte2016SINBADFlst,
	title = {A large-scale time-domain modeling and inversion workflow in Julia},
	year = {2016},
	publisher = {SINBAD},
	abstract = {We present our initial steps towards the development of a large-scale seismic modeling workflow in Julia which provides a framework for wave-equation based inversion methods like full waveform inversion or least squares migration. Our framework is based on the Devito tool for optimized finite difference computations that generates highly optimized code from symbolic PDEs. We aim at developing a flexible workflow which is based on abstract operators and allows coding that is {\textquotedblleft}close to the math{\textquotedblright}, while at the same time relying on extremely fast and efficient, state of the art wave equation solvers.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFlst/witte2016SINBADFlst.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFlst/witte2016SINBADFlst.mov},
	author = {Philipp A. Witte and Felix J. Herrmann}
}
@presentation {wason2016SINBADFlcr,
	title = {Low-cost, randomized 3D towed-marine time-lapse seismic acquisition},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Most often conventional 3D towed-marine seismic data acquisition suffers from limited illumination of subsurface due to narrow azimuth. Although, acquisition techniques such as Ocean Bottom Cable (OBC), Ocean Bottom Node (OBN), Rich-Azimuth (RAZ) acquisition, Multi-Azimuth (MAZ) acquisition have been developed to illuminate the subsurface from all possible angles, these acquisition techniques are expensive. Motivated by the design principles of compressed sensing, we acquire randomly subsampled and simultaneous (e.g., simultaneous long offset) 3D towed-marine time-lapse seismic data for few (random) azimuths, and recover densely sampled multiple azimuth baseline and monitor data via sparsity promotion and rank minimization. Our acquisition is low cost since we have subsampled measurements.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/wason2016SINBADFlcr/wason2016SINBADFlcr.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/wason2016SINBADFlcr/marine_acq_mask.pdf},
	author = {Rajiv Kumar and Felix Oghenekohwo and Shashin Sharan and Haneet Wason 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 {kumar2016SINBADFlrm,
	title = {Low-rank methods for on-the-fly slicing \& dicing of seismic data \& image volumes},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Conventional oil and gas fields are increasingly difficult to explore and produce, which calls for more complex wave-equation based inversion (WEI) algorithms that require dense long-offset samplings. These requirements result in an exponential growth in data volumes and prohibitive demands on computational resources. In this work, we propose a fast, resilient, and scalable wave-equation based inversion methodology for both the data and image-domain, which can handle complicated wave physics. First we show that both the data and image domains exhibit low-rank structure in a transform-domain, which can be exploited to compress the dense data or image volumes. Then, by accessing information from the compressed volumes on-the fly, we devise a scalable computational inversion framework driven by gradient calculations, which works with small subsets of source experiments. In the full-waveform inversion context, we demonstrate the efficacy of low-rank interpolation to improve downstream inversion results compared to merely inverting the velocity model using the subsampled data volume directly. We demonstrate the effectiveness of the proposed framework on two different waveform-inversion formulations, specifically performing full-waveform inversion on 5D data set generated using the overthrust model, and wave-equation based migration velocity analysis on the Marmousi model. This is joint work with Curt Da Silva and Yiming Zhang.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/kumar2016SINBADFlrm/kumar2016SINBADFlrm.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/kumar2016SINBADFlrm/kumar2016SINBADFlrm.mov},
	author = {Rajiv Kumar and Curt Da Silva and Yiming Zhang 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 {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}
}
@presentation {witte2016SINBADFpve,
	title = {Phase velocity error minimizing scheme for the anisotropic pure P-wave equation},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Pure P-wave equations for acoustic modeling in transverse isotropic media are derived by approximating the exact pure P-wave dispersion relation. In this work, we present an alternative to the common Taylor expansion based equations, in which we approximate the exact dispersion relation through a polynomial expansion and determine its coefficients by solving a least squares problem that minimizes the phase velocity error over the complete range of phase angles. We show that this approach is up to an order of magnitude more accurate than other pure P-wave equations at a comparable computational cost.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFpve/witte2016SINBADFpve.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFpve/witte2016SINBADFpve.mov},
	author = {Philipp A. Witte and Christiaan C. Stolk and Felix J. Herrmann}
}
@presentation {witte2016SINBADFtdl,
	title = {Time-domain least-squares RTM with sparsity promotion on field data},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Least squares reverse time migration (LSRTM) is an inversion based imaging algorithm which can surpress acquisition related artifacts and provides images with increased frequency content, decreased noise level and balanced amplitudes. In this work demonstrate our time-domain LSRTM workflow using various pre-conditioners, source-subsampling and sparsity promotion and show its application to the 2D Machar field data set.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFtdl/witte2016SINBADFtdl.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFtdl/witte2016SINBADFtdl.mov},
	author = {Philipp A. Witte and Felix J. Herrmann}
}
@presentation {yang2016SINBADFtds,
	title = {Time-domain sparsity-promoting least-squares migration with source estimation},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Compared to traditional reverse-time migration (RTM), least-squares RTM (LS_RTM) is able to obtain true amplitude images as solutions of $\ell_2$-norm minimization problems by fitting the synthetic and observed reflection data. The shortcoming is that solutions of these $\ell_2$ problems tend to be overfitted and computationally too expensive. By working with randomized subsets of data only, the computational costs of LS-RTM can be brought down to an acceptable level, producing artifact-free high-resolution images. By including on-the-fly source-time function estimation into the method of Linearized Bregman (LB), we tackle the open issues of these "compressive imaging" in the aspects of algorithmic complexity of solver, guaranteed convergence and source estimation. We also inbvestigate whether the algorithm can be accelerated when we combine LB with the Nesterov method. Application of our algorithm on a 2D synthetic shows that we are able to get high-resolution images, with accurate estimates of the wavelet, for one single data pass.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/yang2016SINBADFtds/yang2016SINBADFtds.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/yang2016SINBADFtds/yang2016SINBADFtds.m4v},
	author = {Mengmeng Yang and Philipp A. Witte and Zhilong Fang and Felix J. Herrmann}
}
@presentation {wang2016SINBADFtmf,
	title = {Two methods for frequency down-extrapolation},
	year = {2016},
	publisher = {SINBAD},
	abstract = {It is well known that low frequency data are essential to the success of the Full-Waveform Inversion. Field data from a usual marine acquisition, however, typically lack reliable low frequencies due to the existing physical limitations. One research direction focuses on studying low-frequency extrapolation, the process of extending reliable frequency bands of the raw data towards the lower end of the spectrum. We propose two optimization problems, by solving which low frequencies can be made out of the high SNR high frequency component. We present both theoretical explanation and numerical experiments to demonstrate the efficacy as well as limitation of these methods.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/wang2016SINBADFtmf/wang2016SINBADFtmf.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/wang2016SINBADFtmf/wang2016SINBADFtmf.mov},
	author = {Rongrong Wang and Felix J. Herrmann}
}
@presentation {dasilva2016SINBADFuse,
	title = {A unified 2D/3D software environment for large scale time-harmonic full waveform inversion},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Full Waveform Inversion is a costly and complex procedure, resulting in industrial codebases that are often written entirely in low-level languages, making them hard to understand, maintain, improve, and extend. In this work, we propose a software framework for organizing a 3D FWI environment that helps mitigate design complexities inherent in the problem in a straightforward way while delegating the performance-critical portions to low level languages to maintain efficiency. The result is a software framework that has a unified interface for 2D and 3D and is flexible, efficient, scalable, and demonstrably correct. We demonstrate the effectiveness of this approach on a large scale 3D FWI example.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/dasilva2016SINBADFuse/dasilva2016SINBADFuse.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/dasilva2016SINBADFuse/dasilva2016SINBADFuse.mov},
	author = {Curt Da Silva and Felix J. Herrmann}
}
@presentation {herrmann2016SINBADFwom,
	title = {Welcome \& Overview of the Meeting},
	year = {2016},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/herrmann2016SINBADFwom/herrmann2016SINBADFwom.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/herrmann2016SINBADFwom/herrmann2016SINBADFwom.mov},
	author = {Felix J. Herrmann}
}
@presentation {peters2015SINBADF3dw,
	title = {3D WRI},
	year = {2015},
	publisher = {SINBAD},
	abstract = {The Wavefield Reconstruction Inversion (WRI) approach to seismic inversion is a topic of active research in the SLIM group. Several variants have been proposed, all of which rely on the solution of a linear least-squares problem with a Helmholtz discretization. While algorithms based on LU or QR factorization are very efficient for 2D problems, 3D problems required the development of a factorization-free iterative method. This talk introduces the current version of the algorithm, which requires significantly less memory and computation and uses existing Helmholtz solvers as an important building block. We plan to discuss early results of 3-D WRI on a small 3-D synthetic example.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/peters2015SINBADF3dw/peters2015SINBADF3dw.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/peters2015SINBADF3dw/peters2015SINBADF3dw.mov},
	author = {Bas Peters and Chen Greif and Felix J. Herrmann}
}
@presentation {kumar2015SINBADFaoi,
	title = {Affordable omnidirectional image volumes extension to 3D},
	year = {2015},
	publisher = {SINBAD},
	abstract = {Image gathers are an important tool for velocity analysis, AVA analysis and targeted imaging, however, the computational cost involving in forming the image volumes makes them prohibitively expensive in case of large scale 3D seismic data acquisition. In this work, we propose the extension of our probing techniques from 2D to 3D to form the extended image volumes efficiently. We show the efficacy of proposed formulation on synthetic data set.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/kumar2015SINBADFaoi/kumar2015SINBADFaoi.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/kumar2015SINBADFaoi/kumar2015SINBADFaoi.mov},
	author = {Rajiv Kumar and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {witte2015SINBADFara,
	title = {Anisotropic RTM applied to field data},
	year = {2015},
	publisher = {SINBAD},
	abstract = {In this talk we present our latest reverse time-migration results of the BP Machar data set, a 2D seismic line from the North sea featuring a large chalk dome. In contrast to our prior imaging attempts, we now account for anisotropic effects in the data using our new TTI pure quasi-P wave modeling code in the time domain.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/witte2015SINBADFara/witte2015SINBADFara.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/witte2015SINBADFara/witte2015SINBADFara.mov},
	author = {Philipp A. Witte and Felix J. Herrmann}
}
@presentation {esser2015SINBADasd,
	title = {Automatic salt delineation {\textemdash} Wavefield Reconstruction Inversion with convex constraints},
	year = {2015},
	note = {posthumously presented by Felix J. Herrmann},
	publisher = {SINBAD},
	abstract = {We extend full-waveform inversion by Wavefield Reconstruction Inversion by including convex constraints on the model. Contrary to the conventional adjoint-state formulations, Wavefield Reconstruction Inversion has the advantage that the Gauss-Newton Hessian is well approximated by a diagonal scaling, which allows us to add convex constraints, such as the box- and the edge-preserving total-variation constraint, on the square slowness without incurring significant increases in computational costs. As the examples demonstrate, including these constraints yields far superior results in complex geological areas that contain high-velocity high-contrast bodies (e.g. salt or basalt). Without these convex constraints, adjoint-state and Wavefield Reconstruction Inversion get trapped in local minima for poor starting models.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/esser2015SINBADasd/esser2015SINBADasd.pdf},
	author = {Ernie Esser and Llu{\'\i}s Guasch and Felix J. Herrmann}
}
@presentation {esser2015SINBADFasd,
	title = {Automatic salt delineation {\textemdash} Wavefield Reconstruction Inversion with convex constraints},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We extend full-waveform inversion by Wavefield Reconstruction Inversion by including convex constraints on the model. Contrary to the conventional adjoint-state formulations, Wavefield Reconstruction Inversion has the advantage that the Gauss-Newton Hessian is well approximated by a diagonal scaling, which allows us to add convex constraints, such as the box- and the edge-preserving total-variation constraint, on the square slowness without incurring significant increases in computational costs. As the examples demonstrate, including these constraints yields far superior results in complex geological areas that contain high-velocity high-contrast bodies (e.g. salt or basalt). Without these convex constraints, adjoint-state and Wavefield Reconstruction Inversion get trapped in local minima for poor starting models.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/esser2015SINBADFasd/esser2015SINBADFasd.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/esser2015SINBADFasd/esser2015SINBADFasd.mov},
	author = {Ernie Esser and Llu{\'\i}s Guasch and Felix J. Herrmann}
}
@presentation {oghenekohwo2015SINBADFcst,
	title = {Comparative study of time-lapse FWI approaches},
	year = {2015},
	publisher = {SINBAD},
	abstract = {I will illustrate the performance of our joint recovery model for time-lapse FWI. Specifically, I will compare our method to other conventional methods, namely, parallel difference and sequential difference FWI. I will illustrate the robustness of our method compared to others especially in the presence of missing data caused by large acquisition gaps. In addition, I will assess the performance of these methods in the detectability of the time-lapse change in synthetic data. Analysis of these methods under different starting FWI model will also be highlighted.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/oghenekohwo2015SINBADFcst/oghenekohwo2015SINBADFcst.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/oghenekohwo2015SINBADFcst/oghenekohwo2015SINBADFcst.mov},
	author = {Felix Oghenekohwo and Felix J. Herrmann}
}
@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 {yang2015SINBADFfis,
	title = {Fast imaging with surface-related multiples: {\textendash} shallow water multiples},
	year = {2015},
	publisher = {SINBAD},
	abstract = {In this talk, we will show surface-related multiples imaging with source estimation. Here the algorithms are based on linearized bregman, and we give the source estimation formulation in this case. With the featured blocked structure of linearized bregman, we can do efficient imaging in straightforward way by randomly choosing sources and frequencies. Another benefit is the simplicity to delivery the algorithm.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/yang2015SINBADFfis/yang2015SINBADFfis.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/yang2015SINBADFfis/yang2015SINBADFfis.mov},
	author = {Mengmeng Yang and Ning Tu and Felix J. Herrmann}
}
@presentation {herrmann2015SINBADfom,
	title = {Fast {\textquotedblleft}online{\textquotedblright} migration with Compressive Sensing},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We present a novel adaptation of a recently developed relatively simple iterative algorithm to solve large-scale sparsity-promoting optimization problems. Our algorithm is particularly suitable to large-scale geophysical inversion problems, such as sparse least-squares reverse-time migration or Kirchoff migration since it allows for a tradeoff between parallel computations, memory allocation, and turnaround times, by working on subsets of the data with different sizes. Comparison of the proposed method for sparse least-squares imaging shows a performance that rivals and even exceeds the performance of state-of-the art one-norm solvers that are able to carry out least-squares migration at the cost of a single migration with all data.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/herrmann2015SINBADfom/herrmann2015SINBADfom.pdf},
	author = {Felix J. Herrmann}
}
@presentation {wang2015SINBADFifw,
	title = {Improving full waveform inversion with spectral extrapolation},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We develop a sparsity based frequency extrapolation method to obtain low frequency data from the high frequency ones. As other frequency extrapolation methods, the extrapolation introduces noise due to dispersion and possibly densely distributed reflectors. To mitigate such noise, we incorporate Curvlet coefficient regularization in the extrapolation algorithm. Numerical results demonstrate the effectiveness of the extrapolation in frequency domain FWI in models of moderate complexity.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/wang2015SINBADFifw/wang2015SINBADFifw.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/wang2015SINBADFifw/wang2015SINBADFifw.mov},
	author = {Rongrong Wang and Felix J. Herrmann}
}
@presentation {wason2015SINBADldr,
	title = {Latest developments in randomized marine acquisition and source separation},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We present the latest developments in randomized (simultaneous) marine acquisition for time-lapse surveys. We also show a comparison between two source separation algorithms {\textemdash} sparsity-promotion and rank-minimization {\textemdash} for simultaneous data.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/wason2015SINBADldr/wason2015SINBADldr.pdf},
	author = {Haneet Wason and Rajiv Kumar and Felix Oghenekohwo and Felix J. Herrmann}
}
@presentation {dasilva2015SINBADFnso,
	title = {A new software organization for 3D FWI},
	year = {2015},
	publisher = {SINBAD},
	abstract = {In this work, I have implemented a scalable and extendable framework for 3D Full Waveform Inversion in Matlab. This approach uses modern software design principles to create a codebase that is easy to understand, maintain, and extend and that also allows for rapid prototyping of new algorithmic ideas that can be easily transferred to large scale problems. The code itself is modularized in a proper way, which allows straightforward testing of each component (e.g., Taylor error test, adjoint tests, etc.). Improvements to the computational kernel (i.e., Helmholtz solves with more efficient matrix-vector products, new preconditioners, etc.) propagate to the entire framework.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/dasilva2015SINBADFnso/dasilva2015SINBADFans.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/dasilva2015SINBADFnso/dasilva2015SINBADFsdd.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}
}
@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 {peters2015SINBADFqpf,
	title = {A quadratic-penalty full-space method for waveform inversion},
	year = {2015},
	publisher = {SINBAD},
	abstract = {In PDE-constrained optimization problems other than geophysical full-waveform inversion, full-space optimization methods are commonly used. This type of optimization method updates both the medium parameters and the wavefields, instead of solving wave equations explicitly. In the FWI context, this means the objective function value and gradient can be obtained at very little computational cost. A major obstacle is, however, the requirement to have all wavefields in memory.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/peters2015SINBADFqpf/peters2015SINBADFqpf.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/peters2015SINBADFqpf/peters2015SINBADFqpf.mov},
	author = {Bas Peters and Felix J. Herrmann}
}
@presentation {wason2015SINBADFrtl,
	title = {Randomization of time-lapse marine surveys},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We present an extension of our time-jittered (simultaneous) marine acquisition for time-lapse surveys, where we work on more realistic field acquisition scenarios by incorporating irregular spatial grids without insisting on repeatability between the surveys. Since we are always subsampled in both the baseline and monitor surveys, we recover the densely sampled baseline and monitor and then the (complete) 4-D difference from subsampled baseline and monitor data.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/wason2015SINBADFrtl/wason2015SINBADFrtl.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/wason2015SINBADFrtl/wason2015SINBADFrtl.mov},
	author = {Haneet Wason and Felix Oghenekohwo and Felix J. Herrmann}
}
@presentation {oghenekohwo2015SINBADrdc,
	title = {Recent developments in compressive sensing for time-lapse studies},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We present an overview of the advances we have made with respect to extending compressive sensing ideas to time-lapse studies. We show applications in compressive time-lapse seismic data acquisition, imaging and full-waveform inversion.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/oghenekohwo2015SINBADrdc/oghenekohwo2015SINBADrdc.pdf},
	author = {Felix Oghenekohwo and Rajiv Kumar and Haneet Wason and Ernie Esser and Ning Tu and Felix J. Herrmann}
}
@presentation {dasilva2015SINBADrii,
	title = {Recent improvements to the 2/3{\textemdash}D imaging \& inversion algorithms in the SLIM software release},
	year = {2015},
	publisher = {SINBAD},
	abstract = {The SLIM software release contains algorithms for frequency domain 2D imaging and inversion based on direct LU and QR factorizations. Recent improvments include significant speedups, by utilizing the latest parallel direct factorization algorithms and inversion strategies which solve for each frequency on its corresponding grid. The iterative Helmholtz solvers available in the software release are now also applicable to 2D problems, extending the 2D algorithms to very large \& high frequency 2D imaging and inversion.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/dasilva2015SINBADrii/dasilva2015SINBADrii.pdf},
	author = {Curt Da Silva and Bas Peters and Felix J. Herrmann}
}
@presentation {peters2015SINBADrwi,
	title = {Regularizing waveform inversion by projections onto convex sets},
	year = {2015},
	publisher = {SINBAD},
	abstract = {A framework is proposed for regularizing the waveform inversion problem by projections onto intersections of convex sets. Multiple pieces of prior information about the geology are represented by multiple convex sets, for example limits on the velocity or minimum smoothness conditions on the model. The data-misfit is then minimized, such that the estimated model is always in the intersection of the convex sets. Therefore, it is clear what properties the estimated model will have at each iteration. This approach does not require any quadratic penalties to be used and thus avoids the known problems and limitations of those types of penalties. It is shown that by formulating waveform inversion as a constrained problem, regularization ideas such as Tikhonov regularization and gradient filtering can be incorporated into one framework. The algorithm is generally applicable, in the sense that it works with any (differentiable) objective function and does not require significant additional computation. The method is demonstrated on the inversion of the 2D marine isotropic elastic synthetic seismic benchmark by Chevron using an acoustic modeling code. To highlight the effect of the projections, we apply no data pre-processing.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/peters2015SINBADrwi/peters2015SINBADrwi.pdf},
	author = {Bas Peters and Brendan R. Smithyman 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 {peters2015SINBADswr,
	title = {Solving WRI{\textquoteright}s data-augmented wave equation in 3D},
	year = {2015},
	publisher = {SINBAD},
	abstract = {The Wavefield Reconstruction Inversion (WRI) approach to seismic inversion is a topic of active research in the SLIM group. Several variants have been proposed, all of which rely on the solution of a linear least-squares problem with a Helmholtz discretization. While algorithms based on LU or QR factorization are very efficient for 2D problems, 3D problems required the development of a factorization-free iterative method. This talk introduces the current version of the algorithm, which requires significantly less memory and computation then a few months ago and uses existing Helmholtz solvers as an important building block.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/peters2015SINBADswr/peters2015SINBADswr.pdf},
	author = {Bas Peters and Chen Greif and Felix J. Herrmann}
}
@presentation {sharan2015SINBADFscu,
	title = {Source collocation using the method of Linearized Bregman},
	year = {2015},
	publisher = {SINBAD},
	abstract = {In this work, we present a method to collocate sources from seismic reflections recorded at receiver locations. We use the method of linearized Bregman. This algorithm focuses unknown sources by promoting sparsity in the {\textquotedblleft}Helmholtz domain{\textquotedblright}{\textemdash}i.e., the wavefield under the action of the the Helmholtz system{\textemdash}under the constraint of a data misfit within a particular tolerance. We extend this method to noisy measurements. We are in particular interested in situations where the noise is coherent because it is in the range of the Helmholtz equation.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/sharan2015SINBADFscu/sharan2015SINBADFscu.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/sharan2015SINBADFscu/sharan2015SINBADFscu.mov},
	author = {Shashin Sharan and Rongrong Wang and Felix J. Herrmann}
}
@presentation {wason2015SINBADFsss,
	title = {Source separation for simultaneous towed-streamer acquisition via compressed sensing},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We show a comparison between two compressed-sensing based source-separation algorithms, i.e., sparsity promotion and rank minimization, for simultaneous data.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/wason2015SINBADFsss/wason2015SINBADFsss.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/wason2015SINBADFsss/wason2015SINBADFsss.mov},
	author = {Haneet Wason and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {witte2015SINBADFtdf,
	title = {Time-domain FWI in TTI media},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We develop an inversion workflow for tilted transverse isotropic (TTI) media using a purely acoustic formulation of the wave equation. The anisotropic modeling kernel is used for the forward modeling operator, as well as for the adjoint Jacobian to back propagate the data residual, thus providing the true gradient of the FWI objective function. We apply this workflow on a synthetic FWI example and perform RTM on a field data set. Furthermore, we discuss alternatives for the pseudo-spectral Laplacian operator of the current implementation.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/witte2015SINBADFtdf/witte2015SINBADFtdf.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/witte2015SINBADFtdf/witte2015SINBADFtdf.mov},
	author = {Philipp A. Witte and Felix J. Herrmann}
}
@presentation {oghenekohwo2015SINBADFtli,
	title = {Time-lapse imaging with multiples and distributed compressed sensing},
	year = {2015},
	publisher = {SINBAD},
	abstract = {In this talk, we leverage our joint-recovery model, which uses the fact that time-lapse images generally have a lot in common, to improve imaging problems with large gaps. We demonstrate that the adverse effects of large acquisition gaps can be mitigated by using this joint-recovery model especially in combination with imaging with surface-related multiples.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/oghenekohwo2015SINBADFtli/oghenekohwo2015SINBADFtli.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/oghenekohwo2015SINBADFtli/oghenekohwo2015SINBADFtli.mov},
	author = {Felix Oghenekohwo 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 {fang2015SINBADwri,
	title = {Wavefield reconstruction inversion with source estimation and its application to 2014 Chevron synthetic blind test dataset},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We present a robust wavefield reconstruction inversion with source estimation. The source wavelet is estimated with the reconstruction of wavefield simultaneously by solving an extended data augmentation problem. We apply this method to the 2014 Chevron synthetic blind test dataset and show the robustness of our method.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/fang2015SINBADwri/fang2015SINBADwri.pdf},
	author = {Zhilong Fang and Felix J. Herrmann}
}
@presentation {fang2015SINBADFwri,
	title = {Wavefield-reconstruction inversion with source estimation and minimum smoothness constraint {\textendash} application to the Chevron 2014 dataset},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We present a robust wavefield reconstruction inversion with source estimation. The source wavelet is estimated with the reconstruction of wavefield simultaneously by solving an extended data-augmented problem. We apply this method to the 2014 Chevron synthetic blind test dataset and show the robustness of our method.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/fang2015SINBADFwri/fang2015SINBADFwri.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/fang2015SINBADFwri/fang2015SINBADFwri.mov},
	author = {Zhilong Fang and Bas Peters and Felix J. Herrmann}
}
@presentation {herrmann2015SINBADFwom,
	title = {Welcome \& Overview of the Meeting},
	year = {2015},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/herrmann2015SINBADFwom/herrmann2015SINBADFwom.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/herrmann2015SINBADFwom/herrmann2015SINBADFwom.mov},
	author = {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 {petrenko2014SINBADaih,
	title = {Accelerating an iterative Helmholtz solver with FPGAs},
	year = {2014},
	publisher = {SINBAD},
	abstract = {An implementation of seismic wave simulation on a platform consisting of a conventional host processor and a reconfigurable hardware accelerator is presented. This research is important in the field of exploration for oil and gas resources, where a 3D model of the subsurface is frequently required. By comparing seismic data collected in a real-world survey with synthetic data generated by simulated waves, it is possible to deduce such a model. However this requires many time-consuming simulations with different Earth models to find the one that best fits the measured data. Speeding up the wave simulations would allow more models to be tried, yielding a more accurate estimate of the subsurface. The reconfigurable hardware accelerator employed in this work is a field programmable gate array (FPGA). FPGAs are computer chips that consist of electronic building blocks that the user can configure and reconfigure to represent their algorithm in hardware. Whereas a traditional processor can be viewed as a pipeline for processing instructions, an FPGA is a pipeline for processing data. The chief advantage of the FPGA is that all the instructions in the algorithm are already hardwired onto the chip. This means that execution time depends only on the amount of data to be processed, and not on the complexity of the algorithm. The main contribution is an implementation of the well-known Kaczmarz row projection algorithm on the FPGA, using techniques of dataflow programming. This kernel is used as the preconditioning step of CGMN, a modified version of the conjugate gradients method that is used to solve the time-harmonic acoustic isotropic constant density wave equation. Using one FPGA accelerator, the current implementation allows seismic wave simulations to be performed over twice as fast, compared to running on one Intel Xeon E5-2670 core. I also discuss the effect of modifications of the algorithm necessitated by the hardware on the convergence properties of CGMN. Finally, a specific plan for future work is set-out in order to fully exploit the accelerator platform, and my work is set in its larger context},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/petrenko2014SINBADaih/petrenko2014SINBADaih.pdf},
	author = {Art Petrenko and Tristan van Leeuwen and Diego Oriato and Simon Tilbury and Felix J. Herrmann}
}
@presentation {peters2014SINBADcda,
	title = {Challenges and developments arising from 2D FWI of a land VSP dataset in the Permian Basin},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Full-waveform inversion was carried out to map 3D velocity structures for a site in the Permian Basin of Texas, USA. The data come from co-located surface and 3D VSP seismic surveys that share common source vibration points. This challenging on-land setting has been used as a testbed to evaluate our FWI algorithms and their effectiveness when applied to real-data problems. We discuss the challenges posed by this case study, and several new techniques and methodological refinements that have been developed and tested as a result. These include: regularization and incorporation of multiple datasets in an on-land FWI workflow; restrictions on the types of models allowed (viz., smoothness regularization) by quadratic penalty and by Projected Quasi-Newton (PQN) inversion; corrections to enable 2D FWI and recovery of 3D models; the state of ongoing work in WRI ({\textquoteleft}{\textquoteleft}Penalty Method{\textquoteright}{\textquoteright} inversion) and the handling of anisotropy.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/peters2014SINBADcda/peters2014SINBADcda.pdf},
	author = {Brendan R. Smithyman and Bas Peters 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 {wang2014SINBADdwi,
	title = {Denoising the wavefield inversion problem through source blending and penalty method},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Previous FWI denoising techniques, such as source blending and stacking, are designed applied to the seismic traces. Hence no physical information is used to improve the fidelity of the reconstruction, even when a good initial model is available. Moreover, traditional source blending requires the existence of common source gathers, which might be missing from the data. In this talk, we incorporate the source blending idea into the penalty method, and extract the synthetic sources corresponding to the principle directions of the gradient update. By blending the sources, we can not only achieve an acceleration and storage efficiency of the penalty method, but also make the reconstruction more robust to the random noise. This method is applicable to any source receiver position but is more efficient if common source gathers are available.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/wang2014SINBADdwi/wang2014SINBADdwi.pdf},
	author = {Rongrong Wang and Felix J. Herrmann}
}
@presentation {herrmann2014SINBADdns,
	title = {DNOISE III {\textendash} the next step},
	year = {2014},
	publisher = {SINBAD},
	abstract = {During this presentation I will discuss our plans for the NSERC Collaborative Research and Development Grant DNOISE III. With this grant, we aim to match the industry contributions of SINBAD dollar-for-dollar.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/herrmann2014SINBADdns/herrmann2014SINBADdns.pdf},
	author = {Felix J. Herrmann}
}
@presentation {zheglova2014SINBADead,
	title = {Exploring applications of depth stepping in seismic inverse problems},
	year = {2014},
	publisher = {SINBAD},
	abstract = {We are exploring applications of stable depth extrapolation with the full wave equation to imaging and inversion. Depth stepping with full wave equation can be advantageous to the time and frequency domain modelling if special care is taken to stabilize the depth exptrapolator efficiently, since it reduces the higher dimensional modelling problem to a number of lower dimensional subproblems. We are interested in exploring applications in inversion, modelling and imaging. For example, just as the reverse time migration can be shown to be the gradient of the reduced formulation of the full waveform inversion problem, it is interesting to explore whether a formulation of the inversion problem can be achieved whose gradient can be computed using depth stepping techniques. We are also interested in such applications as preconditioning of iterative methods for Helmholtz equation and imaging.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/zheglova2014SINBADead/zheglova2014SINBADead.pdf},
	author = {Polina Zheglova and Felix J. Herrmann}
}
@presentation {kumar2014SINBADeia,
	title = {Extended images in action {\textendash}- image gather with surface-related multiples},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Common image gathers are used in building velocity models, invert anisotropy parameters and to analyze reservoir attributes. Often primary reflections are used to form the image gathers and multiples are typically attenuated in processing. However, researchers have shown that, if correctly used, they can provide useful information about the subsurface during reverse-time migration. In this work, i will show how we can use multiples along with primaries to form the image-gathers.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/kumar2014SINBADeia/kumar2014SINBADeia.pdf},
	author = {Rajiv Kumar and Ning Tu and Tristan van Leeuwen and Felix J. Herrmann}
}
@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 {tu2014SINBADfis,
	title = {Fast imaging with surface-related multiples by sparse inversion},
	year = {2014},
	publisher = {SINBAD},
	abstract = {When used correctly, surface-related multiples can provide extra illumination coverage compared with primaries. In this talk, I will discuss how to jointly image primaries and surface-related multiples in a computationally efficient fashion. We bring down the computational cost by two means. First we use wave-equation solvers to implicitly carry out the expensive dense matrix-matrix multiplications in the prediction of surface-related multiples. Second we bring down the simulation cost by subsampling the frequencies and monochromatic source experiments together with curvelet-domain sparsity-promoting and rerandomization. As a result, we obtain true-amplitude least-squares migrated seismic images with computational costs that are comparable to a single RTM with all the data. We demonstrate the efficacy of the proposed method using realistic synthetic examples.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/tu2014SINBADfis/tu2014SINBADfis.pdf},
	author = {Ning Tu 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 {tu2014SINBADind,
	title = {Imaging the Nelson data set using surface-related multiples},
	year = {2014},
	publisher = {SINBAD},
	abstract = {We will present out latest finding on migration with multiples on the Nelson dataset.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/tu2014SINBADind/tu2014SINBADind.pdf},
	author = {Ning Tu 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 {peters2014SINBADiss,
	title = {Iterative solution strategy for least-squares problem with a PDE-block},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Waveform inversion using the Wavefield Reconstruction Inversion (WRI) is a quadratic-penalty based method developed in our group and has shown to have advantages over the reduced Lagrangian based conventional FWI methods. WRI involves no explicit PDE solves, but a least-squares problem with a PDE block. This type of least-squares problem is very challenging to solve using iterative methods. In this talk we show algorithms we developed specifically for this application and which involve a combination of preconditioning, low-rank decomposition and deflation.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/peters2014SINBADiss/peters2014SINBADiss.pdf},
	author = {Bas Peters and Felix J. Herrmann}
}
@presentation {dasilva2014SINBADlss,
	title = {Large-scale seismic data interpolation in a parallel computing environment},
	year = {2014},
	publisher = {SINBAD},
	abstract = {This talk will outline the details for a parallel implementation of the SPG-LR algorithm for matrix completion and its applications to large-scale seismic data interpolation. Previous work done in our group on LR-based matrix factorization has been shown to be extremely efficient for seismic data interpolation that fits in memory. We extend the SPGL1 framework that this method is built on to instances where the full seismic data volume cannot entirely fit in the memory of one computational node and must be run in a distributed environment. We will also look at off-the-grid regularization for tensor completion using a non-uniform FFT operator and examine its effects on interpolating an irregularly sampled, 3D seismic frequency slice.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/dasilva2014SINBADlss/dasilva2014SINBADlss.pdf},
	author = {Curt Da Silva and Felix J. Herrmann}
}
@presentation {herrmann2014SINBADldm,
	title = {Latest developments in marine (4D) acquisition},
	year = {2014},
	publisher = {SINBAD},
	abstract = {During this talk, we will show the advantages of randomly dithered marine acquisition with ocean bottom nodes and how randomization techniques from compressive sensing affect the way we think about time-lapse surveys. This is joint work with Felix and Haneet.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/herrmann2014SINBADldm/herrmann2014SINBADldm.pdf},
	author = {Felix J. Herrmann}
}
@presentation {herrmann2014SINBADldm2,
	title = {Latest developments in randomized (4D) acquisition},
	year = {2014},
	publisher = {SINBAD},
	abstract = {We will present the latest developments in randomized (4D) seismic data acquisition.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/herrmann2014SINBADldm2/herrmann2014SINBADldm2.pdf},
	author = {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 {dasilva2014SINBADlrp,
	title = {Low-rank promoting transformations and tensor interpolation - applications to seismic data denoising},
	year = {2014},
	publisher = {SINBAD},
	abstract = {In this presentation, we extend our previous work in Hierarchical Tucker (HT) tensor completion, which uses an extremely efficient representation for representing high-dimensional tensors exhibiting low-rank structure, to handle subsampled tensors with noisy entries. We consider a {\textquoteleft}low-noise{\textquoteright} case, so that the energies of the noise and the signal are nearly indistinguishable, and a {\textquoteleft}high-noise{\textquoteright} case, in which the noise energy is now scaled to the amplitude of the entire data volume. For the low-noise case in particular, standard trace-by-trace energy comparisons cannot distinguish noise from signal. We examine the behaviour of noise in terms of the singular values along different matricizations of the data, i.e. reshaping of the tensor along different dimensions. By interpreting this effect in the context of tensor completion, we demonstrate the inefficacy of denoising by this method in the source-receiver domain. In light of this observation, we transform the decimated, noisy data in to the midpoint-offset domain, which promotes low-rank behaviour in the signal and high-rank behaviour in the noise. This distinction between signal and noise allows low-rank interpolation to effectively denoise the signal, without knowledge of the noise location, with only a marginal increase in computational cost. We demonstrate the effectiveness of this approach on a 4D frequency slice.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/dasilva2014SINBADlrp/dasilva2014SINBADlrp.pdf},
	author = {Curt Da Silva 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 {peters2014SINBADmpw,
	title = {Multi-parameter waveform inversion; exploiting the structure of penalty-methods},
	year = {2014},
	publisher = {SINBAD},
	abstract = {In this talk I consider the problem of inverting waveforms for multiple medium parameters. The governing PDE is chosen to be the Helmholtz equation with compressibility and buoyancy as the unknowns. Both unknowns occur in the same equation and practice has shown it is very hard to estimate both equally accurate; the buoyancy estimate (or density if a slightly different parametrization is used) is typically much smoother than the compressibility (or velocity). Here I introduce a new waveform inversion algorithm: a full Newton-type method based on a penalty method which adds the PDE constraint as a quadratic penalty term. This method updates both the {\textquoteright}wavefields{\textquoteright} and medium parameters, without explicitly solving PDE{\textquoteright}s. One of the main advantages is the availability of a sparse Hessian and exact gradient which are not the result of any PDE solves. We asses if the availability of the Hessian, which includes information about the coupling between the two medium parameters, can help reconstruct both compressibility and buoyancy.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/peters2014SINBADmpw/peters2014SINBADmpw.pdf},
	author = {Bas Peters 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 {peters2014SINBADqpb,
	title = {Quadratic-penalty based full-space methods for waveform inversion},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Waveform inversion problems are commonly solved by eliminating the field variables. Full-space methods store and update the fields, rather than solving for them. This leads to a problem with nice properties, such as free function value evaluation and exact gradients and Hessians at no cost. The existing literature is almost exclusively based on the Lagrangian formulation. We propose to work with a quadratic penalty formulation, which allows us to reduce the storage requirements and gives the Newton system a favourable structure which we exploit to obtain a intrinsically parallel updating scheme for the fields and medium parameters. We will show a comparison with reduced-space methods.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/peters2014SINBADqpb/peters2014SINBADqpb.pdf},
	author = {Bas Peters and Felix J. Herrmann}
}
@presentation {wason2014SINBADrrt,
	title = {Randomization and repeatability in time-lapse marine acquisition},
	year = {2014},
	publisher = {SINBAD},
	abstract = {During this talk, we will show how randomization techniques from (distributed) compressed sensing affect the way we think about time-lapse surveys.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/wason2014SINBADrrt/wason2014SINBADrrt.pdf},
	author = {Haneet Wason and Felix Oghenekohwo and Felix J. Herrmann}
}
@presentation {oghenekohwo2014SINBADrsw,
	title = {Randomized sampling without repetition in time-lapse seismic surveys},
	year = {2014},
	publisher = {SINBAD},
	abstract = {In this talk, we will show a method for acquiring time-lapse data, where we do not have to repeat the survey geometry. Our method works provided our acquisition is randomized, where sources and receivers are at random locations on a computational grid, and provided we know the spatial locations of the shots and receivers. In addition, we show the implications of either (sub)sampling more for the baseline and less for the monitor or vice-versa, and how this sampling scheme affects the 4-D signal.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/oghenekohwo2014SINBADrsw/oghenekohwo2014SINBADrsw.pdf},
	author = {Felix Oghenekohwo and Haneet Wason and Ernie Esser and Felix J. Herrmann}
}
@presentation {oghenekohwo2014SINBADrst,
	title = {Randomized subsampling in time-lapse surveys and recovery techniques},
	year = {2014},
	publisher = {SINBAD},
	abstract = {This talk centres on new insights on how we think of acquisition of time-lapse data using ideas from compressed sensing. Specifically, we will show how simultaneous processing of time-lapse data from randomized subsampling gives more reliable estimates of the vintages and time-lapse signal, compared to parallel processing.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/oghenekohwo2014SINBADrst/oghenekohwo2014SINBADrst.pdf},
	author = {Felix Oghenekohwo and Rajiv Kumar and Haneet Wason 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 {herrmann2014SINBADrpe,
	title = {Relax the physics \& expand the search space {\textendash} FWI via Wavefield Reconstruction Inversion},
	year = {2014},
	publisher = {SINBAD},
	abstract = {During this talk, we will present a new formulation of full-waveform inversion that combines the best of full-space constrained methods and reduced-space unconstrained methods. Instead of eliminating the constraint, which leads to the reduced adjoint-state method that underpins most formulations of full-waveform inversion, our method relaxes the PDE constraint by replacing it by an additive (least-squares) penalty term. By using the method of variable projection, we arrive at a formulation that alternates between solving for the wavefield, given the velocity model \& data, and solving for velocity-model updates, given the wavefields. We named this method Wavefield Reconstruction Inversion (WRI) because it inverts for the model updates by reconstructing the wavefield everywhere given data observed at the receivers and the physics of the wave equation. During the talk, we present this new method and discuss how the increased search space, now consisting of the wavefields and model, mitigates the effects of local minima. We will also discuss recent extensions including multi-parameter inversion and regularization. This is joint work with Tristan van Leeuwen, Bas Peters, and Ernie Esser.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/herrmann2014SINBADrpe/herrmann2014SINBADrpe.pdf},
	author = {Felix J. Herrmann}
}
@presentation {esser2014SINBADsgp,
	title = {A scaled gradient projection method for total variation regularized full waveform inversion},
	year = {2014},
	publisher = {SINBAD},
	abstract = {We propose a modification to the quadratic penalty formulation for seismic full waveform inversion proposed by van Leeuwen and Herrmann that includes convex constraints on the model. In particular, we show how to simultaneously constrain the total variation of the slowness squared while enforcing bound constraints to keep it within a physically realistic range. Synthetic experiments show that including total variation regularization can improve the recovery of a high velocity perturbation to a smooth background model.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/esser2014SINBADsgp/esser2014SINBADsgp.pdf},
	author = {Ernie Esser and Tristan van Leeuwen and Aleksandr Y. Aravkin and Felix J. Herrmann}
}
@presentation {peters2014SINBADsmp,
	title = {Single- and multi-parameter WRI {\textendash}- synthetic examples},
	year = {2014},
	publisher = {SINBAD},
	abstract = {We demonstrate the performance of WRI on a number of single- and multi-parameter synthetic examples. The examples shot that WRI is less reliant on accurate starting models while this formulation is also conducive to multi-parameter inversion.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/peters2014SINBADsmp/peters2014SINBADsmp.pdf},
	author = {Bas Peters 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 {wason2014SINBADsss,
	title = {Source separation via SVD-free rank-minimization in the hierarchical semi-separable representation},
	year = {2014},
	publisher = {SINBAD},
	abstract = {During this talk, we will show a source separation algorithm for blended marine acquisition, where two sources are deployed at different depths (over/under acquisition). The separation method incorporates the Hierarchical Semi-Separable (HSS) structure inside rank-regularized least-squares formulations. We also compare this deblending scheme with the sparsity-promoting one-norm minimization scheme.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/wason2014SINBADsss/wason2014SINBADsss.pdf},
	author = {Haneet Wason and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {tu2014SINBADsis,
	title = {Sparse inversion simplified},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Implementation of the SPGl1 solver in low-level computer languages such as C or Fortran may sound daunting to many. This talk is tailored for you if you would like to reap benefits from using sparse constraints to regularize your inversion problem, and at the same time want some easy-to-implement algorithm to solve it. We discuss in this talk the linearized Bregman projection method that embraces both ingredients, and show promising applications in fast least-squares imaging and seismic data interpolation problems.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/tu2014SINBADsis/tu2014SINBADsis.pdf},
	author = {Ning Tu and Navid Ghadermarzy and Rajiv Kumar 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 {kumar2014SINBADtjm,
	title = {Time-jittered marine acquisition: low-rank v/s sparsity},
	year = {2014},
	publisher = {SINBAD},
	abstract = {In this work we will show how simultaneous or blended acquisition can be setup as a rank-minimization (deblending) problem. The simultaneous acquisition scenario is a pragmatic time-jittered marine acquisition scheme, where a source vessel (with two airgun arrays) sails across an ocean-bottom array firing at jittered source locations and instances in time, resulting in better spatial sampling and acquisition speedup. We make comparisons with sparsity-promoting based techniques and demonstrate that rank-minimization based debelending techniques are computationally faster and memory efficient. This is joint work with Haneet Wason.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/kumar2014SINBADtjm/kumar2014SINBADtjm.pdf},
	author = {Rajiv Kumar and Haneet Wason and Felix J. Herrmann}
}
@presentation {esser2014SINBADtvc,
	title = {Total variation constrained full waveform inversion with continuation},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Adding a total variation constraint to the Wavefield Reconstruction Inversion model proposed by van Leeuwen and Herrmann can improve robustness and remove artifacts by encouraging piecewise smooth solutions. However, if the parameter controlling the strength of the TV regularization is not well chosen, important details can be lost. We investigate continuation strategies that gradually reduce the strength of the TV constraint so that these important details are eventually allowed back into the solution. Preliminary numerical experiments suggest that this can improve the solution path and lead to better velocity models than if TV regularization were not used during the intermediate steps.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/esser2014SINBADtvc/esser2014SINBADtvc.pdf},
	author = {Ernie Esser and Llu{\'\i}s Guasch and Tristan van Leeuwen and Aleksandr Y. Aravkin 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 {oghenekohwo2014SINBADuwc,
	title = {Use what{\textquoteleft}s in common: time-lapse FWI with distributed Compressive Sensing},
	year = {2014},
	publisher = {SINBAD},
	abstract = {In this talk, we will extend our joint recovery formulation to FWI in time-lapse seismic, using the modified Gauss-Newton approach. Leveraging ideas from distributed compressive sensing, we exploit shared information in the sparse model perturbations of the baseline and monitor data.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/oghenekohwo2014SINBADuwc/oghenekohwo2014SINBADuwc.pdf},
	author = {Felix Oghenekohwo and Rajiv Kumar and Ernie Esser and Felix J. Herrmann}
}
@presentation {herrmann2014SINBADwri,
	title = {Wavefield Reconstruction Inversion (WRI) - a new take on wave-equation based inversion},
	year = {2014},
	publisher = {SINBAD},
	abstract = {We discuss a recently proposed novel method for waveform inversion: Wavefield Reconstruction Inversion (WRI). As opposed to conventional FWI {\textendash}- which attempts to minimize the error between observed and predicted data obtained by solving a wave equation {\textendash}- WRI reconstructs a wave-field from the data and extracts a model-update from this wavefield by minimizing the wave-equation residual. The method does not require explicit computation of an adjoint wavefield as all the necessary information is contained in the reconstructed wavefield. We show how the corresponding model updates can be interpreted physically analogously to the conventional imaging-condition-based approach.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/herrmann2014SINBADwri/herrmann2014SINBADwri.pdf},
	author = {Felix J. Herrmann}
}
@presentation {ghadermarzy2014SINBADwrr,
	title = {Wavefield reconstruction via randomized sampling},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Regular sampling along the time axis is a common approach for seismic imaging. However, seismic data are often spatially undersampled due to economical reasons as well as ground surface limitations. Operational conditions might result in noisy traces or even gaps in coverage and irregular sampling which often leads to image artifacts. Seismic trace interpolation aims to interpolate missing traces in an otherwise regularly sampled seismic data to a complete regular grid. In this talk, we explain some techniques, with low computational complexity, that can be used to improve the results. In particular, we talk about weighting, switching to midpoint-offset domain from frequency-space domain, and uniform versus jittered undersampling. We illustrate the advantages of these modifications using a real seismic line from the Gulf of Suez.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/ghadermarzy2014SINBADwrr/ghadermarzy2014SINBADwrr.pdf},
	author = {Navid Ghadermarzy and Ozgur Yilmaz and Felix J. Herrmann}
}
@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 {petrenko2013SINBADaih,
	title = {Accelerating an iterative Helmholtz solver with FPGAs},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Solution of the Helmholtz equation is the main computational burden of full-waveform inversion in the frequency domain. For this task we employ the CARP-CG algorithm (Gordon \& Gordon 2010), an iterative solver that preconditions the original Helmholtz system into an equivalent symmetric positive definite system and then applies the method of conjugate gradients. Forming the matrix for the new system is not necessary as its multiplicative action on a vector is implemented using a series of projections onto the rows of the original system. Our contribution is implementing CARP-CG for a host + accelerator (FPGA) computing environment. The computational paradigm is one of dataflow: vector and matrix elements are streamed from memory through the accelerator which applies the row projections. The advantage of an FPGA to process streams of data is that unless the algorithm is memory bandwidth limited, computation time is directly proportional to the amount of data. The complexity of the algorithm implemented on the FPGA is irrelevant since all the operations programmed onto the FPGA happen in the same clock tick. In contrast, on a CPU, more complex algorithms require more clock ticks as the instructions are executed sequentially, or with only a small amount of parallelism. Ongoing work porting the CARP-CG algorithm to the accelerator is presented.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/petrenko2013SINBADaih/petrenko2013SINBADaih.pdf},
	author = {Art Petrenko and Tristan van Leeuwen and Felix J. Herrmann and Diego Oriato and Simon Tilbury}
}
@presentation {esser2013SINBADasr,
	title = {Applications of phase retrieval methods to blind seismic deconvolution},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Phase retrieval is the non-convex optimization problem of recovering a signal from magnitudes of complex linear measurements. Solving convex semi-definite program (SDP) relaxations has been shown to be a robust approach, but it remains too expensive to apply to large problems. We will discuss methods for accelerating computations and explore applications to seismic deconvolution.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/esser2013SINBADasr/esser2013SINBADasr.pdf},
	author = {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 {hargreaves2013SINBADbfo,
	title = {The bridge from orthogonal to redundant transforms and weighted $\ell_1$ optimization},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Traditional arguments in synthesis $\ell_1$ -optimization require our forward operator to be orthogonal, though we use redundant transforms in practice. These traditional arguments do not translate to redundant transforms, and other arguments require impractical conditions on our effective measurement matrix. Recent theory in one-norm analysis, namely the optimal dual $\ell_1$ analysis of Shidong et al, have provided point-wise reconstruction error estimates for synthesis using an equivalence relationship where we can use weaker assumptions. This exposes an important model assumption indicating why analysis might outperform synthesis, for which careful consideration in seismic is necessary, and the need for models such as the cosparse model. In this talk we will discuss these ideas, provide evidence which indicates this theory should generalize to uniform error estimates(and thus not signal dependent), and how redundancy, support information, and weighting play important roles.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/hargreaves2013SINBADbfo/hargreaves2013SINBADbfo.pdf},
	author = {Brock Hargreaves and Ozgur Yilmaz and Felix J. Herrmann}
}
@presentation {yeung2013SINBADcsr,
	title = {Compressed sensing, recovery of signals using random Turbo matrices},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Compressed sensing is an emerging technique that allows us to recover an image using far fewer number of measurements than classical sampling techniques. Designing the measurement matrices with certain properties are critical to this task. Gaussian matrices are most commonly used. We discover a new class of random matrices that can outperform the Gaussian matrices when we are in a situation of taking an outrageously small number of samples.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/yeung2013SINBADcsr/yeung2013SINBADcsr.pdf},
	author = {Enrico Au-Yeung and Ozgur Yilmaz and Felix J. Herrmann}
}
@presentation {tu2013SINBADrerand,
	title = {Controlling linearization errors with rerandomization},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Least squares migration aims to fit the observed seismic data with data predicted by linearized modelling, by solving an PDE-constrained optimization problem. This problem is challenging mostly because of its prohibitive computational cost. To address the issue, dimensionality reduction techniques were proposed in the literature. However, the solution of the reduced problem can deviate from that of the full problem when there are components in the observed data that cannot be explained by linearized modelling. We solve the problem by rerandomizing our $\ell_1$ regularized inversion. In this presentation, I will explain the method and demonstrate what we can achieve with rerandomization, especially for resolving fine sub-salt structures.},
	keywords = {Presentation, SINBAD, SINBADSPRING2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/tu2013SINBADrerand/tu2013SINBADrerand.pdf},
	author = {Ning Tu 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 {macedo2013SINBADdap,
	title = {A dual approach to PhaseLift via gauge programming and bundle methods},
	year = {2013},
	publisher = {SINBAD},
	abstract = {A feature common to many sparse optimization problems is that the number of variables may be significantly larger than the number of constraints- e.g., the matrix-lifting approach taken by PhaseLift for phase retrieval results in a problem where the number of variables is quadratic in the number of constraints. We consider a duality framework and numerical methods to leverage the relatively small number of constraints. Preliminary numerical results illustrate our approach and its flexibility.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/macedo2013SINBADdap/macedo2013SINBADdap.pdf},
	author = {Ives Macedo and Michael P. Friedlander and Felix J. Herrmann}
}
@presentation {oghenekohwo2013SINBADedt,
	title = {Estimating 4D differences in time-lapse using randomized sampling techniques},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Repeatability in the seismic survey and processing has been cited as the main reason for which 4D seismic technology work. In the last decade, concerted efforts have been spent to make the 4D seismic process highly repeatable, without significant success. On the contrary, Compressed Sensing, which is also a relatively new sampling paradigm proposes that one can recover an estimate of a fully sampled signal from a noisy, under-sampled measurements provided the acquisition architecture satisfies some properties. By observing different under-sampled and random measurements from each vintage, corresponding to different acquisition geometries, we show that one can still detect the 4D change in time using recent ideas from Compressed sensing. Using a realistic synthetic model, we show two methods of estimating the 4D difference and compare their relative performance to each other.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/oghenekohwo2013SINBADedt/oghenekohwo2013SINBADedt.pdf},
	author = {Felix Oghenekohwo and Ernie Esser and Felix J. Herrmann}
}
@presentation {peters2013SINBADepm,
	title = {Examples from the penalty-method},
	year = {2013},
	publisher = {SINBAD},
	abstract = {A novel penalty method for PDE-constrained optimization was recently proposed by van Leeuwen and Herrmann (2013). The conventional PDE-constrained optimization formulation used in seismic waveform inversion is based on calculating the gradient of the data misfit objective functional via the adjoint-state method, at the cost of two PDE solves. The penalty method requires only one solution of an overdetermined linear system, in the least-squares sense. In this talk some numerical properties of this linear system will be exposed. The penalty method for PDE-constrained optimization involves a parameter, balancing the data misfit and PDE-misfit parts of the objective functional. This talk will address how to select this very important parameter. Some examples will be shown in which the penalty method outperforms the conventional method in non-linear waveform inversion, as well as linearized seismic imaging by migration.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/peters2013SINBADepm/peters2013SINBADepm.pdf},
	author = {Bas Peters and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {herrmann2013SINBADeia,
	title = {Extended images in action},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Image gathers as a function of subsurface offset are an important tool for migration-velocity and amplitude-versus-angle analysis in areas of complex geology. Traditionally, these gathers are thought of as multidimensional correlations of the source and receiver wavefields. The bottleneck in computing the gathers lies in the fact that one needs to store and compute these wavefields and in correlating the wavefields to obtain the desired image gathers. Therefore, the image gathers are typically only computed for a limited number of subsurface points and for a limited range of subsurface offsets. In this presentation, we offer a new perspective on such gathers by organizing the extended image as a function of all subsurface offsets for all subsurface points in a matrix whose (i,j) entry captures the interaction between gridpoints i and j. Of course, it is infeasible to form and store this matrix. Instead, we propose an efficient algorithm to glean information from the image volume via the action of matrix-vector products with this matrix. We illustrate how this can be used to (i) form conventional image gathers and construct objective functions for automated MVA and (ii) calculate true two-way wave-equation angle gathers that allow us to carry out linearized angle-versus-offset inversion with geologic-dip correction. This is joint work with Tristan van Leeuwen and Rajiv Kumar.},
	keywords = {Presentation, SINBAD, SINBADSPRING2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/herrmann2013SINBADeia/herrmann2013SINBADeia.pdf},
	author = {Felix J. Herrmann}
}
@presentation {kumar2013SINBADava,
	title = {Extended images in action: efficient AVA via probing},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Common image gathers (CIG) are an important tool to perform AVA analysis in areas of complex geology. Unfortunately, it is prohibitively very expensive to compute these CIG for all the subsurface points. In this work, we present an efficient way to compute CIG for all subsurface offsets without explicitly calculating the source and receiver wavefields for all the sources. Because the CIG contain all possible subsurface offsets, we compute the angle-domain image gathers by selecting the subsurface offset that is aligned with the local geologic dip. We propose a method to compute the local dip information directly from common-image-point gathers. To assess the quality of the angle-domain common-image-points gathers we compute the angle-dependent reflectivity coefficients and compare them with theoretical reflectivity coefficients yielded by the (linearized) Zoeppritz equations for a few synthetic models.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/kumar2013SINBADava/kumar2013SINBADava.pdf},
	author = {Rajiv Kumar and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {kumar2013SINBADeia,
	title = {Extended images in action: efficient WEMVA via randomized probing},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Extended images as a function of the full subsurface offset are an important tool to perform wave-equation based migration velocity analysis (WEMVA) in areas of complex geology. Unfortunately, computation \& storage of these extended images is prohibitively expensive. In this work, we present an efficient way to compute extended images for all subsurface offsets without explicitly calculating the source and receiver wavefields for all the sources. Instead, we calculate actions of extended image volumes on probing vectors that live in the image space. The probing can either be defined as vectors from the Dirac basis, which allows us to form the extended image at the location of the point diffractor, or they can be defined in terms of Gaussian noise. The latter corresponds to sources with random weights firing simultaneously at every grid point. We demonstrate that this probing leads to a computationally efficient implementation of WEMVA. This is joint work with Tristan van Leeuwen.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/kumar2013SINBADeia/kumar2013SINBADeia.pdf},
	author = {Rajiv Kumar and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {miao2013SINBADfid,
	title = {Fast imaging via depth stepping with the two-way wave equation},
	year = {2013},
	publisher = {SINBAD},
	abstract = {In this presentation we propose a fast imaging algorithm via depth stepping with the two-way wave equation. Within the framework of survey sinking, a stabilized depth extrapolation operator is computed using a spectral projector which can efficiently split evanescent wave components. The computation of the spectral projector features with an Hierarchically Semi-Seperable (HSS) matrix representation speeded up polynomial recursion, resulting in an accleration from cubic numerical complexity to linear numerical complexity.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/miao2013SINBADfid/miao2013SINBADfid.pdf},
	author = {Lina Miao and Felix J. Herrmann}
}
@presentation {tu2013SINBADfim,
	title = {Fast imaging with multiples and source estimation},
	year = {2013},
	publisher = {SINBAD},
	abstract = {During this talk, we present a computationally efficient (cost of 1-2 RTMs with all data) iterative sparsity-promoting inversion framework where surface-related multiples are jointly imaged with primaries and where the source signature is estimated on the fly. Our imaging algorithm is computationally efficient because it works during each iteration with small independent randomized subsets of data. The multiples are handled by introducing an areal source term that includes the upgoing wavefield. We update the source signature for each iteration using a variable projection method. The resulting algorithm removes imaging artifacts from surface-related multiples, estimates and removes the imprint of the source, recovers true amplitudes, is fast, and robust to linearization errors by virtue of the statistical independence of the subsets of data we are working with at each iteration.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/tu2013SINBADfim/tu2013SINBADfim.pdf},
	author = {Ning Tu and Felix J. Herrmann}
}
@presentation {tu2013SINBADvp,
	title = {Fast imaging with multiples and source estimation},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Multiples are usually treated as unwanted components in seismic data. However, if correctly used, they can provide valuable information about the subsurface. In this presentation, I will talk about how to make use of multiples in $\ell_1$ regularized least-squares imaging, and how to estimate the source wavelet on the fly using multiples. Synthetic examples show that using multiples not only helps to retrieve the true-amplitude seismic image and the source wavelet, it also increases the wavenumber contents in the seismic image. By using dimensionality reduction techniques with rerandomization, we also greatly decrease the simulation cost without compromising the image quality.},
	keywords = {Presentation, SINBAD, SINBADSPRING2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/tu2013SINBADvp/tu2013SINBADvp.pdf},
	author = {Ning Tu and Aleksandr Y. Aravkin and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {herrmann2013SINBADfwi,
	title = {Frugal FWI},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Seismic waveform inversion aims at obtaining detailed estimates of subsurface medium parameters, such as soundspeed, from seismic data. A formulation in the frequency-domain leads to an optimization problem constrained by a Helmholtz equation with many right-hand- sides. Application of this technique in 3D precludes the use of factorization techniques to solve the Helmholtz equation due the large number of gridpoints and the bandwidth of the matrix. While many sophisticated pre-conditioned iterative techniques have been developed for the Helmholtz equation, they often include model-specific tuning parameters and are thus not very attractive for inversion since the medium parameters change from one iteration to the next. In this paper, we propose a method for 3D seismic waveform inversion that addresses both the need to efficiently solve the Helmholtz equation as well as the computational cost induced by the many right-hand-sides. To solve the Helmholtz equation, we consider a simple generic preconditioned iterative method (CARP-CG) that is well-suited for inversion because of its robustness. We extend this method to a block-iterative method that can efficiently handle multiple right-hand sides. To reduce the computational cost of of the overall optimization procedure, we use recently proposed techniques from stochastic optimization that allow us to work with approximate gradient information. These approximations are obtained by evaluating only a small portion of the right-hand sides and/or by solving the PDE approximately. We propose heuristics to adaptively determine the required accuracy of the PDE solves and the sample-size and illustrate the algorithms on synthetic benchmark models. This is joint work with Tristan van Leeuwen.},
	keywords = {Presentation, SINBAD, SINBADSPRING2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/herrmann2013SINBADfwi/herrmann2013SINBADfwi.pdf},
	author = {Felix J. Herrmann}
}
@presentation {herrmann2013SINBADffwi,
	title = {Frugal FWI},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Seismic waveform inversion aims at obtaining detailed estimates of subsurface medium parameters, such as soundspeed, from seismic data. A formulation in the frequency-domain leads to an optimization problem constrained by a Helmholtz equation with many right-hand sides. Application of this technique in 3D precludes the use of factorization techniques to solve the Helmholtz equation due the large number of gridpoints and the bandwidth of the matrix. While many sophisticated pre-conditioned iterative techniques have been developed for the Helmholtz equation, they often include model-specific tuning parameters and are thus not very attractive for inversion since the medium parameters change from one iteration to the next. In this paper, we propose a method for 3D seismic waveform inversion that addresses both the need to efficiently solve the Helmholtz equation as well as the computational cost induced by the many right-hand sides. To solve the Helmholtz equation, we consider a simple generic preconditioned iterative method (CARP-CG) that is well-suited for inversion because of its robustness. We extend this method to a block-iterative method that can efficiently handle multiple right-hand sides. To reduce the computational cost of of the overall optimization procedure, we use recently proposed techniques from stochastic optimization that allow us to work with approximate gradient information. These approximations are obtained by evaluating only a small portion of the right-hand sides and/or by solving the PDE approximately. We propose heuristics to adaptively determine the required accuracy of the PDE solves and the sample-size and illustrate the algorithms on synthetic benchmark models. This is joint work with Tristan van Leeuwen.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/herrmann2013SINBADffwi/herrmann2013SINBADffwi.pdf},
	author = {Felix J. Herrmann}
}
@presentation {dasilva2013SINBADSPRhtuck,
	title = {Hierarchical Tucker tensor optimization - applications to 4D seismic data interpolation},
	year = {2013},
	publisher = {SINBAD},
	abstract = {In this work, we develop optimization algorithms on the manifold of Hierarchical Tucker (HT) tensors, an extremely efficient format for representing high-dimensional tensors exhibiting particular low-rank structure. With some minor alterations to existing theoretical developments, we develop an optimization framework based on the geometric understanding of HT tensors as a smooth manifold, a generalization of smooth curves/surfaces. Building on the existing research of solving optimization problems on smooth manifolds, we develop Steepest Descent and Conjugate Gradient methods for HT tensors. The resulting algorithms converge quickly, are immediately parallelizable, and do not require the computation of SVDs. We also derive efficient Gauss-Newton based algorithms which converge much faster than standard, first-order methods. We also extend ideas about favourable sampling conditions for missing-data recovery from the field of Matrix Completion to Tensor Completion and demonstrate how the organization of data can affect the success of recovery. As a result, if one has data with randomly missing source pairs, using these ideas, coupled with an efficient solver, one can interpolate large-scale seismic data volumes with missing sources and/or receivers by exploiting the multidimensional dependencies in the data. We are able to recover data volumes amidst extremely high subsampling ratios (in some cases, > 75\%) using this approach and we demonstrate our recovery on a synthetic 5D data set provided to us by BG.},
	keywords = {3D data interpolation, hierarchical tucker, Presentation, SINBAD, SINBADSPRING2013, SLIM, structured tensor},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/dasilva2013SINBADSPRhtuck/dasilva2013SINBADSPRhtuck.pdf},
	author = {Curt Da Silva and Felix J. Herrmann}
}
@presentation {zheglova2013SINBADihss,
	title = {Imaging with hierarchical semi separable matrices},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Hierarchically Semi Separable (HSS) matrices are (in general) dense matrices that have low rank off-diagonal blocks that can be represented economically. Exploiting a specific structure of HSS representation, fast algorithms have been devised for matrix matrix multiplication, addition and computing a matrix inverse. We are interested in developing fast algorithms for seismic imaging using ideas from this approach. An overview of HSS representation and some methods using HSS representations of operators will be shown in this talk.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/zheglova2013SINBADihss/zheglova2013SINBADihss.pdf},
	author = {Polina Zheglova 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 {herrmann2013SINBADldc,
	title = {Latest developments on the Chevron GOM and other datasets},
	year = {2013},
	publisher = {SINBAD},
	abstract = {During this talk, we will give a brief overview on our imaging and FWI results on various synthetic and field datasets we have been working on. This is joint work with Andrew Calvert, Brendan R. Smithyman, and the SLIM team.},
	keywords = {Presentation, SINBAD, SINBADSPRING2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/herrmann2013SINBADldc/herrmann2013SINBADldc.pdf},
	author = {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 {akalin2013SINBADmtc,
	title = {Matrix and tensor completion for large-scale seismic interpolation: a comparative study},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Owing to their high dimensionality, interpolating 3D seismic data volumes remains a computationally daunting task. In this work, we outline a comprehensive framework for sampling and interpolating such volumes based on the well-understood theory of Matrix and Tensor completion. This interpolation theory consists of three components major signal structure, structure-destroying sampling, and structure-restoring optimization. By viewing interpolation in the context of this theory, we are able to specify exactly when these approaches are expected to perform well. We also introduce structure-revealing transformations that promote the inherent low-rank structure in seismic data as well as a factorization approach that scales to large problem sizes. Our methods are able to handle large-scale data volumes more accurately and more quickly compared to other more ad-hoc approaches, as we will demonstrate. This is joint work with Curt Da Silva, Rajiv Kumar, Ben Recht, and Felix J. Herrmann.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/akalin2013SINBADmtc/akalin2013SINBADmtc.pdf},
	author = {Okan Akalin and Curt Da Silva and Rajiv Kumar and Ben Recht and Felix J. Herrmann}
}
@presentation {herrmann2013SINBADmlm,
	title = {Mitigating local minima in full-waveform inversion by expanding the search space with the penalty method},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Wave-equation based inversions, such as full-waveform inversion, are challenging because of their computational costs, memory requirements, and reliance on accurate initial models. To confront these issues, we propose a novel formulation of full-waveform inversion based on a penalty method. In this formulation, the objective function consists of a data-misfit term and a penalty term which measures how accurately the wavefields satisfy the wave-equation. Because we carry out the inversion over a larger search space, including both the model and synthetic wavefields, our approach suffers less from local minima. Our main contribution is the development of an efficient optimization scheme that avoids having to store and update the wavefields by explicit elimination. Compared to existing optimization strategies for full-waveform inversion, our method differers in two main aspects; i. The wavefields are solved from an augmented wave-equation, where the solution is forced to solve the wave-equation and fit the observed data, ii. no adjoint wavefields are required to update the model, which leads to significant computational savings. We demonstrate the validity of our approach by carefully selected examples and discuss possible extensions and future research. This is joint work with Tristan van Leeuwen.},
	keywords = {Presentation, SINBAD, SINBADSPRING2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/herrmann2013SINBADmlm/herrmann2013SINBADmlm.pdf},
	author = {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 {wang2013SINBADnru,
	title = {Noise reduction by using interferometric measurements},
	year = {2013},
	publisher = {SINBAD},
	abstract = {The interferometric formulation of linear wave-based inversion problem was proposed by Demanet and Jugnon recently. Instead of directly fitting the data, they proposed to fit a subset of the data{\textquoteright}s cross-correlation. It can be verified that if the full cross-correlation is used, then the problem is equivalent to the usual least square problem. The subsampling, which is usually considered to cause instability to the solution, is surprisingly useful in this setting. Numerical experiments for the inverse source problem and the inverse scatting problem have both suggested that a {\textquoteright}good{\textquoteright} sampling strategy can actually increase the stability under modeling error caused by the uncertainty of a kinetic nature. We will study the mathematical mechanism behind this phenomenon, and try to see whether or not there exists a universally {\textquoteright}good{\textquoteright} sampling strategy independent of the types of forward operators we use.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/wang2013SINBADnru/wang2013SINBADnru.pdf},
	author = {Rongrong Wang and Bas Peters and Felix J. Herrmann}
}
@presentation {fang2013SINBADp3dfwi,
	title = {Parallel 3D FWI with simultaneous shots},
	year = {2013},
	publisher = {SINBAD},
	abstract = {In this work, we manage to build the workflow for the parallel 3D full-waveform inversion. In the forward simulation part, we generate the Helmholtz matrix in parallel and use parallel CARPCG to solve the Helmholtz equation. In the inversion process, simultaneous shots are used to reduce the computational costs. Additionally, we propose a method to select the number of simultaneous shots and tolerance of CARPCG dynamically, to reach a compromise between the computational costs and accuracy.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/fang2013SINBADp3dfwi/fang2013SINBADp3dfwi.pdf},
	author = {Zhilong Fang and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {smithyman2013SINBADprb,
	title = {Phase-residual based quality-control methods and techniques for mitigating cycle skips},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Most full-waveform inversion algorithms use local optimization methods to iteratively improve the numerical earth model. All of these make the implicit assumption that the the model close to the true earth to avoid cycle. In practice this may not be true. We explore two questions: 1. How do we understand and visualize the cycle skip phenomenon in order to recognize it if it exists? 2. How do we automate this quality control step and use the rich information from multiple data to mitigate cycle skips and avoid local minima?},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/smithyman2013SINBADprb/smithyman2013SINBADprb.pdf},
	author = {Brendan R. Smithyman and Felix J. Herrmann}
}
@presentation {pong2013SINBADppg,
	title = {The proximal-proximal gradient algorithm},
	year = {2013},
	publisher = {SINBAD},
	abstract = {In many applications, one has to minimize the sum of a smooth loss function modeling misfit and a regularization term inducing structures. In this talk, we consider the case when the regularization is a composition of a convex function, whose proximal mapping is easy to compute, and a nonzero linear map. Such instances arise in system identification and realization problems. In this talk, we present a new algorithm, the proximal-proximal gradient algorithm, which admits easy subproblems. Our algorithm reduces to the proximal gradient algorithm if the linear map is just the identity map, and can be viewed as "very inexact" inexact proximal gradient algorithm. We show that the whole sequence generated from the algorithm converges to an optimal solution, and establish an upper bound on iteration complexity.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/pong2013SINBADppg/pong2013SINBADppg.pdf},
	author = {Ting Kei Pong and Michael P. Friedlander and Felix J. Herrmann}
}
@presentation {nutini2013SINBADpcb,
	title = {Putting the curvature back into sparse solvers},
	year = {2013},
	publisher = {SINBAD},
	abstract = {For many problems in signal and image processing, we seek a sparse solution that solves that approximately solves the problem Ax $\approx$ b, where A is an m-by-n matrix and b is an m-vector. Many of the most used approaches to problem thissuch as iterative soft thresholding SPGL1 andare first-order methods. As a result, these methods can sometimes be slow to converge. In this talk, we present an approach that takes advantage of the easily-obtainable second-order information. By exploiting this available second-order information, we are able to put the curvature back into sparse solvers and improve upon the convergence rates of existing solvers.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/nutini2013SINBADpcb/nutini2013SINBADpcb.pdf},
	author = {Julie Nutini and Michael P. Friedlander and Felix J. Herrmann}
}
@presentation {vanleeuwen2013SINBADrpp,
	title = {Relaxing the physics: a penalty method for full-waveform inversion},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Computationally efficient method of solving partial differential equation (PDE)-constrained optimization problems that occur in (geophysical) inversion problems. The method takes measured data from a physical system as input and minimizes an objective function that depends on an unknown model for the physical parameters, fields, and additional nuisance parameters such as the source function. The invention consists of a minimization procedure involving a cost functional comprising of a data-misfit term and a penalty term that measures how accurately the fields satisfy the PDE. The method is composed of two alternating steps, namely the solution of a system of equations forming the discretization of the data-augmented PDE, and the solution of physical model parameters from the PDE itself given the field that solves the data-augmented system and an estimate for the sources. Compared to all-at-once approaches to PDE-constrained optimization, there is no need to update and store the fields for all sources leading to significant memory savings. As in the all-at-once approach, the proposed method explores a larger search space and is therefore less sensitive to initial estimates for the physical model parameters. Contrary to the reduced formulation, the proposed method does not require the solution of an adjoint PDE, effectively halving the number of PDE solves and memory requirement. As in the reduced formulation, fields can be computed independently and aggregated, possibly in parallel.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/vanleeuwen2013SINBADrpp/vanleeuwen2013SINBADrpp.pdf},
	author = {Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {kumar2013SINBADhss,
	title = {Seismic data interpolation via low-rank matrix factorization in the hierarchical semi-separable representation},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Recent developments in matrix rank optimization have allowed for new computational approaches in the field of seismic data interpolation. One of the main requirements of exploiting rank-minimization approaches is that the target data set should exhibit a low-rank structure. Seismic frequency slices exhibit low-rank structure at the low-frequencies, but not at the high frequencies. This behavior is due to the increase in oscillations as we move from low to high-frequency slices, even though the energy remains focused around the diagonal. Therefore, interpolation via rank minimization in the high-frequency range requires extended formulations that incorporate low-rank structure. We propose an approach for seismic data interpolation which incorporates the Hierarchical Semi-Separable Structure (HSS) inside rank-regularized least-squares formulations for the missing-trace interpolation problem. The proposed approach is suitable for large scale problems, since it avoids SVD computations and uses a low-rank factorized formulation instead. We illustrate the advantages of the new HSS approach by interpolating a seismic line from the Gulf of Suez and compare the reconstruction with conventional rank minimization.},
	keywords = {Presentation, SINBAD, SINBADSPRING2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/kumar2013SINBADhss/kumar2013SINBADhss.pdf},
	author = {Rajiv Kumar and Hassan Mansour and Aleksandr Y. Aravkin and Felix J. Herrmann}
}
@presentation {tu2013SINBADmachar,
	title = {SLIM{\textquoteright}s findings on the Machar dataset},
	year = {2013},
	publisher = {SINBAD},
	abstract = {By courtesy of BP, we are able to get some hands-on experience imaging seismic data from the Machar field in North Sea. We performed reverse-time migration and sparsity-promoting migration with source-estimation to this dataset (or a cropped section of the dataset in many cases due to computational constraints), and had some interesting findings. In this presentation, we will show some conclusive results we have got, and explain the key techniques in imaging this dataset.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/tu2013SINBADmachar/tu2013SINBADmachar.pdf},
	author = {Ning Tu and Felix J. Herrmann}
}
@presentation {vanleeuwen2013SINBADsda,
	title = {Solving the data-augmented wave equation},
	year = {2013},
	publisher = {SINBAD},
	abstract = {The recently proposed penalty method promises to mitigate some of the non-linearity inherent in full-waveform inversion by relaxing the requirement that the wave-equation needs to be solved exactly. The basic workflow of this new method is as follows; i) solve an overdetermined wave-equation (the data-augmented wave-equation), where the data serves as additional constraints for the wavefields, ii) compute the wavefield-residual by substituting this wavefield in the wave-equation, and iii) correlate the wavefield with the wavefield-residual to obtain a model-update. As opposed to the conventional workflow, no explicit adjoint solve is needed to compute the model-update. However, instead of solving a wave-equation, we need to solve a data-augmented wave-equation. In this talk we explore some of the challenges of solving this data-augmented wave-equation and review some possible solution strategies for both time and frequency-domain applications.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/vanleeuwen2013SINBADsda/vanleeuwen2013SINBADsda.pdf},
	author = {Tristan van Leeuwen and Bas Peters and Felix J. Herrmann}
}
@presentation {dasilva2013SINBADstf,
	title = {Structured tensor formats for missing-trace interpolation and beyond},
	year = {2013},
	publisher = {SINBAD},
	abstract = {High-dimensional data, alternatively known as tensors, occurs in a variety of seismic problems. By exploiting the fact that seismic data can be well represented as a structured tensor, we design algorithms that operate on much lower dimensional parameters. In this talk, we will review some recent developments in interpolating seismic data volumes in the so-called Hierarchical Tucker format as well as demonstrate the need for such formats when tackling high-dimensional problems such as Uncertainty Quantification.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/dasilva2013SINBADstf/dasilva2013SINBADstf.pdf},
	author = {Curt Da Silva 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 {goh2013SINBADttt,
	title = {Taming time through tangents},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Given two vectors of (possibly) different lengths, the edit distance considers all possible alignments between the two and picks the one that minimizes the number of operations needed to turn one into the other. Though highly non-smooth and riddled with local minima, we show a way to compute the convex envelope of this function, which opens the door to using the approximate edit distance as a surrogate for the L2 distance and comparing vectors of different lengths.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/goh2013SINBADttt/goh2013SINBADttt.pdf},
	author = {Gabriel Goh and Michael P. Friedlander and Felix J. Herrmann}
}
@presentation {wason2013SINBADtjm,
	title = {Time-jittered marine sources},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Current efforts towards dense shot (and/or receiver) sampling and full azimuthal coverage to produce higher-resolution images have led to the deployment of multiple source vessels across the survey area. A step ahead from multi-source seismic acquisition is simultaneous or blended acquisition where different source arrays/vessels fire shots at near-simultaneous or slightly random times. Seismic data acquisition with simultaneous (or blended) sources has helped improve acquisition efficiency and mitigate acquisition related costs. Deblending then aims to recover unblended data, as acquired during conventional acquisition, from blended data since many processing techniques rely on full, regular sampling. We present a simultaneous/blended marine acquisition setup where shots fire at significantly jittered instances in time resulting in jittered shot locations for a given speed of the source vessel. The conventional, unblended data is recovered from the blended, jittered/irregular data by sparsity-promoting inversion using the non-equispaced fast discrete curvelet transform. The optimization scheme aims to deblend the blended data along with regularization and interpolation to a (finer) regular grid.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/wason2013SINBADtjm/wason2013SINBADtjm.pdf},
	author = {Haneet Wason and Felix J. Herrmann}
}
@presentation {wason2013SINBADtjo,
	title = {Time-jittered ocean bottom seismic acquisition},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Leveraging ideas from the field of compressed sensing, we show how simultaneous or blended acquisition can be setup as a {\textendash} compressed sensing problem. This helps us to design a pragmatic time-jittered marine acquisition scheme where multiple source vessels sail across an ocean-bottom array firing airguns at {\textendash} jittered source locations and instances in time, resulting in better spatial sampling, and speedup acquisition. Furthermore, 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.},
	keywords = {Presentation, SINBAD, SINBADSPRING2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/wason2013SINBADtjo/wason2013SINBADtjo.pdf},
	author = {Haneet Wason and Felix J. Herrmann}
}
@presentation {fang2013SINBADuafwi,
	title = {Uncertainty analysis for FWI},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Uncertainty analysis is important for seismic interpretation. Based on the framework of Bayesian, we can analyse different statistic parameters of our FWI result. However, directly sampling the posterior probability density function (pdf) is computationally intractable. In order to make this problem computationally tractable, in this work, we use Gaussian distribution approximation and low rank approximation to generate the posterior pdf. Simultaneous shots are also used to reduce the computational costs.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/fang2013SINBADuafwi/fang2013SINBADuafwi.pdf},
	author = {Zhilong Fang and Felix J. Herrmann}
}
@presentation {ghadermarzy2013SINBADups,
	title = {Using prior support information in approximate message passing algorithms},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Consider the standard compressed sensing problem. We want to recover sparse compressible aorsignal from few linear measurements. In this talk we investigate recovery performance when we have prior information about the support, i.e., the indices of the non-zero entries, of the signal to be recovered. First we briefly review the results of "weighted $\ell_p$ minimization algorithm with p = 1 and 0 < p < 1". Then we derive a weighted approximate message passing (AMP) algorithm which incorporates prior support information into the AMP algorithm. We empirically show that this algorithm recovers sparse signals significantly faster than weighted $\ell_1$ minimization. We also introduce a reweighting scheme for AMP and weighted AMP which, we observe, substantially improves the recovery conditions of these algorithms. We illustrate our results with extensive numerical experiments on synthetic data and seismic data reconstruction.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/ghadermarzy2013SINBADups/ghadermarzy2013SINBADups.pdf},
	author = {Navid Ghadermarzy and Ozgur Yilmaz and Felix J. Herrmann}
}
@presentation {kumar2013SINBADwrs,
	title = {Wavefield reconstruction with SVD-free low-rank matrix factorization},
	year = {2013},
	publisher = {SINBAD},
	abstract = {As shown in past, we can leverage the ideas from the field of compressed sensing to cast problems like seismic data interpolation or sequential shot data recovery from simultaneous data, as a compressed sensing problem. In this work we will show how we can borrow the same ideas of compressed sensing and cast these problems as matrix completion problems. Instead of sparsity we will show that we can exploit the low-rank structure of seismic data to solve these problems. One of the impediments in rank-minimization problem is the computation of singular values. We will also show how we can solve the rank minimization problems SVD-free. The practical application is divided into three parts: 1. In case of sequential seismic data acquisition, how jittered subsampling helps to recover the better quality data as compared to random subsampling. 2. How the incorporation of reciprocity principles help to enhance the quality of recovered fully sampled data. 3. How we can recover the sequential source data from simultaneous source data.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/kumar2013SINBADwrs/kumar2013SINBADwrs.pdf},
	author = {Rajiv Kumar and Aleksandr Y. Aravkin and Hassan Mansour and Ernie Esser and Felix J. Herrmann}
}
@presentation {herrmann2012SINBADals,
	title = {Fast sparsity-promoting imaging with message passing},
	year = {2012},
	publisher = {SINBAD},
	abstract = {To meet current-day challenges, exploration seismology increasingly relies on more and more sophisticated algorithms that require multiple paths through all data. This requirement leads to problems because the size of seismic data volumes is increasing exponentially, exposing bottlenecks in IO and computational capability. To overcome these bottlenecks, we follow recent trends in machine learning and compressive sensing by proposing a sparsity-promoting inversion technique that works on small randomized subsets of data only. We boost the performance of this algorithm significantly by modifying a state-of-the-art l1-norm solver to benefit from message passing, which breaks the build up of correlations between model iterates and the randomized linear forward model. We demonstrate the performance of this algorithm on a toy sparse-recovery problem and on a realistic reverse-time-migration example with random source encoding. The improvements in speed, memory use, and output quality are truly remarkable.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/herrmann2012SINBADals/herrmann2012SINBADals_pres.pdf},
	author = {Felix J. Herrmann}
}
@presentation {herrmann2012SINBADlds,
	title = {Latest developments in seismic-data recovery},
	year = {2012},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADSPRING2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Spring/herrmann2012SINBADlds/herrmann2012SINBADlds_pres.pdf},
	author = {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 {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 {herrmann2012SINBADsls,
	title = {Supercool(ed) least-squares imaging: latest insights in sparsity-promoting migration},
	year = {2012},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADSPRING2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Spring/herrmann2012SINBADsls/herrmann2012SINBADsls_pres.pdf},
	author = {Felix J. Herrmann}
}
@presentation {herrmann2011SINBADcoc,
	title = {Challenges and opportunities for compressive sensing in seismic acquisition},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/herrmann2011SINBADcoc/herrmann2011SINBADcoc_pres.pdf},
	author = {Felix J. Herrmann}
}
@presentation {herrmann2011SINBADcos,
	title = {Challenges and opportunities in sparse wavefield inversion},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/herrmann2011SINBADcos/herrmann2011SINBADcos_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {mansour2011SBGFcspsma,
	title = {A compressive sensing perspective on simultaneous marine acquisition},
	booktitle = {SBGF},
	year = {2011},
	publisher = {SBGF},
	organization = {SBGF},
	abstract = {The high cost of acquiring seismic data in marine environments compels the adoption of simultaneous- source acquisition - an emerging technology that is stimulating both geophysical research and commercial efforts. In this paper, we discuss the properties of randomized simultaneous acquisition matrices and demonstrate that sparsity-promoting recovery improves the quality of the reconstructed seismic data volumes. Simultaneous marine acquisition calls for the development of a new set of design principles and post-processing tools. Leveraging established findings from the field of compressed sensing, the recovery from simultaneous sources depends on a sparsifying transform that compresses seismic data, is fast, and reasonably incoherent with the compressive sampling matrix. To achieve this incoherence, we use random time dithering where sequential acquisition with a single airgun is replaced by continuous acquisition with multiple airguns firing at random times and at random locations. We demonstrate our results with simulations of simultaneous Marine acquisition using periodic and randomized time dithering.},
	keywords = {Acquisition, Compressive Sensing, Presentation, SBGF},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SBGF/2011/Mansour11SBGFcspsma/Mansour11SBGFcspsma.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SBGF/2011/Mansour11SBGFcspsma/Mansour11SBGFcspsma.pdf},
	author = {Hassan Mansour and Haneet Wason and Tim T.Y. Lin and Felix J. Herrmann}
}
@presentation {herrmann2011SINBADdre,
	title = {Dimensionality-reduced estimation of primaries by sparse inversion},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBASPRING2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Spring/herrmann2011SINBADdre/herrmann2011SINBADdre_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {jumah2011SEGdrepsi,
	title = {Dimensionality-reduced estimation of primaries by sparse inversion},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {30},
	year = {2011},
	month = {09},
	pages = {3520-3525},
	publisher = {SEG},
	organization = {SEG},
	abstract = {Data-driven methods{\textendash}-such as the estimation of primaries by sparse inversion{\textendash}-suffer from the "curse of dimensionality", which leads to disproportional growth in computational and storage demands when moving to realistic 3-D field data. To remove this fundamental impediment, we propose a dimensionality reduction technique where the "data matrix" is approximated adaptively by a randomized low-rank approximation. Compared to conventional methods, our approach has the advantage that the cost of the low-rank approximation is reduced significantly, which may lead to considerable reductions in storage and computational costs of the sparse inversion. Application of the proposed formalism to synthetic data shows that significant improvements are achievable at low computational overhead required to compute the low-rank approximations.},
	keywords = {Presentation, Processing, SEG},
	doi = {10.1190/1.3627931},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2011/Jumah11SEGdrepsi/Jumah11SEGdrepsi.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2011/Jumah11SEGdrepsi/Jumah11SEGdrepsi_pres.pdf},
	author = {Bander Jumah and Felix J. Herrmann}
}
@conference {herrmann2011EAGEefmsp,
	title = {Efficient least-squares migration with sparsity promotion},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2011},
	month = {05},
	abstract = {Seismic imaging relies on the collection of multi-experimental data volumes in combination with a sophisticated back-end to create high-fidelity inversion results. While significant improvements have been made in linearized inversion, the current trend of incessantly pushing for higher quality models in increasingly complicated regions reveals fundamental shortcomings in handling increasing problem sizes numerically. The so-called {\textquoteleft}{\textquoteleft}curse of dimensionality" is the main culprit because it leads to an exponential growth in the number of sources and the corresponding number of wavefield simulations required by {\textquoteleft}{\textquoteleft}wave-equation" migration. We address this issue by reducing the number of sources by a randomized dimensionality reduction technique that combines recent developments in stochastic optimization and compressive sensing. As a result, we replace the cur- rent formulations of imaging that rely on all data by a sequence of smaller imaging problems that use the output of the previous inversion as input for the next. Empirically, we find speedups of at least one order-of-magnitude when each reduced experiment is considered theoretically as a separate compressive-sensing experiment.},
	keywords = {EAGE, Imaging, Presentation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2011/herrmann11EAGEefmsp/herrmann11EAGEefmsp.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2011/herrmann11EAGEefmsp/herrmann11EAGEefmsp_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=50333},
	author = {Felix J. Herrmann and Xiang Li}
}
@conference {lin2011EAGEepsic,
	title = {Estimating primaries by sparse inversion in a curvelet-like representation domain},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2011},
	month = {05},
	abstract = {We present an uplift in the fidelity and wavefront continuity of results obtained from the Estimation of Primaries by Sparse Inversion (EPSI) program by reconstructing the primary events in a hybrid wavelet-curvelet representation domain. EPSI is a multiple removal technique that belongs to the class of wavefield inversion methods, as an alternative to the traditional adaptive-subtraction process. The main assumption is that the correct primary events should be as sparsely-populated in time as possible. A convex reformulation of the original EPSI algorithm allows its convergence property to be preserved even when the solution wavefield is not formed in the physical domain. Since wavefronts and edge-type singularities are sparsely represented in the curvelet domain, sparse solutions formed in this domain will exhibit vastly improved continuity when compared to those formed in the physical domain, especially for the low-energy events at later arrival times. Further- more, a wavelet-type representation domain will preserve sparsity in the reflected events even if they originate from non-zero-order discontinuities in the subsurface, providing an additional level of robustness. This method does not require any changes in the underlying computational algorithm and does not explicitly impose continuity constraints on each update.},
	keywords = {EAGE, Presentation, Processing},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2011/lin11EAGEepsic/lin11EAGEepsic.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2011/lin11EAGEepsic/lin11EAGEepsic_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=50427},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@conference {li2011EAGEfwirr,
	title = {Full-waveform inversion with randomized L1 recovery for the model updates},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2011},
	month = {05},
	abstract = {Full-waveform inversion (FWI) is a data fitting procedure that relies on the collection of seismic data volumes and sophisticated computing to create high-resolution results. With the advent of FWI, the improvements in acquisition and inversion have been substantial, but these improvements come at a high cost because FWI involves extremely large multi-experiment data volumes. The main obstacle is the "curse of dimensionality" exemplified by Nyquist{\~O}s sampling criterion, which puts a disproportionate strain on current acquisition and processing systems as the size and desired resolution increases. In this paper, we address the "curse of dimensionality" by randomized dimensionality reduction of the FWI problem adapted from the field of CS. We invert for model updates by replacing the Gauss-Newton linearized subproblem for subsampled FWI with a sparsity promoting formulation, and solve this formulation using the SPGl1 algorithm. We speed up the algorithm and avoid overfitting the data by solving for the linearized updates only approximately. 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 = {EAGE, Full-waveform inversion, Presentation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2011/li11EAGEfwirr/li11EAGEfwirr.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2011/li11EAGEfwirr/li11EAGEfwirr_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=50345},
	author = {Xiang Li and Aleksandr Y. Aravkin and Tristan van Leeuwen and Felix J. Herrmann}
}
@conference {herrmann2011SLIMsummer1,
	title = {Gene Golub SIAM Summer School July 4 - 15, 2011},
	booktitle = {SLIM},
	year = {2011},
	month = {08},
	keywords = {Presentation},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SIAM/2011/herrmann11SLIMsummer1/herrmann11SLIMsummer1.pdf},
	author = {Felix J. Herrmann}
}
@conference {vanleeuwen2011AMPhsdmwi,
	title = {A hybrid stocahstic-deterministic method for waveform inversion},
	booktitle = {AMP},
	year = {2011},
	note = {Presented at AMP Medical and Seismic Imaging, 2011, Vancouver BC},
	month = {07},
	publisher = {WAVES 2011},
	organization = {WAVES 2011},
	abstract = {A lot of seismic and medical imaging problems can be written as a least-squares data- fitting problem. In particular, we consider the case of multi-experiment data, where the data consists of a large number of "independent" measurements. Solving the inverse problem then involves repeatedly forward modeling the data for each of these experiments. In case the number of experiments is large and the modeling kernel expensive to apply, such an approach may be prohibitively expensive. We review techniques from stochastic optimization which aim at dramatically reducing the number of experiments that need to be modeled at each iteration. This reduction is typically achieved by randomly subsampling the data. Special care needs to be taken in the optimization to deal with the stochasticity that is introduced in this way.},
	keywords = {Presentation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ICIAM/2011/vanleeuwen2011AMPhsdmwi/vanleeuwen2011AMPhsdmwi.pdf},
	author = {Tristan van Leeuwen and Mark Schmidt and Michael P. Friedlander and Felix J. Herrmann}
}
@conference {vanleeuwen2011EAGEhsdomwi,
	title = {A hybrid stochastic-deterministic optimization method for waveform inversion},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2011},
	month = {05},
	abstract = {Present-day high quality 3D acquisition can give us lower frequencies and longer offsets with which to invert. However, the computational costs involved in handling this data explosion are tremendous. Therefore, recent developments in full-waveform inversion have been geared towards reducing the computational costs involved. A key aspect of several approaches that have been proposed is a dramatic reduction in the number of sources used in each iteration. A reduction in the number of sources directly translates to less PDE-solves and hence a lower computational cost. Recent attention has been drawn towards reducing the sources by randomly combining the sources in to a few supershots, but other strategies are also possible. In all cases, the full data misfit, which involves all the sequential sources, is replaced by a reduced misfit that is much cheaper to evaluate because it involves only a small number of sources (batchsize). The batchsize controls the accuracy with which the reduced misfit approximates the full misfit. The optimization of such an inaccurate, or noisy, misfit is the topic of stochastic optimization. In this paper, we propose an optimization strategy that borrows ideas from the field of stochastic optimization. The main idea is that in the early stage of the optimization, far from the true model, we do not need a very accurate misfit. The strategy consists of gradually increasing the batchsize as the iterations proceed. We test the proposed strategy on a synthetic dataset. We achieve a very reasonable inversion result at the cost of roughly 13 evaluations of the full misfit. We observe a speed-up of roughly a factor 20.},
	keywords = {EAGE, Full-waveform inversion, Optimization, Presentation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2011/vanleeuwen11EAGEhsdomwi/vanleeuwen11EAGEhsdomwi.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2011/vanleeuwen11EAGEhsdomwi/vanleeuwen11EAGEhsdomwi_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=50341},
	author = {Tristan van Leeuwen and Felix J. Herrmann and Mark Schmidt and Michael P. Friedlander}
}
@conference {herrmann11SLIMsummer2,
	title = {Lecture 2. Gene Golub SIAM Summer School July 4 - 15, 2011},
	booktitle = {SLIM},
	year = {2011},
	month = {08},
	keywords = {Presentation},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SIAM/2011/herrmann11SLIMsummer2/herrmann11SLIMsummer2.pdf},
	author = {Felix J. Herrmann}
}
@conference {tu2011SEGmult,
	title = {Migration with surface-related multiples from incomplete seismic data},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {30},
	year = {2011},
	month = {09},
	pages = {3222-3227},
	publisher = {SEG},
	organization = {SEG},
	abstract = {Seismic acquisition is confined by limited aperture that leads to finite illumination, which, together with other factors, hinders imaging of subsurface objects in complex geological settings such as salt structures. Conventional processing, including surface-related multiple elimination, further reduces the amount of information we can get from seismic data. With the growing consensus that multiples carry valuable information that is missing from primaries, we are motivated to exploit the extra illumination provided by multiples to image the sub- surface. In earlier research, we proposed such a method by combining primary estimation and sparsity-promoting migration to invert for model perturbations directly from the total up-going wavefield. In this abstract, we focus on a particular case. By exploiting the extra illumination from surface-related multiples, we mitigate the effects caused by migrating from incomplete data with missing sources and missing near-offsets.},
	keywords = {Imaging, Presentation, Processing, SEG},
	doi = {10.1190/1.3627865},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2011/tu11SEGmult/tu11SEGmult.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2011/tu11SEGmult/tu11SEGmult_pres.pdf},
	author = {Ning Tu and Tim T.Y. Lin and Felix J. Herrmann}
}
@conference {vanleeuwen2011WAVESpeiv,
	title = {Probing the extended image volume for seismic velocity inversion},
	booktitle = {WAVES},
	year = {2011},
	month = {07},
	publisher = {Waves 2011},
	organization = {Waves 2011},
	abstract = {In seismic velocity inversion one aims to reconstruct a kinematically correct subsurface velocity model that can be used as input for further processing and inversion of the data. An important tool in velocity inversion is the prestack image volume. This image volume can be defined as a cross-correlation of the source and receivers wavefields for non-zero space and time lags. If the background velocity is kinematically acceptable, this image volume will have its main contributions at zero lag, even for complex models. Thus, it is an ideal tool for wave-equation migration velocity analysis in the presence of strong lateral heterogeneity. In particular, it allows us to pose migration velocity analysis as a PDE-constrained optimization problem, where the goal is to minimize the energy in the image volume at non-zero lag subject to fitting the data approximately. However, it is computationally infeasible to explicitly form the whole image volume. In this paper, we discuss several ways to reduce the computational costs involved in computing the image volume and evaluating the focusing criterion. We reduce the costs for calculating the data by randomized source synthesis. We also present an efficient way to subsample the image volume. Finally, we propose an alternative optimization criterion and suggest a multiscale inversion strategy for wave-equation MVA.},
	keywords = {Presentation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ICIAM/2011/vanleeuwen2011WAVESpeiv/vanleeuwen2011WAVESpeiv.pdf},
	author = {Tristan van Leeuwen and Felix J. Herrmann}
}
@conference {lin2011SEGrssde,
	title = {Robust source signature deconvolution and the estimation of primaries by sparse inversion},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {30},
	year = {2011},
	month = {09},
	pages = {4354-4359},
	publisher = {Dept. of Earth and Ocean Sciences, University of British Columbia},
	organization = {Dept. of Earth and Ocean Sciences, University of British Columbia},
	abstract = {The past few years had seen some concentrated interest on a particular wavefield-inversion approach to the popular SRME multiple removal technique called Estimation of Primaries by Sparse Inversion (EPSI). EPSI promises greatly improved tolerance to noise, missing data, edge effect, and other physi- cal phenomenon generally not described by the SRME relation (van Groenestijn and Verschuur, 2009a,b). It is based on the premise that it is possible to stably invert for both the primary impulse response and the source signature despite beforehand having no (or very limited) explicit knowledge of latter. The key to successful applications of EPSI, as shown in very recent works (Savels et al., 2010), is a robust way to reconstruct very sparse primary impulse response events as part of the inversion process. Based on the various successful demonstrations in literature, there is a very strong sense that EPSI will also play an important role in future developments of source sig- nature deconvolution and the general recovering of wavefield spectrum.},
	keywords = {deconvolution, Presentation, Processing, SEG, sparse inversion},
	doi = {10.1190/1.3628116},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2011/lin11SEGrssde/lin11SEGrssde.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2011/lin11SEGrssde/lin11SEGrssde_pres.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@conference {aravkin2011ICIAMspf,
	title = {Sparsity promoting formulations and algorithms for FWI},
	booktitle = {ICIAM},
	year = {2011},
	note = {Presented at AMP Medical and Seismic Imaging, 2011, Vancouver BC.},
	month = {07},
	publisher = {ICIAM 2011},
	organization = {ICIAM 2011},
	abstract = {Full Waveform Inversion (FWI) is a computational procedure to extract medium parameters from seismic data. FWI is typically formulated as a nonlinear least squares optimization problem, and various regularization techniques are used to guide the optimization because the problem is ill-posed. We propose a novel sparse regularization which exploits the ability of curvelets to efficiently represent geophysical images. We then formulate a corresponding sparsity promoting constrained optimization problem, which we solve using an open source algorithm. The techniques are applicable to any inverse problem where sparsity modeling is appropriate. We demonstrate the efficacy of the formulation on a toy example (stylized cross-well experiment) and on a realistic Seismic example (partial Marmoussi model). We also discuss the tradeoff between model fit and sparsity promotion, with a view to extend existing techniques for linear inverse problems to the case where the forward model is nonlinear.},
	keywords = {Full-waveform inversion, Optimization, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ICIAM/2011/aravkin2011ICIAMspf/aravkin2011ICIAMspf_pres.pdf},
	author = {Aleksandr Y. Aravkin and Tristan van Leeuwen and James V. Burke and Felix J. Herrmann}
}
@conference {tu2011EAGEspmsrm,
	title = {Sparsity-promoting migration with surface-related multiples},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2011},
	month = {05},
	abstract = {Multiples, especially the surface-related multiples, form a significant part of the total up-going wave- field. If not properly dealt with, they can lead to false reflectors in the final image. So conventionally practitioners remove them prior to migration. Recently research has revealed that multiples can actually provide extra illumination so different methods are proposed to address the issue that how to use multiples in seismic imaging, but with various kinds of limitations. In this abstract, we combine primary estimation and sparsity-promoting migration into one convex-optimization process to include information from multiples. Synthetic examples show that multiples do make active contributions to seismic migration. Also by this combination, we can benefit from better recoveries of the Greens function by using sparsity-promoting algorithms since reflectivity is sparser than the Greens function.},
	keywords = {EAGE, Imaging, Presentation, Processing},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2011/tu11EAGEspmsrm/tu11EAGEspmsrm.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2011/tu11EAGEspmsrm/tu11EAGEspmsrm_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=50369},
	author = {Ning Tu and Tim T.Y. Lin and Felix J. Herrmann}
}
@conference {wason2011SEGsprsd,
	title = {Sparsity-promoting recovery from simultaneous data: a compressive sensing approach},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {30},
	year = {2011},
	month = {09},
	pages = {6-10},
	publisher = {SEG},
	organization = {SEG},
	abstract = {Seismic data acquisition forms one of the main bottlenecks in seismic imaging and inversion. The high cost of acquisition work and collection of massive data volumes compel the adoption of simultaneous-source seismic data acquisition - an emerging technology that is developing rapidly, stimulating both geophysical research and commercial efforts. Aimed at improving the performance of marine- and land-acquisition crews, simultaneous acquisition calls for development of a new set of design principles and post-processing tools. Leveraging developments from the field of compressive sensing the focus here is on simultaneous-acquisition design and sequential-source data recovery. Apart from proper compressive sensing sampling schemes, the recovery from simultaneous simulations depends on a sparsifying transform that compresses seismic data, is fast, and reasonably incoherent with the compressive-sampling matrix. Using the curvelet transform, in which seismic data can be represented parsimoniously, the recovery of the sequential-source data volumes is achieved using the sparsity-promoting program {\textemdash} SPGL1, a solver based on projected spectral gradients. The main outcome of this approach is a new technology where acquisition related costs are no longer determined by the stringent Nyquist sampling criteria.},
	keywords = {Acquisition, Compressive Sensing, Presentation, SEG},
	doi = {10.1190/1.3628174},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2011/wason11SEGsprsd/wason11SEGsprsd.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2011/wason11SEGsprsd/wason11SEGsprsd_pres.pdf},
	author = {Haneet Wason and Felix J. Herrmann and Tim T.Y. Lin}
}
@presentation {herrmann2011SINBADrnr,
	title = {To redraw or not to redraw: recent insights in randomized dimensionality reduction for inversion},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/herrmann2011SINBADrnr/herrmann2011SINBADrnr_pres.pdf},
	author = {Felix J. Herrmann}
}
@presentation {herrmann2011SINBADoverview,
	title = {Welcome and overview of SINBAD \& DNOISE},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/herrmann2011SINBADoverview/herrmann2011SINBADoverview_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2010MATHIAScssr,
	title = {Compressive sensing and sparse recovery in exploration seismology},
	booktitle = {MATHIAS},
	year = {2010},
	note = {Presented at MATHIAS 2010 organized by Total SA. Paris},
	abstract = {During this presentation, I will talk about how recent results from compressive sensing and sparse recovery can be used to solve problems in exploration seismology where incomplete sampling is ubiquitous. I will also talk about how these ideas apply to dimensionality reduction of full-waveform inversion by randomly phase encoded sources.},
	keywords = {Presentation},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/MATHIAS/2010/herrmann2010MATHIAScssr/herrmann2010MATHIAScssr.pdf},
	author = {Felix J. Herrmann}
}
@presentation {herrmann2010SINBADcss,
	title = {Compressive sensing and sparse recovery in exploration seismology},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/herrmann2010SINBADcss/herrmann2010SINBADcss_pres.pdf},
	author = {Felix J. Herrmann}
}
@presentation {herrmann2010SINBADdrf,
	title = {Dimensionality reduction for full-waveform inversion},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/herrmann2010SINBADdrf/herrmann2010SINBADdrf_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {li2010SEGfwi,
	title = {Full-waveform inversion from compressively recovered model updates},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {29},
	year = {2010},
	month = {10},
	pages = {1029-1033},
	publisher = {SEG},
	organization = {SEG},
	abstract = {Full-waveform inversion relies on the collection of large multi-experiment data volumes in combination with a sophisticated back-end to create high-fidelity inversion results. While improvements in acquisition and inversion have been extremely successful, the current trend of incessantly pushing for higher quality models in increasingly complicated regions of the Earth reveals fundamental shortcomings in our ability to handle increasing problem size numerically. Two main culprits can be identified. First, there is the so-called {\textquoteleft}{\textquoteleft}curse of dimensionality{\textquoteright}{\textquoteright} exemplified by Nyquist{\textquoteright}s sampling criterion, which puts disproportionate strain on current acquisition and processing systems as the size and desired resolution increases. Secondly, there is the recent {\textquoteleft}{\textquoteleft}departure from Moore{\textquoteright}s law{\textquoteright}{\textquoteright} that forces us to lower our expectations to compute ourselves out of this. In this paper, we address this situation by randomized dimensionality reduction, which we adapt from the field of compressive sensing. In this approach, we combine deliberate randomized subsampling with structure-exploiting transform-domain sparsity promotion. Our approach is successful because it reduces the size of seismic data volumes without loss of information. With this reduction, we compute Newton-like updates at the cost of roughly one gradient update for the fully-sampled wavefield.},
	keywords = {Full-waveform inversion, Presentation, SEG},
	doi = {10.1190/1.3513022},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2010/li10SEGfwi/li10SEGfwi.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2010/li10SEGfwi/li10SEGfwi_pres.pdf},
	author = {Xiang Li and Felix J. Herrmann}
}
@conference {herrmann2010EAGErds,
	title = {Randomized dimensionality reduction for full-waveform inversion},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2010},
	month = {06},
	abstract = {Full-waveform inversion relies on the collection of large multi-experiment data volumes in combination with a sophisticated back-end to create high-fidelity inversion results. While improvements in acquisition and inversion have been extremely successful, the current trend of incessantly pushing for higher quality models in increasingly complicated regions of the Earth continues to reveal fundamental shortcomings in our ability to handle the ever increasing problem size numerically. Two causes can be identified as the main culprits responsible for this barrier. First, there is the so-called {\textquoteleft}{\textquoteleft}curse of dimensionality{\textquoteright}{\textquoteright} exemplified by Nyquist{\textquoteright}s sampling criterion, which puts disproportionate strain on current acquisition and processing systems as the size and desired resolution of our survey areas continues to increase. Secondly, there is the recent {\textquoteleft}{\textquoteleft}departure from Moore{\textquoteright}s law{\textquoteright}{\textquoteright} that forces us to lower our expectations to compute ourselves out of this. In this paper, we address this situation by randomized dimensionality reduction, which we adapt from the field of compressive sensing. In this approach, we combine deliberate randomized subsampling with structure-exploiting transform-domain sparsity promotion. Our approach is successful because it reduces the size of seismic data volumes without loss of information. With this reduction, we compute Newton-like updates at the cost of roughly one gradient update for the fully-sampled wavefield.},
	keywords = {EAGE, Presentation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2010/herrmann10EAGErds/herrmann10EAGErds.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2010/herrmann10EAGErds/herrmann10EAGErds_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=39352},
	author = {Felix J. Herrmann and Xiang Li}
}
@conference {moghaddam2010SEGrfw,
	title = {Randomized full-waveform inversion: a dimenstionality-reduction approach},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {29},
	year = {2010},
	month = {10},
	pages = {977-982},
	publisher = {SEG},
	organization = {SEG},
	abstract = {Full-waveform inversion relies on the collection of large multi-experiment data volumes in combination with a sophisticated back-end to create high-fidelity inversion results. While improvements in acquisition and inversion have been extremely successful, the current trend of incessantly pushing for higher quality models in increasingly complicated regions of the Earth reveals fundamental shortcomings in our ability to handle increasing problem sizes numerically. Two main culprits can be identified. First, there is the so-called {\textquoteleft}{\textquoteleft}curse of dimensionality{\textquoteright}{\textquoteright} exemplified by Nyquist{\textquoteright}s sampling criterion, which puts disproportionate strain on current acquisition and processing systems as the size and desired resolution increases. Secondly, there is the recent {\textquoteleft}{\textquoteleft}departure from Moore{\textquoteright}s law{\textquoteright}{\textquoteright} that forces us to develop algorithms that are amenable to parallelization. In this paper, we discuss different strategies that address these issues via randomized dimensionality reduction.},
	keywords = {Full-waveform inversion, Optimization, Presentation, SEG},
	doi = {10.1190/1.3513940},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2010/moghaddam10SEGrfw/moghaddam10SEGrfw.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2010/moghaddam10SEGrfw/moghaddam10SEGrfw_pres.pdf},
	author = {Peyman P. Moghaddam and Felix J. Herrmann}
}
@conference {herrmann2010EAGErss,
	title = {Randomized sampling strategies},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2010},
	month = {06},
	abstract = {Seismic exploration relies on the collection of massive data volumes that are subsequently mined for information during seismic processing. While this approach has been extremely successful in the past, the current trend towards higher quality images in increasingly complicated regions continues to reveal fundamental shortcomings in our workflows for high-dimensional data volumes. Two causes can be identified. First, there is the so-called {\textquoteleft}{\textquoteleft}curse of dimensionality{\textquoteright}{\textquoteright} exemplified by Nyquist{\textquoteright}s sampling criterion, which puts disproportionate strain on current acquisition and processing systems as the size and desired resolution of our survey areas continues to increase. Secondly, there is the recent {\textquoteleft}{\textquoteleft}departure from Moore{\textquoteright}s law{\textquoteright}{\textquoteright} that forces us to lower our expectations to compute ourselves out of this curse of dimensionality. In this paper, we offer a way out of this situation by a deliberate randomized subsampling combined with structure-exploiting transform-domain sparsity promotion. Our approach is successful because it reduces the size of seismic data volumes without loss of information. As such we end up with a new technology where the costs of acquisition and processing are no longer dictated by the size of the acquisition but by the transform-domain sparsity of the end-product.},
	keywords = {EAGE, Presentation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2010/herrmann10EAGErss/herrmann10EAGErss.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2010/herrmann10EAGErss/herrmann10EAGErss_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=39131},
	author = {Felix J. Herrmann}
}
@conference {lin2010SEGspm,
	title = {Sparsity-promoting migration from surface-related multiples},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {29},
	year = {2010},
	month = {10},
	pages = {3333-3337},
	publisher = {SEG},
	organization = {SEG},
	abstract = {Seismic imaging typically begins with the removal of multiple energy in the data, out of fear that it may introduce erroneous structure. However, seismic multiples have effectively seen more of the earth{\textquoteright}s structure, and if treated correctly can potential supply more information to a seismic image compared to primaries. Past approaches to accomplish this leave ample room for improvement; they either require extensive modification to standard migration techniques, rely too much on prior information, require extensive pre-processing, or resort to full-waveform inversion. We take some valuable lessons from these efforts and present a new approach balanced in terms of ease of implementation, robustness, efficiency and well-posedness, involving a sparsity-promoting inversion procedure using standard Born migration and a data-driven multiple modeling approach based on the focal transform.},
	keywords = {Presentation, Processing, SEG},
	doi = {10.1190/1.3513540},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2010/lin10SEGspm/lin10SEGspm.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2010/lin10SEGspm/lin10SEGspm_pres.pdf},
	author = {Tim T.Y. Lin and Ning Tu and Felix J. Herrmann}
}
@conference {lin2010EAGEseo,
	title = {Stabilized estimation of primaries via sparse inversion},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2010},
	month = {06},
	abstract = {Estimation of Primaries by Sparse Inversion (EPSI) is a recent method for surface-related multiple removal using a direct estimation method closely related to Amundsen inversion, where under a sparsity assumption the primary impulse response is determined directly from a data-driven wavefield inversion process. One of the major difficulties in its practical adoption is that one must have precise knowledge of a time-window that contains multiple-free primaries during each update. Moreover, due to the nuances involved in regularizing the model impulse response in the inverse problem, the EPSI approach has an additional number of inversion parameters where it may be difficult to choose a reasonable value. We show that the specific sparsity constraint on the EPSI updates lead to an inherently intractable problem, and that the time-window and other inversion variables arise in the context of additional regularizations that attempts to drive towards a meaningful solution. We furthermore suggest a way to remove almost all of these parameters via convexification, which stabilizes the inversion while preserving the crucial sparsity assumption in the primary impulse response model.},
	keywords = {EAGE, Presentation, Processing},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2010/lin10EAGEseo/lin10EAGEseo.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2010/lin10EAGEseo/lin10EAGEseo_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=39122},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@conference {herrmann2010IRISsns,
	title = {Sub-Nyquist sampling and sparsity: getting more information from fewer samples},
	booktitle = {IRIS},
	year = {2010},
	note = {Presented at the IRIS Workshop},
	abstract = {Many seismic exploration techniques rely on the collection of massive data volumes. While this approach has been extremely successful in the past, current efforts toward higher resolution images in increasingly complicated regions of the Earth continue to reveal fundamental shortcomings in our workflows. Chiefly amongst these is the so-called {\textquoteleft}{\textquoteleft}curse of dimensionality{\textquoteright}{\textquoteright} exemplified by Nyquist{\textquoteright}s sampling criterion, which disproportionately strains current acquisition and processing systems as the size and desired resolution of our survey areas continues to increase. In this presentation, we offer an alternative sampling method leveraging recent insights from compressive sensing towards seismic acquisition and processing of severely under-sampled data. The main outcome of this approach is a new technology where acquisition and processing related costs are no longer determined by overly stringent sampling criteria, such as Nyquist. At the heart of our approach lies randomized incoherent sampling that breaks subsampling related interferences by turning them into harmless noise, which we subsequently remove by promoting transform-domain sparsity. Now, costs no longer grow significantly with resolution and dimensionality of the survey area, but instead depend on transform-domain sparsity only. Our contribution is twofold. First, we demonstrate by means of carefully designed numerical experiments that compressive sensing can successfully be adapted to seismic exploration. Second, we show that accurate recovery can be accomplished for compressively sampled data volumes sizes that exceed the size of conventional transform-domain data volumes by only a small factor. Because compressive sensing combines transformation and encoding by a single linear encoding step, this technology is directly applicable to acquisition and to dimensionality reduction during processing. In either case, sampling, storage, and processing costs scale with transform-domain sparsity. Many seismic exploration techniques rely on the collection of massive data volumes. While this approach has been extremely successful in the past, current efforts toward higher resolution images in increasingly complicated regions of the Earth continue to reveal fundamental shortcomings in our workflows. Chiefly amongst these is the so-called {\textquoteleft}{\textquoteleft}curse of dimensionality{\textquoteright}{\textquoteright} exemplified by Nyquist{\textquoteright}s sampling criterion, which disproportionately strains current acquisition and processing systems as the size and desired resolution of our survey areas continues to increase. In this presentation, we offer an alternative sampling method leveraging recent insights from compressive sensing towards seismic acquisition and processing of severely under-sampled data. The main outcome of this approach is a new technology where acquisition and processing related costs are no longer determined by overly stringent sampling criteria, such as Nyquist. At the heart of our approach lies randomized incoherent sampling that breaks subsampling related interferences by turning them into harmless noise, which we subsequently remove by promoting transform-domain sparsity. Now, costs no longer grow significantly with resolution and dimensionality of the survey area, but instead depend on transform-domain sparsity only. Our contribution is twofold. First, we demonstrate by means of carefully designed numerical experiments that compressive sensing can successfully be adapted to seismic exploration. Second, we show that accurate recovery can be accomplished for compressively sampled data volumes sizes that exceed the size of conventional transform-domain data volumes by only a small factor. Because compressive sensing combines transformation and encoding by a single linear encoding step, this technology is directly applicable to acquisition and to dimensionality reduction during processing. In either case, sampling, storage, and processing costs scale with transform-domain sparsity.},
	keywords = {Presentation},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/IRIS/2010/herrmann2010IRISsns/herrmann2010IRISsns.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2009PIMScssr2,
	title = {Compressed sensing and sparse recovery in exploration seismology},
	booktitle = {PIMS},
	year = {2009},
	note = {Lecture II presented at the PIMS Summer School on Seismic Imaging, Seattle},
	abstract = {In this course, I will present how recent results from compressed sensing and sparse recovery apply to exploration seismology. During the first lecture, I will present the basic principles of compressive sensing; the importance of random jitter sampling and sparsifying transforms; and large-scale one-norm solvers. I will discuss the application of these techniques to missing trace interpolation. The second lecture will be devoted to coherent signal separation based on curveletdomain matched filtering and Bayesian separation with sparsity promotion. Applications of these techniques to the primary-multiple wavefield-separation problem on real data will be discussed as well. The third lecture will be devoted towards sparse recovery in seismic modeling and imaging and includes the problem of preconditioning the imaging operators, and the recovery from simultaneous source-acquired data.},
	keywords = {Presentation},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/PIMS/2009/herrmann2009PIMScssr2/herrmann2009PIMScssr2.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2009PIMScssr3,
	title = {Compressed sensing and sparse recovery in exploration seismology},
	booktitle = {PIMS},
	year = {2009},
	note = {Lecture III presented at the PIMS Summer School on Seismic Imaging, Seattle},
	abstract = {In this course, I will present how recent results from compressed sensing and sparse recovery apply to exploration seismology. During the first lecture, I will present the basic principles of compressive sensing; the importance of random jitter sampling and sparsifying transforms; and large-scale one-norm solvers. I will discuss the application of these techniques to missing trace interpolation. The second lecture will be devoted to coherent signal separation based on curveletdomain matched filtering and Bayesian separation with sparsity promotion. Applications of these techniques to the primary-multiple wavefield-separation problem on real data will be discussed as well. The third lecture will be devoted towards sparse recovery in seismic modeling and imaging and includes the problem of preconditioning the imaging operators, and the recovery from simultaneous source-acquired data.},
	keywords = {Presentation},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/PIMS/2009/herrmann2009PIMScssr3/herrmann2009PIMScssr3.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2009PIMScssr1,
	title = {Compressed sensing and sparse recovery in exploration seismology},
	booktitle = {PIMS},
	year = {2009},
	note = {Lecture I presented at the PIMS Summer School on Seismic Imaging, Seattle},
	abstract = {In this course, I will present how recent results from compressed sensing and sparse recovery apply to exploration seismology. During the first lecture, I will present the basic principles of compressive sensing; the importance of random jitter sampling and sparsifying transforms; and large-scale one-norm solvers. I will discuss the application of these techniques to missing trace interpolation. The second lecture will be devoted to coherent signal separation based on curveletdomain matched filtering and Bayesian separation with sparsity promotion. Applications of these techniques to the primary-multiple wavefield-separation problem on real data will be discussed as well. The third lecture will be devoted towards sparse recovery in seismic modeling and imaging and includes the problem of preconditioning the imaging operators, and the recovery from simultaneous source-acquired data},
	keywords = {Presentation},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/PIMS/2009/herrmann2009PIMScssr1/herrmann2009PIMScssr1.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2009SEGcib,
	title = {Compressive imaging by wavefield inversion with group sparsity},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {28},
	year = {2009},
	month = {10},
	pages = {2337-2341},
	publisher = {SEG},
	organization = {SEG},
	abstract = {Migration relies on multi-dimensional correlations between source- and residual wavefields. These multi-dimensional correlations are computationally expensive because they involve operations with explicit and full matrices that contain both wavefields. By leveraging recent insights from compressive sampling, we present an alternative method where linear correlation-based imaging is replaced by imaging via multidimensional deconvolutions of compressibly sampled wavefields. Even though this approach goes at the expense of having to solve a sparsity-promotion recovery program for the image, our wavefield inversion approach has the advantage of reducing the system size in accordance to transform-domain sparsity of the image. Because seismic images also exhibit a focusing of the energy towards zero offset, the compressive-wavefield inversion itself is carried out using a recent extension of one-norm solver technology towards matrix-valued problems. These so-called hybrid $(1,\,2)$-norm solvers allow us to penalize pre-stack energy away from zero offset while exploiting joint sparsity amongst near-offset images. Contrary to earlier work to reduce modeling and imaging costs through random phase-encoded sources, our method compressively samples wavefields in model space. This approach has several advantages amongst which improved system-size reduction, and more flexibility during subsequent inversions for subsurface properties.},
	keywords = {Presentation, SEG},
	doi = {10.1190/1.3255328},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/herrmann09SEGcib/herrmann09SEGcib.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/herrmann09SEGcib/herrmann09SEGcib_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2009IAPcsisa,
	title = {Compressive seismic imaging with simultaneous acquisition},
	booktitle = {IAP},
	year = {2009},
	note = {Presented at the IAP meeting, Vienna},
	abstract = {The shear size of seismic data volumes forms one of the major impediments for the inversion of seismic data. Turning forward modeling and inversion into a compressive sensing (CS) problem - where simulated data are recovered from a relatively small number of independent sources - can effectively mitigate this high-cost impediment. Our key contribution lies in the design of a sub-sampling operator that commutes with the time-harmonic Helmholtz system. As in compressive sensing, this leads to a reduction of simulation cost. This reduction is commensurate with the transform-domain sparsity of the solution., implying that computational costs are no longer determined by the size of the discretization but by transform-domain sparsity of the solution of the CS problem that recovers the data. The combination of this sub-sampling strategy with our recent work on preconditioned implicit solvers for the time-harmonic Helmholtz equation provides a viable alternative to full-waveform inversion schemes based on explicit time-domain finite-difference methods.},
	keywords = {Presentation},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/AIP/2009/herrmann2009IAPcsisa/Herrmann09AIP1.pdf},
	author = {Felix J. Herrmann and Yogi A. Erlangga and Tim T.Y. Lin}
}
@conference {lin2009SEGcsf,
	title = {Compressive simultaneous full-waveform simulation},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {28},
	year = {2009},
	month = {10},
	pages = {2577-2581},
	publisher = {SEG},
	organization = {SEG},
	abstract = {The fact that the computational complexity of wavefield simulation is proportional to the size of the discretized model and acquisition geometry, and not to the complexity of the simulated wavefield, is a major impediment within seismic imaging. By turning simulation into a compressive sensing problem{\textendash}where simulated data is recovered from a relatively small number of independent simultaneous sources{\textendash}we remove this impediment by showing that compressively sampling a simulation is equivalent to compressively sampling the sources, followed by solving a reduced system. As in compressive sensing, this allows for a reduction in sampling rate and hence in simulation costs. We demonstrate this principle for the time-harmonic Helmholtz solver. The solution is computed by inverting the reduced system, followed by a recovery of the full wavefield with a sparsity promoting program. Depending on the wavefield{\textquoteright}s sparsity, this approach can lead to a significant cost reduction, in particular when combined with the implicit preconditioned Helmholtz solver, which is known to converge even for decreasing mesh sizes and increasing angular frequencies. These properties make our scheme a viable alternative to explicit time-domain finite-difference.},
	keywords = {Presentation, SEG},
	doi = {10.1190/1.3255381},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/Lin09SEGcsf/Lin09SEGcsf.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/Lin09SEGcsf/Lin09SEGcsf_pres.pdf},
	author = {Tim T.Y. Lin and Yogi A. Erlangga and Felix J. Herrmann}
}
@conference {shahidi2009SEGcmf,
	title = {Curvelet-domain matched filtering with frequency-domain regularization},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {28},
	year = {2009},
	month = {10},
	pages = {3645-3649},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In Herrmann et al. (2008), it is shown that zero-order pseudodifferential operators, which model the migration-demigration operator and the operator mapping the predicted multiples to the true multiples, can be represented by a diagonal weighting in the curvelet domain. In that paper, a smoothness constraint was introduced in the phase space of the operator in order to regularize the solution to make it unique. In this paper, we use recent results in Demanet and Ying (2008) on the discrete symbol calculus to impose a further smoothness constraint, this time in the frequency domain. It is found that with this additional constraint, faster convergence is realized. Results on a synthetic pseudodifferential operator as well as on an example of primary-multiple separation in seismic data are included, comparing the model with and without the new smoothness constraint, from which it is found that results of improved quality are also obtained.},
	keywords = {Presentation, SEG},
	doi = {10.1190/1.3255624},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/shahidi09SEGcmf/shahidi09segcmf.pdf},
	author = {Reza Shahidi and Felix J. Herrmann}
}
@conference {lin2009EAGEdsa,
	title = {Designing simultaneous acquisitions with compressive sensing},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2009},
	month = {06},
	abstract = {The goal of this paper is in designing a functional simultaneous acquisition scheme by applying the principles of compressive sensing. By framing the acquisition in a compressive sensing setting we immediately gain insight into not only how to choose the source signature and shot patterns, but also in how well we can hope to demultiplex the data when given a set amount of reduction in the number of sweeps. The principles of compressive sensing dictates that the quality of the demultiplexed data is closely related to the transform-domain sparsity of the solution. This means that, given an estimate in the complexity of the expectant data wavefield, it is possible to controllably reduce the number of shots that needs to be recorded in the field. We show a proof of concept by introducing an acquisition compatible with compressive sensing based on randomly phase-encoded vibroseis sweeps.},
	keywords = {EAGE, Presentation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2009/lin09EAGEdsa/lin09EAGEdsa.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2009/lin09EAGEdsa/lin2009EAGEdsa_pres.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@conference {yan2009SEGgpb,
	title = {Groundroll prediction by interferometry and separation by curvelet-domain matched filtering},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {28},
	year = {2009},
	month = {10},
	pages = {3297-3301},
	publisher = {SEG},
	organization = {SEG},
	abstract = {The removal of groundroll in land based seismic data is a critical step for seismic imaging. In this paper, we introduce a work flow to predict the groundroll by interferometry and then separate the groundroll in the curvelet domain. Thus workflow is similar to the workflow of surface-related multiple elimination (SRME). By exploiting the adaptability and sparsity of curvelets, we are able to significantly improve the separation of groundroll in comparison to results yielded by frequency-domain adaptive subtraction methods. We provide synthetic data example to illustrate our claim.},
	keywords = {Presentation, SEG},
	doi = {10.1190/1.3255544},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/yan09SEGgpb/yan09SEGgpb.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/yan09SEGgpb/yan09SEGgpb_pres.pdf},
	author = {Jiupeng Yan and Felix J. Herrmann}
}
@conference {yan2009SEGgpb2,
	title = {Groundroll prediction by interferometry and separation by curvelet-domain filtering},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2009},
	abstract = {The removal of groundroll in land based seismic data is a critical step for seismic imaging. In this paper, we introduce a work flow to predict the groundroll by interferometry and then separate the groundroll in the curvelet domain. Thus workflow is similar to the workflow of surface-related multiple elimination (SRME). By exploiting the adaptability and sparsity of curvelets, we are able to significantly improve the separation of groundroll in comparison to results yielded by frequency-domain adaptive subtraction methods. We provide synthetic data example to illustrate our claim.},
	keywords = {Presentation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/yan2009SEGgpb2/yan2009SEGgpb2.pdf},
	author = {Jiupeng Yan and Felix J. Herrmann}
}
@conference {tang2009SEGhdb,
	title = {Higher dimensional blue-noise sampling schemes for curvelet-based seismic data recovery},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {28},
	year = {2009},
	month = {10},
	pages = {191-195},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In combination with compressive sensing, a successful reconstruction scheme called Curvelet-based Recovery by Sparsity-promoting Inversion (CRSI) has been developed, and has proven to be useful for seismic data processing. One of the most important issues for CRSI is the sampling scheme, which can greatly affect the quality of reconstruction. Unlike usual regular undersampling, stochastic sampling can convert aliases to easy-to-eliminate noise. Some stochastic sampling methods have been developed for CRSI, e.g. jittered sampling, however most have only been applied to 1D sampling along a line. Seismic datasets are usually higher dimensional and very large, thus it is desirable and often necessary to develop higher dimensional sampling methods to deal with these data. For dimensions higher than one, few results have been reported, except uniform random sampling, which does not perform well. In the present paper, we explore 2D sampling methodologies for curvelet-based reconstruction, possessing sampling spectra with blue noise characteristics, such as Poisson Disk sampling, Farthest Point Sampling, and the 2D extension of jittered sampling. These sampling methods are shown to lead to better recovery and results are compared to the other more traditional sampling protocols.},
	keywords = {Presentation, SEG},
	doi = {10.1190/1.3255230},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/tang09SEGhdb/tang09SEGhdb.pdf},
	author = {Gang Tang and Reza Shahidi and Felix J. Herrmann and Jianwei Ma}
}
@conference {kumar2009SEGins,
	title = {Incoherent noise suppression with curvelet-domain sparsity},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {28},
	year = {2009},
	month = {10},
	pages = {3356-3360},
	publisher = {SEG},
	organization = {SEG},
	abstract = {The separation of signal and noise is a key issue in seismic data processing. By noise we refer to the incoherent noise that is present in the data. We use the recently introduced multiscale and multidirectional curvelet transform for suppression of random noise. The curvelet transform decomposes data into directional plane waves that are local in nature. The coherent features of the data occupy the large coefficients in the curvelet domain, whereas the incoherent noise lives in the small coefficients. In other words, signal and noise have minimal overlap in the curvelet domain. This gives us a chance to use curvelets to suppress noise present in data.},
	keywords = {Presentation, SEG},
	doi = {10.1190/1.3255557},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/kumar09SEGins/kumar09SEGins.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/kumar09SEGins/kumar09SEGins_pres.pdf},
	author = {Vishal Kumar and Felix J. Herrmann}
}
@conference {lin2009DELPHIrwi,
	title = {Randomized wavefield inversion},
	booktitle = {DELPHI},
	year = {2009},
	note = {Presented at the DELPHI meeting. The Hague},
	keywords = {Presentation},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/Delphi/2009/lin2009DELPHIrwi/lin2009DELPHIrwi.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann and Yogi A. Erlangga}
}
@conference {herrmann2009SEGrpl,
	title = {Reflector-preserved lithological upscaling},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {28},
	year = {2009},
	month = {10},
	pages = {3466-3470},
	publisher = {SEG},
	organization = {SEG},
	abstract = {By combining Percolation models with lithological smoothing, we arrive at method for upscaling rock elastic constants that preserves reflections. In this approach, the Percolation model predicts sharp onsets in the elastic moduli of sand-shale mixtures when the shales reach a critical volume fraction. At that point, the shale inclusions form a connected cluster, and the macroscopic rock properties change with the power-law growth of the cluster. This switch-like nonlinearity preserves singularities, and hence reflections, even if no sharp transition exists in the lithology or if they are smoothed out using standard upscaling procedures.},
	keywords = {Presentation, SEG},
	doi = {10.1190/1.3255582},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/herrmann09SEGrpl/herrmann09SEGrpl.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/herrmann09SEGrpl/herrmann09SEGrpl_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2009SEGsns,
	title = {Sub-Nyquist sampling and sparsity: how to get more information from fewer samples},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {28},
	year = {2009},
	month = {10},
	pages = {3410-3415},
	publisher = {SEG},
	organization = {SEG},
	abstract = {Seismic exploration relies on the collection of massive data volumes that are subsequently mined for information during seismic processing. While this approach has been extremely successful in the past, the current trend of incessantly pushing for higher quality images in increasingly complicated regions of the Earth continues to reveal fundamental shortcomings in our workflows to handle massive high-dimensional data volumes. Two causes can be identified as the main culprits responsible for this barrier. First, there is the so-called {\textquoteleft}{\textquoteleft}curse of dimensionality{\textquoteright}{\textquoteright} exemplified by Nyquist{\textquoteright}s sampling criterion, which puts disproportionate strain on current acquisition and processing systems as the size and desired resolution of our survey areas continues to increase. Secondly, there is the recent {\textquoteleft}{\textquoteleft}departure from Moore{\textquoteright}s law{\textquoteright}{\textquoteright} that forces us to lower our expectations to compute ourselves out of this curse of dimensionality. In this paper, we offer a way out of this situation by a deliberate \emphrandomized subsampling combined with structure-exploiting transform-domain sparsity promotion. Our approach is successful because it reduces the size of seismic data volumes without loss of information. Because of this size reduction both impediments are removed and we end up with a new technology where the costs of acquisition and processing are no longer dictated by the \emphsize of the acquisition but by the transform-domain \emphsparsity of the end-product after processing.},
	keywords = {Presentation, SEG},
	doi = {10.1190/1.3255570},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/herrmann09SEGsns/herrmann09SEGsns.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/herrmann09SEGsns/herrmann09SEGsns_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {lin2009SEGucs,
	title = {Unified compressive sensing framework for simultaneous acquisition with primary estimation},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {28},
	year = {2009},
	month = {10},
	pages = {3113-3117},
	abstract = {The central promise of simultaneous acquisition is a vastly improved crew efficiency during acquisition at the cost of additional post-processing to obtain conventional source-separated data volumes. Using recent theories from the field of compressive sensing, we present a way to systematically model the effects of simultaneous acquisition. Our formulation form a new framework in the study of acquisition design and naturally leads to an inversion-based approach for the separation of shot records. Furthermore, we show how other inversion-based methods, such as a recently proposed method from van Groenestijn and Verschuur (2009) for primary estimation, can be processed together with the demultiplexing problem to achieve a better result compared to a separate treatment of these problems.},
	keywords = {Presentation, SEG},
	doi = {10.1190/1.3255502},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/lin09SEGucs/lin09SEGucs.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/lin09SEGucs/lin09SEGucs_pres.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@conference {herrmann2008SINBADacd2,
	title = {Adaptive curvelet-domain primary-multiple separation},
	booktitle = {SINBAD},
	year = {2008},
	note = {SINBAD 2008},
	publisher = {SINBAD},
	organization = {SINBAD},
	abstract = {In many exploration areas, successful separation of primaries and multiples greatly determines the quality of seismic imaging. Despite major advances made by Surface-Related Multiple Elimination (SRME), amplitude errors in the predicted multiples remain a problem. When these errors vary for each type of multiple differently (as a function of offset, time and dip), these amplitude errors pose a serious challenge for conventional least-squares matching and for the recently introduced separation by curvelet-domain thresholding. We propose a data-adaptive method that corrects amplitude errors, which vary smoothly as a function of location, scale (frequency band) and angle. In that case, the amplitudes can be corrected by an element-wise curvelet-domain scaling of the predicted multiples. We show that this scaling leads to a successful estimation of the primaries, despite amplitude, sign, timing and phase errors in the predicted multiples. Our results on synthetic and real data show distinct improvements over conventional least-squares matching, in terms of better suppression of multiple energy and high-frequency clutter and better recovery of the estimated primaries.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/herrmann2008SINBADacd2/herrmann2008SINBADacd2.pdf},
	author = {Felix J. Herrmann}
}
@conference {yarham2008SEGbgr,
	title = {Bayesian ground-roll seperation by curvelet-domain sparsity promotion},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {27},
	year = {2008},
	month = {11},
	pages = {2576-2580},
	publisher = {SEG},
	organization = {SEG},
	abstract = {The removal of coherent noise generated by surface waves in land based seismic is a prerequisite to imaging the subsurface. These surface waves, termed as ground roll, overlay important reflector information in both the t-x and f-k domains. Standard techniques of ground-roll removal commonly alter reflector information. We propose the use of the curvelet domain as a sparsifying transform in which to preform signal-separation techniques that preserves reflector information while increasing ground-roll removal. We look at how this method preforms on synthetic data for which we can build quantitative results and a real field data set.},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.3063878},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/yarham08SEGbgr/yarham08SEGbgr.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/yarham08SEGbgr/yarham08SEGbgr_pres.pdf},
	author = {Carson Yarham and Felix J. Herrmann}
}
@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 {herrmann2008IONcsa,
	title = {Compressive sampling: a new paradigm for seismic data acquistion and processing?},
	booktitle = {ION},
	year = {2008},
	abstract = {Seismic data processing and imaging are firmly rooted in the well-established paradigm of regular Nyquist sampling. Faced with a typical uncooperative environment, practitioners of seismic data acquisition make all efforts to comply to this theory by creating regularly-sampled seismic-data volumes that are suitable for Fourier-based processing flows. The current advent of new alternative transform domains{\textendash}- such as the sparsifying curvelet domain, where seismic data is decomposed into localized, multiscale and multidirectional plane waves{\textendash}- opens the possibility to change this paradigm by no longer combating sampling irregularity but by embracing it. During this talk, we show that as long as seismic data volumes permit a compressible representation{\textendash}-i.e., data can be represented as a superposition of relatively few number of elementary waveforms{\textendash}- Nyquist sampling is unnecessary pessimistic. So far, nothing new, we all know from the work on Fourier- or other transform-based seismic-data regularization methodologies that wavefields can be recovered accurately from sub-Nyquist samplings through some sort of optimization procedure. What is new, however, are recent insights from the field of "compressive sampling", which dictate the conditions that guarantee or, at least, in practice provide conditions that favor sparsity-promoting recovery from sub-Nyquist sampling. Random sub-sampling, or to be more precise, jitter sub-sampling creates favorable conditions for curvelet-based recovery. We explain this phenomenon by arguing that this type of sampling leads to noisy data, hence our slogan "Simply denoise: wavefield reconstruction via jittered undersampling", where we bank on separating incoherent sub-sampling noise with curvelet-domain sparsity promotion. During our presentation, we introduce you to what curvelets are, why random jitter sampling is important and why this opens a pathway towards a new paradigm of curvelet-domain seismic data processing. Our claims will be supported by examples on synthetic and field data. This is joint work with Gilles Hennenfent, PhD. student at SLIM.},
	keywords = {ION, Presentation, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/ION/herrmann2008IONcsa/herrmann08ion_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2008SIAMcsm,
	title = {Compressive sampling meets seismic imaging},
	booktitle = {SIAM},
	year = {2008},
	abstract = {Compressive sensing has led to fundamental new insights in the recovery of compressible signals from sub-Nyquist samplings. It is shown how jittered subsampling can be used to create favorable recovery conditions. Applications include mitigation of incomplete acquisitions and wavefield computations. While the former is a direct adaptation of compressive sampling, the latter application represents a new way of compressing wavefield extrapolation operators. Operators are not diagonalized but are compressively sampled reducing the computational costs.},
	keywords = {Presentation, SIAM, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SIAM/2008/herrmann2008SIAMcsm/herrmann2008SIAMcsm.pdf},
	author = {Felix J. Herrmann and Yogi A. Erlangga and Tim T.Y. Lin}
}
@conference {moghaddam2008SEGcbm,
	title = {Curvelet-based migration preconditioning},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {27},
	year = {2008},
	month = {11},
	pages = {2211-2215},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this paper, we introduce a preconditioner for seismic imaging{\textendash}-i.e., the inversion of the linearized Born scattering operator. This preconditioner approximately corrects for the {\textquoteleft}{\textquoteleft}square root{\textquoteright}{\textquoteright} of the normal{\textendash}-i.e., the demigration-migration operator. This approach consists of three parts, namely (i) a left preconditoner, defined by a fractional time integration designed to make the migration operator zero order, and two right preconditioners that apply (ii) a scaling in the physical domain accounting for a spherical spreading, and (iii) a curvelet-domain scaling that corrects for spatial and reflector-dip dependent amplitude errors. We show that a combination of these preconditioners lead to a significant improvement of the convergence for iterative least-squares solutions to the seismic imaging problem based on reverse-time migration operators.},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.3059325},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/moghaddam08SEGcbm/moghaddam08SEGcbm.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/moghaddam08SEGcbm/moghaddam08SEGcbm_pres.pdf},
	author = {Peyman P. Moghaddam and Cody R. Brown and Felix J. Herrmann}
}
@conference {herrmann2008SEGcdm,
	title = {Curvelet-domain matched filtering},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {27},
	number = {3643-3649},
	year = {2008},
	month = {08},
	publisher = {SEG},
	organization = {SEG},
	abstract = {Matching seismic wavefields and images lies at the heart of many pre-/post-processing steps part of seismic imaging{\textendash}- whether one is matching predicted wavefield components, such as multiples, to the actual to-be-separated wavefield components present in the data or whether one is aiming to restore migration amplitudes by scaling, using an image-to-remigrated-image matching procedure to calculate the scaling coefficients. The success of these wavefield matching procedures depends on our ability to (i) control possible overfitting, which may lead to accidental removal of energy or to inaccurate image-amplitude corrections, (ii) handle data or images with nonunique dips, and (iii) apply subsequent wavefield separations or migraton amplitude corrections stably. In this paper, we show that the curvelet transform allows us to address all these issues by imposing smoothness in phase space, by using their capability to handle conflicting dips, and by leveraging their ability to represent seismic data and images sparsely. This latter property renders curvelet-domain sparsity promotion an effective prior.},
	keywords = {Presentation, SEG, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/herrmann08SEGcdm/herrmann08SEGcdm.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/herrmann08SEGcdm/herrmann08SEGcdm_pres.pdf},
	author = {Felix J. Herrmann and Peyman P. Moghaddam and Deli Wang}
}
@conference {kumar2008CSEGcrs,
	title = {Curvelet-regularized seismic deconvolution},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2008},
	month = {05},
	publisher = {CSEG},
	organization = {CSEG},
	abstract = {There is an inherent continuity along reflectors of a seismic image. We use the recently introduced multiscale and multidirectional curvelet transform to exploit this continuity along reflectors for cases in which the assumption of spiky reflectivity may not hold. We show that such type of seismic reflectivity can be represented in the curvelet-domain by a vector whose entries decay rapidly. This curvelet-domain compression of reflectivity opens new perspectives towards solving classical problems in seismic processing including the deconvolution problem. In this paper, we present a formulation that seeks curvelet-domain sparsity for non-spiky reflectivity and we compare our results with those of spiky deconvolution.},
	keywords = {Presentation, SLIM},
	url = {http://slim.gatech.edu/Publications/Public/Conferences/CSEG/2008/kumar2008CSEGcrs/kumar2008CSEGcrs.pdf},
	presentation = {http://slim.gatech.edu/Publications/Public/Conferences/CSEG/2008/kumar2008CSEGcrs/kumar2008CSEGcrs_pres.pdf},
	author = {Vishal Kumar and Felix J. Herrmann}
}
@conference {kumar2008SEGdwc,
	title = {Deconvolution with curvelet-domain sparsity},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {27},
	year = {2008},
	month = {11},
	pages = {1996-2000},
	publisher = {SEG},
	organization = {SEG},
	abstract = {There is an inherent continuity along reflectors of a seismic image. We use the recently introduced multiscale and multidirectional curvelet transform to exploit this continuity along reflectors for cases in which the assumption of spiky reflectivity may not hold. We show that such type of seismic reflectivity can be represented in the curvelet-domain by a vector whose entries decay rapidly. This curvelet-domain compression of reflectivity opens new perspectives towards solving classical problems in seismic processing including the deconvolution problem. In this paper, we present a formulation that seeks curvelet-domain sparsity for non-spiky reflectivity and we compare our results with those of spiky deconvolution.},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.3059287},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/kumar08SEGdwc/kumar08SEGdwc.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/kumar08SEGdwc/kumar08SEGdwc_pres.pdf},
	author = {Vishal Kumar and Felix J. Herrmann}
}
@conference {herrmann2008SINBADfwr,
	title = {(De)-Focused wavefield reconstructions},
	booktitle = {SINBAD 2008},
	year = {2008},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/herrmann2008SINBADfwr/herrmann2008SINBADfwr.pdf},
	author = {Felix J. Herrmann}
}
@conference {lin2008SEGiso,
	title = {Interpolating solutions of the Helmholtz equation with compressed sensing},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {27},
	year = {2008},
	month = {01},
	pages = {2122-2126},
	publisher = {SEG},
	organization = {SEG},
	abstract = {We present an algorithm which allows us to model wavefields with frequency-domain methods using a much smaller number of frequencies than that typically required by the classical sampling theory in order to obtain an alias-free result. The foundation of the algorithm is the recent results on the compressed sensing, which state that data can be successfully recovered from an incomplete measurement if the data is sufficiently sparse. Results from numerical experiment show that only 30\% of the total frequency spectrum is need to capture the full wavefield information when working in the hard 2D synthetic Marmousi model.},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.3059307},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/lin08SEGiso/lin08SEGiso.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/lin08SEGiso/lin08SEGiso.pdf},
	author = {Tim T.Y. Lin and Evgeniy Lebed and Yogi A. Erlangga and Felix J. Herrmann}
}
@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 {herrmann2008SINBADpsm,
	title = {Phase-space matched filtering and migration preconditioning},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {During this talk, I will report on new phase-space regularization functionals defined in terms of splines. This spline representation reduces the dimensionality of estimating our phase-space matched filter. We will discuss how this filter can be used in migration preconditioning. This is joint work with Christiaan Stolk.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/herrmann2008SINBADpsm/herrmann2008SINBADpsm.pdf},
	author = {Felix J. Herrmann}
}
@conference {wang2008SINBADrri,
	title = {Recent results in curvelet-based primary-multiple separation},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {We present a nonlinear curvelet-based sparsity-promoting formulation for the primary-multiple separation problem. We show that these coherent signal components can be separated robustly by explicitly exploiting the locality of curvelets in phase space (space-spatial frequency plane) and their ability to compress data volumes that contain wavefronts. This work is an extension of earlier results and the presented algorithms are shown to be stable under noise and moderately erroneous multiple predictions.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/wang2008SINBADrri/wang2008SINBADrri.pdf},
	author = {Deli Wang and Rayan Saab and Ozgur Yilmaz and Felix J. Herrmann}
}
@conference {herrmann2008SEGgbu,
	title = {Seismic noise: the good, the bad, \& the ugly},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2008},
	keywords = {Presentation, SEG, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/herrmann08SEGgbu/herrmann08SEGgbu.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2008SEGswi,
	title = {Seismic wavefield inversion with curvelet-domain sparsity promotion},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {27},
	year = {2008},
	month = {11},
	pages = {2497-2501},
	publisher = {SEG},
	organization = {SEG},
	abstract = {Inverting seismic wavefields lies at the heart of seismic data processing and imaging{\textendash}- whether one is applying {\textquoteleft}{\textquoteleft}a poor man{\textquoteright}s inverse{\textquoteright}{\textquoteright} by correlating wavefields during imaging or whether one inverts wavefields as part of a focal transform interferrometric deconvolution or as part of computing the {\textquoteright}data verse{\textquoteright}. The success of these wavefield inversions depends on the stability of the inverse with respect to data imperfections such as finite aperture, bandwidth limitation, and missing data. In this paper, we show how curvelet domain sparsity promotion can be used as a suitable prior to invert seismic wavefields. Examples include, seismic data regularization with the focused curvelet-based recovery by sparsity-promoting inversion (fCRSI), which involves the inversion of the primary-wavefield operator, the prediction of multiples by inverting the adjoint of the primary operator, and finally the inversion of the data itself {\textendash}- the so-called {\textquoteright}data inverse{\textquoteright}. In all cases, curvelet-domain sparsity leads to a stable inversion.},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.3063862},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/herrmann08SEGswi/herrmann08SEGswi.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/herrmann08SEGswi/herrmann08SEGswi_pres.pdf},
	author = {Felix J. Herrmann and Deli Wang}
}
@conference {herrmann2008SINBADs2c,
	title = {SINBAD 2008 Consortium meeting},
	booktitle = {SINBAD 2008},
	year = {2008},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/herrmann2008SINBADs2c/herrmann2008SINBADs2c.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2007SINBADcwe,
	title = {Compressed wavefield extrapolation},
	booktitle = {SINBAD 2007},
	year = {2007},
	abstract = {An explicit algorithm for the extrapolation of one-way wavefields is proposed which combines recent developments in information theory and theoretical signal processing with the physics of wave propagation. Because of excessive memory requirements, explicit formulations for wave propagation have proven to be a challenge in 3-D. By using ideas from {\textquoteleft}{\textquoteleft}compressed sensing{\textquoteright}{\textquoteright}, we are able to formulate the (inverse) wavefield extrapolation problem on small subsets of the data volume, thereby reducing the size of the operators. According to compressed sensing theory, signals can successfully be recovered from an incomplete set of measurements when the measurement basis is incoherent with the representation in which the wavefield is sparse. In this new approach, the eigenfunctions of the Helmholtz operator are recognized as a basis that is incoherent with curvelets that are known to compress seismic wavefields. By casting the wavefield extrapolation problem in this framework, wavefields can successfully be extrapolated in the modal domain via a computationally cheaper operation. A proof of principle for the {\textquoteleft}{\textquoteleft}compressed sensing{\textquoteright}{\textquoteright} method is given for wavefield extrapolation in 2-D. The results show that our method is stable and produces identical results compared to the direct application of the full extrapolation operator. This is joint work with Tim Lin.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2007/herrmann2007SINBADcwe/herrmann2007SINBADcwe.pdf},
	author = {Felix J. Herrmann}
}
@conference {lin2007SEGcwe1,
	title = {Compressed wavefield extrapolation with curvelets},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {26},
	year = {2007},
	pages = {1997-2001},
	abstract = {An explicit algorithm for the extrapolation of one-way wavefields is proposed which combines recent developments in information theory and theoretical signal processing with the physics of wave propagation. Because of excessive memory requirements, explicit formulations for wave propagation have proven to be a challenge in 3-D. By using ideas from {\textquoteleft}{\textquoteleft}compressed sensing{\textquoteright}{\textquoteright}, we are able to formulate the (inverse) wavefield extrapolation problem on small subsets of the data volume, thereby reducing the size of the operators. According to compressed sensing theory, signals can successfully be recovered from an imcomplete set of measurements when the measurement basis is incoherent with the representation in which the wavefield is sparse. In this new approach, the eigenfunctions of the Helmholtz operator are recognized as a basis that is incoherent with curvelets that are known to compress seismic wavefields. By casting the wavefield extrapolation problem in this framework, wavefields can successfully be extrapolated in the modal domain via a computationally cheaper operation. A proof of principle for the {\textquoteleft}{\textquoteleft}compressed sensing{\textquoteright}{\textquoteright} method is given for wavefield extrapolation in 2-D. The results show that our method is stable and produces identical results compared to the direct application of the full extrapolation operator. \copyright2007 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.2792882},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/lin07SEGcwe1/lin07SEGcwe1.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/lin07SEGcwe1/lin07SEGcwe1_pres.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@conference {herrmann2007PIMScsm,
	title = {Compressive sampling meets seismic imaging},
	booktitle = {PIMS},
	year = {2007},
	keywords = {PIMS, Presentation, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/PIMS/2007/herrmann07PIMScsm/herrmann07PIMScsm_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2007AMScsi,
	title = {Compressive seismic imaging},
	booktitle = {AMS Von Neumann},
	year = {2007},
	abstract = {Seismic imaging involves the solution of an inverse-scattering problem during which the energy of (extremely) large data volumes is collapsed onto the Earth{\textquoteright}s reflectors. We show how the ideas from "compressive sampling" can alleviate this task by exploiting the curvelet transform{\textquoteright}s "wavefront-set detection" capability and "invariance" property under wave propagation. First, a wavelet-vaguellete technique is reviewed, where seismic amplitudes are recovered from complete data by diagonalizing the Gramm matrix of the linearized scattering problem. Next, we show how the recovery of seismic wavefields from incomplete data can be cast into a compressive sampling problem, followed by a proposal to compress wavefield extrapolation operators via compressive sampling in the modal domain. During the latter approach, we explicitly exploit the mutual incoherence between the eigenfunctions of the Helmholtz operator and the curvelet frame elements that compress the extrapolated wavefield. This is joint work with Gilles Hennenfent, Peyman Moghaddam, Tim Lin, Chris Stolk and Deli Wang.},
	keywords = {AMS Von Neumann, Presentation, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/vonNeuman/2007/herrmann07AMScsi/herrmann07AMScsi_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {hennenfent2007EAGEcrw,
	title = {Curvelet reconstruction with sparsity-promoting inversion: successes and challenges},
	booktitle = {EAGE Workshop on Curvelets, contourlets, seislets, {\textellipsis} in seismic data processing - where are we and where are we going?},
	year = {2007},
	month = {06},
	abstract = {In this overview of the recent Curvelet Reconstruction with Sparsity-promoting Inversion (CRSI) method, we present our latest 2-D and 3-D interpolation results on both synthetic and real datasets. We compare these results to interpolated data using other existing methods. Finally, we discuss the challenges related to sparsity-promoting solvers for the large-scale problems the industry faces.},
	keywords = {EAGE, Presentation, SLIM, workshop},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/hennenfent07EAGEcrw/hennenfent07EAGEcrw.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/hennenfent07EAGEcrw/hennenfent07EAGEcrw_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=7553},
	author = {Gilles Hennenfent and Felix J. Herrmann}
}
@conference {saab2007SEGcbp,
	title = {Curvelet-based primary-multiple separation from a Bayesian perspective},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {26},
	year = {2007},
	pages = {2510-2514},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this abstract, we present a novel primary-multiple separation scheme which makes use of the sparsity of both primaries and multiples in a transform domain, such as the curvelet transform, to provide estimates of each. The proposed algorithm utilizes seismic data as well as the output of a preliminary step that provides (possibly) erroneous predictions of the multiples. The algorithm separates the signal components, i.e., the primaries and multiples, by solving an optimization problem that assumes noisy input data and can be derived from a Bayesian perspective. More precisely, the optimization problem can be arrived at via an assumption of a weighted Laplacian distribution for the primary and multiple coefficients in the transform domain and of white Gaussian noise contaminating both the seismic data and the preliminary prediction of the multiples, which both serve as input to the algorithm. \copyright2007 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.2792988},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/saab07SEGcbp/saab07SEGcbp.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/saab07SEGcbp/saab07SEGcbp_pres.pdf},
	author = {Rayan Saab and Deli Wang and Ozgur Yilmaz and Felix J. Herrmann}
}
@conference {herrmann2007SINBADfrw,
	title = {Focused recovery with the curvelet transform},
	booktitle = {SINBAD 2007},
	year = {2007},
	abstract = {Incomplete data represents a major challenge for a successful prediction and subsequent removal of multiples. In this paper, a new method will be represented that tackles this challenge in a two-step approach. During the first step, the recently developed curvelet-based recovery by sparsity-promoting inversion (CRSI) is applied to the data, followed by a prediction of the primaries. During the second high-resolution step, the estimated primaries are used to improve the frequency content of the recovered data by combining the focal transform, defined in terms of the estimated primaries, with the curvelet transform. This focused curvelet transform leads to an improved recovery, which can subsequently be used as input for a second stage of multiple prediction and primary-multiple separation. This is joint work with Deli Wang and Gilles Hennenfent.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/sites/data/Papers/Herrmann2007SINBADfoc.pdf},
	author = {Felix J. Herrmann}
}
@conference {hennenfent2007EAGEisf,
	title = {Irregular sampling: from aliasing to noise},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2007},
	month = {06},
	abstract = {Seismic data is often irregularly and/or sparsely sampled along spatial coordinates. We show that these acquisition geometries are not necessarily a source of adversity in order to accurately reconstruct adequately-sampled data. We use two examples to illustrate that it may actually be better than equivalent regularly subsampled data. This comment was already made in earlier works by other authors. We explain this behavior by two key observations. Firstly, a noise-free underdetermined problem can be seen as a noisy well-determined problem. Secondly, regularly subsampling creates strong coherent acquisition noise (aliasing) difficult to remove unlike the noise created by irregularly subsampling that is typically weaker and Gaussian-like.},
	keywords = {EAGE, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/hennenfent07EAGEisf/hennenfent07EAGEisf.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/hennenfent07EAGEisf/hennenfent07EAGEisf_pres.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/hennenfent07EAGEisf/hennenfent07EAGEisf_WS.pdf},
	author = {Gilles Hennenfent and Felix J. Herrmann}
}
@conference {herrmann2007EAGEjda,
	title = {Just diagonalize: a curvelet-based approach to seismic amplitude recovery},
	booktitle = {EAGE Workshop on Curvelets, contourlets, seislets, {\textellipsis} in seismic data processing - where are we and where are we going?},
	year = {2007},
	month = {06},
	abstract = {In his presentation we present a nonlinear curvelet-based sparsity-promoting formulation for the recovery of seismic amplitudes. We show that the curvelet{\textquoteright}s wavefront detection capability and invariance under wave propagation lead to a formulation of this recovery problem that is stable under noise and missing data. \copyright2007 Society of Exploration Geophysicists},
	keywords = {EAGE, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2007/herrmann2007EAGEjda/herrmann2007EAGEjda_paper.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2007/herrmann2007EAGEjda/herrmann2007EAGEjda_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=7555},
	author = {Felix J. Herrmann and Peyman P. Moghaddam}
}
@conference {herrmann2007SEGmpf,
	title = {Multiple prediction from incomplete data with the focused curvelet transform},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {26},
	year = {2007},
	pages = {2505-2600},
	abstract = {Incomplete data represents a major challenge for a successful prediction and subsequent removal of multiples. In this paper, a new method will be represented that tackles this challenge in a two-step approach. During the first step, the recenly developed curvelet-based recovery by sparsity-promoting inversion (CRSI) is applied to the data, followed by a prediction of the primaries. During the second high-resolution step, the estimated primaries are used to improve the frequency content of the recovered data by combining the focal transform, defined in terms of the estimated primaries, with the curvelet transform. This focused curvelet transform leads to an improved recovery, which can subsequently be used as input for a second stage of multiple prediction and primary-multiple separation.},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.2792987},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/herrmann07SEGmpf/herrmann07SEGmpf.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/herrmann07SEGmpf/herrmann07SEGmpf_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {verschuur2007SEGmmp,
	title = {Multiterm multiple prediction using separated reflections and diffractions combined with curvelet-based subtraction},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {26},
	year = {2007},
	pages = {2535-2539},
	publisher = {SEG},
	organization = {SEG},
	abstract = {The surface-related multiple elimination (SRME) method has proven to be successful on a large number of data cases. Most of the applications are still 2D, as the full 3D implementation is still expensive and under development. However, the earth is a 3D medium, such that 3D effects are difficult to avoid. Most of the 3D effects come from diffractive structures, whereas the specular reflections normally have less of a 3D behavior. By separating the seismic data in a specular reflecting and a diffractive part, multiple prediction can be carried out with these different subsets of the input data, resulting in several categories of predicted multiples. Because each category of predicted multiples can be subtracted from the input data with different adaptation filters, a more flexible SRME procedure is obtained. Based on some initial results from a Gulf of Mexico dataset, the potential of this approach is investigated. \copyright2007 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.2792993},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/verschuur07SEGmmp/verschuur07SEGmmp.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/verschuur07SEGmmp/verschuur07SEGmmp_pres.pdf},
	author = {D. J. Verschuur and Deli Wang and Felix J. Herrmann}
}
@conference {herrmann2007COIPpti,
	title = {Phase transitions in explorations seismology: statistical mechanics meets information theory},
	booktitle = {COIP},
	year = {2007},
	abstract = {In this paper, two different applications of phase transitions to exploration seismology will be discussed. The first application concerns a phase diagram ruling the recovery conditions for seismic data volumes from incomplete and noisy data while the second phase transition describes the behavior of bi-compositional mixtures as a function of the volume fraction. In both cases, the phase transitions are the result of randomness in large system of equations in combination with nonlinearity. The seismic recovery problem from incomplete data involves the inversion of a rectangular matrix. Recent results from the field of "compressive sensing" provide the conditions for a successful recovery of functions that are sparse in some basis (wavelet) or frame (curvelet) representation, by means of a sparsity ($\ell_1$-norm) promoting nonlinear program. The conditions for a successful recovery depend on a certain randomness of the matrix and on two parameters that express the matrix{\textquoteright} aspect ratio and the ratio of the number of nonzero entries in the coefficient vector for the sparse signal representation over the number of measurements. It appears that the ensemble average for the success rate for the recovery of the sparse transformed data vector by a nonlinear sparsity promoting program, can be described by a phase transition, demarcating the regions for the two ratios for which recovery of the sparse entries is likely to be successful or likely to fail. Consistent with other phase transition phenomena, the larger the system the sharper the transition. The randomness in this example is related to the construction of the matrix, which for the recovery of spike trains corresponds to the randomly restricted Fourier matrix. It is shown, that these ideas can be extended to the curvelet recovery by sparsity-promoting inversion (CRSI). The second application of phase transitions in exploration seismology concerns the upscaling problem. To counter the intrinsic smoothing of singularities by conventional equivalent medium upscaling theory, a percolation-based nonlinear switch model is proposed. In this model, the transport properties of bi-compositional mixture models for rocks undergo a sudden change in the macroscopic transport properties as soon as the volume fraction of the stronger material reaches a critical point. At this critical point, the stronger material forms a connected cluster, which leads to the creation of a cusp-like singularity in the elastic moduli, which in turn give rise to specular reflections. In this model, the reflectivity is no longer explicitly due to singularities in the rocks composition. Instead, singularities are created whenever the volume fraction exceeds the critical point. We will show that this concept can be used for a singularity-preserved lithological upscaling.},
	keywords = {Presentation, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/COIP/2007/herrmann07COIPpti/herrmann07COIPpti_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {hennenfent2007SEGrsn,
	title = {Random sampling: new insights into the reconstruction of coarsely sampled wavefields},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2007},
	pages = {2575-2579},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this paper, we turn the interpolation problem of coarsely-sampled data into a denoising problem. From this point of view, we illustrate the benefit of random sampling at sub-Nyquist rate over regular sampling at the same rate. We show that, using nonlinear sparsity-promoting optimization, coarse random sampling may actually lead to significantly better wavefield reconstruction than equivalent regularly sampled data. \copyright2007 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.2793002},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/hennenfent07SEGrsn/hennenfent07SEGrsn.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/hennenfent07SEGrsn/hennenfent07SEGrsn_pres.pdf},
	author = {Gilles Hennenfent and Felix J. Herrmann}
}
@conference {herrmann2007EAGErdi,
	title = {Recent developments in curvelet-based seismic processing},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2007},
	month = {06},
	abstract = {Combinations of parsimonious signal representations with nonlinear sparsity promoting programs hold the key to the next-generation of seismic data processing algorithms, since they allow for a formulation that is stable w.r.t. noise \& incomplete data do not require prior information on the velocity or locations and dips of the events},
	keywords = {EAGE, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/herrmann07EAGErdi/herrmann07EAGErdi.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/herrmann07EAGErdi/herrmann07EAGErdi_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=7548},
	author = {Felix J. Herrmann}
}
@conference {herrmann2007SINBADrdi2,
	title = {Recent developments in primary-multiple separation},
	booktitle = {SINBAD 2007},
	year = {2007},
	abstract = {In this talk, we present a novel primary-multiple separation scheme which makes use of the sparsity of both primaries and multiples in a transform domain, such as the curvelet transform, to provide estimates of each. The proposed algorithm utilizes seismic data as well as the output of a preliminary step that provides (possibly) erroneous predictions of the multiples. The algorithm separates the signal components, i.e., the primaries and multiples, by solving an optimization problem that assumes noisy input data and can be derived from a Bayesian perspective. More precisely, the optimization problem can be arrived at via an assumption of a weighted Laplacian distribution for the primary and multiple coefficients in the transform domain and of white Gaussian noise contaminating both the seismic data and the preliminary prediction of the multiples, which both serve as input to the algorithm. Time permitted, we will also briefly discuss a propasal for adaptive curvelet-domain matched filtering. This is joint work with Deli Wang, Rayan Saaba, {\o}zgur Yilmaz and Eric Verschuur.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2007/herrmann07SINBADrdi2/herrmann07SINBADrdi2_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {hennenfent2007SINBADrii,
	title = {Recent insights in $\ell_1$ solvers},
	booktitle = {SINBAD 2007},
	year = {2007},
	abstract = {During this talk, an overview is given on our work on norm-one solvers as part of the DNOISE project. Gilles will explain the ins and outs of our iterative thresholding solver based on log cooling while Felix will present the work of Michael Friedlander "A Newton root-finding algorithms for large-scale basis pursuit denoise". Both approaches involve the solution of the basis pursuit problem that seeks a minimum one-norm solution of an underdetermined least-squares problem. Basis pursuit denoise (BPDN) fits the least-squares problem only approximately, and a single parameter determines a curve that traces the trade-off between the least-squares fit and the one-norm of the solution. In the work of Friedlander, it is shown show that the function that describes this curve is convex and continuously differentiable over all points of interest. They describe an efficient procedure for evaluating this function and its derivatives. As a result, they can compute arbitrary points on this curve. Their method is suitable for large-scale problems. Only matrix-vector operations are required. This is joint work with Ewout van der Berg and Michael P. Friedlander},
	keywords = {Presentation, SINBAD, SLIM},
	author = {Gilles Hennenfent and Felix J. Herrmann}
}
@conference {wang2007SEGrri,
	title = {Recent results in curvelet-based primary-multiple separation: application to real data},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {26},
	year = {2007},
	pages = {2500-2504},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this abstract, we present a nonlinear curvelet-based sparsity-promoting formulation for the primary-multiple separation problem. We show that these coherent signal components can be separated robustly by explicitly exploting the locality of curvelets in phase space (space-spatial frequency plane) and their ability to compress data volumes that contain wavefronts. This work is an extension of earlier results and the presented algorithms are shown to be stable under noise and moderately erroneous multiple predictions. \copyright2007 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.2792986},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/wang07SEGrri/wang07SEGrri.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/wang07SEGrri/wang07SEGrri_pres.pdf},
	author = {Deli Wang and Rayan Saab and Ozgur Yilmaz and Felix J. Herrmann}
}
@conference {moghaddam2007SEGrsi,
	title = {Robust seismic-images amplitude recovery using curvelets},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {26},
	year = {2007},
	pages = {2225-2229},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this paper, we recover the amplitude of a seismic image by approximating the normal (demigration-migration) operator. In this approximation, we make use of the property that curvelets remain invariant under the action of the normal operator. We propose a seismic amplitude recovery method that employs an eigenvalue like decomposition for the normal operator using curvelets as eigen-vectors. Subsequently, we propose an approximate non-linear singularity-preserving solution to the least-squares seismic imaging problem with sparseness in the curvelet domain and spatial continuity constraints. Our method is tested with a reverse-time {\textquoteleft}wave-equation{\textquoteright} migration code simulating the acoustic wave equation on the SEG-AA salt model. \copyright2007 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.2792928},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/moghaddam07SEGrsi/moghaddam07SEGrsi.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/moghaddam07SEGrsi/moghaddam07SEGrsi_pres.pdf},
	author = {Peyman P. Moghaddam and Felix J. Herrmann and Christiaan C. Stolk}
}
@conference {herrmann2007SEGsdp,
	title = {Seismic data processing with curvelets: a multiscale and nonlinear approach},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {26},
	year = {2007},
	pages = {2220-2224},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this abstract, we present a nonlinear curvelet-based sparsity-promoting formulation of a seismic processing flow, consisting of the following steps: seismic data regularization and the restoration of migration amplitudes. We show that the curvelet{\textquoteright}s wavefront detection capability and invariance under the migration-demigration operator lead to a formulation that is stable under noise and missing data. \copyright2007 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.2792927},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/herrmann07SEGsdp/herrmann07SEGsdp.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/herrmann07SEGsdp/herrmann07SEGsdp_pres.pdf},
	author = {Felix J. Herrmann and Deli Wang and Gilles Hennenfent and Peyman P. Moghaddam}
}
@conference {herrmann2007SINBADsia2,
	title = {Seismic image amplitude recovery},
	booktitle = {SINBAD 2007},
	year = {2007},
	abstract = {In this talk, we recover the amplitude of a seismic image by approximating the normal (demigration-migration) operator. In this approximation, we make use of the property that curvelets remain invariant under the action of the normal operator. We propose a seismic amplitude recovery method that employs an eigenvalue like decomposition for the normal operator using curvelets as eigen-vectors. Subsequently, we propose an approximate nonlinear singularity-preserving solution to the least-squares seismic imaging problem with sparseness in the curvelet domain and spatial continuity constraints. Our method is tested with a reverse-time {\textquoteright}wave-equation{\textquoteright} migration code simulating the acoustic wave equation on the SEG-AA salt model. This is joint work with Peyman Moghaddam and Chris Stolk (University of Twente)},
	keywords = {Presentation, SINBAD, SLIM},
	author = {Felix J. Herrmann}
}
@conference {herrmann2007EAGEsia,
	title = {Seismic imaging and processing with curvelets},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2007},
	month = {06},
	abstract = {In this paper, we present a nonlinear curvelet-based sparsity-promoting formulation for three problems in seismic processing and imaging namely, seismic data regularization from data with large percentages of traces missing; seismic amplitude recovery for sub-salt images obtained by reverse-time migration and primary-multiple separation, given an inaccurate multiple prediction. We argue why these nonlinear formulations are beneficial.},
	keywords = {EAGE, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/herrmann07EAGEsia/herrmann07EAGEsia.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/herrmann07EAGEsia/herrmann07EAGEsia_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=7075},
	author = {Felix J. Herrmann and Gilles Hennenfent and Peyman P. Moghaddam}
}
@conference {herrmann2007AIPsit,
	title = {Seismic inversion through operator overloading},
	booktitle = {AIP},
	year = {2007},
	abstract = {Inverse problems in (exploration) seismology are known for their large to very large scale. For instance, certain sparsity-promoting inversion techniques involve vectors that easily exceed 230 unknowns while seismic imaging involves the construction and application of matrix-free discretized operators where single matrix-vector evaluations may require hours, days or even weeks on large compute clusters. For these reasons, software development in this field has remained the domain of highly technical codes programmed in low-level languages with little eye for easy development, code reuse and integration with (nonlinear) programs that solve inverse problems. Following ideas from the Symes{\textquoteright} Rice Vector Library and Bartlett{\textquoteright}s C++ object-oriented interface, Thyra, and Reduction/Transformation operators (both part of the Trilinos software package), we developed a software-development environment based on overloading. This environment provides a pathway from in-core prototype development to out-of-core and MPI {\textquoteright}production{\textquoteright} code with a high level of code reuse. This code reuse is accomplished by integrating the out-of-core and MPI functionality into the dynamic object-oriented programming language Python. This integration is implemented through operator overloading and allows for the development of a coordinate-free solver framework that (i) promotes code reuse; (ii) analyses the statements in an abstract syntax tree and (iii) generates executable statements. In the current implementation, we developed an interface to generate executable statements for the out-of-core unix-pipe based (seismic) processing package RSF-Madagascar (rsf.sf.net). The modular design allows for interfaces to other seismic processing packages and to in-core Python packages such as numpy. So far, the implementation overloads linear operators and elementwise reduction/transformation operators. We are planning extensions towards nonlinear operators and integration with existing (parallel) solver frameworks such as Trilinos.},
	keywords = {AIP, Presentation, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/AIP/2007/herrmann07AIPsit/herrmann07AIPsit.pdf},
	author = {Felix J. Herrmann}
}
@conference {maysami2007EAGEsrc,
	title = {Seismic reflector characterization by a multiscale detection-estimation method},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2007},
	month = {06},
	abstract = {Seismic transitions of the subsurface are typically considered as zero-order singularities (step functions). According to this model, the conventional deconvolution problem aims at recovering the seismic reflectivity as a sparse spike train. However, recent multiscale analysis on sedimentary records revealed the existence of accumulations of varying order singularities in the subsurface, which give rise to fractional-order discontinuities. This observation not only calls for a richer class of seismic reflection waveforms, but it also requires a different methodology to detect and characterize these reflection events. For instance, the assumptions underlying conventional deconvolution no longer hold. Because of the bandwidth limitation of seismic data, multiscale analysis methods based on the decay rate of wavelet coefficients may yield ambiguous results. We avoid this problem by formulating the estimation of the singularity orders by a parametric nonlinear inversion method.},
	keywords = {EAGE, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/maysami07EAGEsrc/maysami07EAGEsrc.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/maysami07EAGEsrc/maysami07EAGEsrc_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=7081},
	author = {Mohammad Maysami and Felix J. Herrmann}
}
@conference {herrmann2007CYBERsmc,
	title = {Seismology meets compressive sampling},
	booktitle = {Cyber},
	year = {2007},
	note = {Presented at the joint NSF-IPAM meeting. Los Angeles. October, 2007},
	abstract = {Presented at Cyber-Enabled Discovery and Innovation: Knowledge Extraction as a success story lecture. See for more detail https://www.ipam.ucla.edu/programs/cdi2007/},
	keywords = {Cyber, Presentation, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/Cyber/2007/herrmann07CYBERsmc/herrmann07CYBERsmc.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2007AIPssd,
	title = {Stable seismic data recovery},
	booktitle = {AIP},
	year = {2007},
	abstract = {In this talk, directional frames, known as curvelets, are used to recover seismic data and images from noisy and incomplete data. Sparsity and invariance properties of curvelets are exploited to formulate the recovery by a {\textquoteleft}1-norm promoting program. It is shown that our data recovery approach is closely linked to the recent theory of {\textquoteleft}{\textquoteleft}compressive sensing{\textquoteright}{\textquoteright} and can be seen as a first step towards a nonlinear sampling theory for wavefields. The second problem that will be discussed concerns the recovery of the amplitudes of seismic images in clutter. There, the invariance of curvelets is used to approximately invert the Gramm operator of seismic imaging. In the high-frequency limit, this Gramm matrix corresponds to a pseudo-differential operator, which is near diagonal in the curvelet domain.In this talk, directional frames, known as curvelets, are used to recover seismic data and images from noisy and incomplete data. Sparsity and invariance properties of curvelets are exploited to formulate the recovery by a l1-norm promoting program. It is shown that our data recovery approach is closely linked to the recent theory of {\textquoteleft}{\textquoteleft}compressive sensing{\textquoteright}{\textquoteright} and can be seen as a first step towards a nonlinear sampling theory for wavefields. The second problem that will be discussed concerns the recovery of the amplitudes of seismic images in clutter. There, the invariance of curvelets is used to approximately invert the Gramm operator of seismic imaging. In the high-frequency limit, this Gramm matrix corresponds to a pseudo-differential operator, which is near diagonal in the curvelet domain.},
	keywords = {AIP, Presentation, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/AIP/2007/herrmann07AIPssd/herrmann07AIPssd.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2007EAGEsrm,
	title = {Surface related multiple prediction from incomplete data},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2007},
	month = {06},
	abstract = {Incomplete data, unknown source-receiver signatures and free-surface reflectivity represent challenges for a successful prediction and subsequent removal of multiples. In this paper, a new method will be represented that tackles these challenges by combining what we know about wavefield (de-)focussing, by weighted convolutions/correlations, and recently developed curvelet-based recovery by sparsity-promoting inversion (CRSI). With this combination, we are able to leverage recent insights from wave physics towards a nonlinear formulation for the multiple-prediction problem that works for incomplete data and without detailed knowledge on the surface effects.},
	keywords = {EAGE, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/herrmann07EAGEsrm/herrmann07EAGEsrm.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/herrmann07EAGEsrm/herrmann07EAGEsrm_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=6496},
	author = {Felix J. Herrmann}
}
@conference {hennenfent2006SEGaos,
	title = {Application of stable signal recovery to seismic data interpolation},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2006},
	pages = {2797-2801},
	publisher = {SEG},
	organization = {SEG},
	abstract = {We propose a method for seismic data interpolation based on 1) the reformulation of the problem as a stable signal recovery problem and 2) the fact that seismic data is sparsely represented by curvelets. This method does not require information on the seismic velocities. Most importantly, this formulation potentially leads to an explicit recovery condition. We also propose a large-scale problem solver for the 1-regularization minimization involved in the recovery and successfully illustrate the performance of our algorithm on 2D synthetic and real examples. \copyright2006 Society of Exploration Geophysicists},
	keywords = {amplitude, continuity, curvelets, fast transform, interpolation, iterative thresholding, Presentation, regularization minimization, SEG, seismic data, SLIM},
	doi = {10.1190/1.2370105},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2006/hennenfent06SEGaos/hennenfent06SEGaos.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2006/hennenfent06SEGaos/hennenfent06SEGaos_pres.pdf},
	author = {Gilles Hennenfent and Felix J. Herrmann}
}
@conference {herrmann2006SINBADmpf,
	title = {Multiple prediction from incomplete data},
	booktitle = {SINBAD 2006},
	year = {2006},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/herrmann2006SINBADmpf/herrmann2006SINBADmpf.pdf},
	author = {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 {herrmann2006SINBADpms,
	title = {Primary-multiple separation by curvelet frames},
	booktitle = {SINBAD 2006},
	volume = {170},
	year = {2006},
	pages = {781-799},
	publisher = {Geophysical Journal International},
	organization = {Geophysical Journal International},
	abstract = {Predictive multiple suppression methods consist of two main steps: a prediction step, during which multiples are predicted from seismic data, and a primary-multiple separation step, during which the predicted multiples are {\textquoteright}matched{\textquoteright} with the true multiples in the data and subsequently removed. The last step is crucial in practice: an incorrect separation will cause residual multiple energy in the result or may lead to a distortion of the primaries, or both. To reduce these adverse effects, a new transformed-domain method is proposed where primaries and multiples are separated rather than matched. This separation is carried out on the basis of differences in the multiscale and multidirectional characteristics of these two signal components. Our method uses the curvelet transform, which maps multidimensional data volumes into almost orthogonal localized multidimensional prototype waveforms that vary in directional and spatio-temporal content. Primaries-only and multiples-only signal components are recovered from the total data volume by a nonlinear optimization scheme that is stable under noisy input data. During the optimization, the two signal components are separated by enhancing sparseness (through weighted l1-norms) in the transformed domain subject to fitting the observed data as the sum of the separated components to within a user-defined tolerance level. Whenever the prediction for the two signal components in the transformed domain correlate, the recovery is suppressed while for regions where the correlation is small the method seeks the sparsest set of coefficients that represent each signal component. Our algorithm does not seek a matched filter and as such it differs fundamentally from traditional adaptive subtraction methods. The method derives its stability from the sparseness obtained by a non-parametric multiscale and multidirectional overcomplete signal representation. This sparsity serves as prior information and allows for a Bayesian interpretation of our method during which the log-likelihood function is minimized while the two signal components are assumed to be given by a superposition of prototype waveforms, drawn independently from a probability function that is weighted by the predicted primaries and multiples. In this paper, the predictions are based on the data-driven surface-related multiple elimination (SRME) method. Synthetic and field data examples show a clean separation leading to a considerable improvement in multiple suppression compared to the conventional method of adaptive matched filtering. This improved separation translates into an improved stack.},
	keywords = {Presentation, SINBAD, SLIM},
	doi = {10.1111/j.1365-246X.2007.03360.x},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/herrmann2006SINBADpms/herrmann2006SINBADpms.pdf},
	author = {Felix J. Herrmann and Urs Boeniger and D. J. Verschuur}
}
@conference {herrmann2006SINBADapo1,
	title = {A primer on sparsity transforms: curvelets and wave atoms},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {During this presentation an overview will be given on the different sparsity transforms that are used at SLIM. Emphasis will be on two directional and multiscale wavelet transforms, namely the curvelet and the recently introduced wave-atom transforms. The main properties of these transforms will be listed and their performance on seismic data will be discussed.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/hennenfent2006SINBADapo/hennenfent2006SINBADapo.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2006SINBADapow,
	title = {A primer on weak conditions for stable recovery},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {During this presentation an introduction will be given on the method of stable recovery from noisy and incomplete data. Weak recovery conditions that guarantee the recovery for typical acquisition geometries will be reviewed and numerical recovery examples will be presented. The advantage of these weak conditions is that they are less pessimistic and {\textquoteleft}verifiable{\textquoteright} or very large-scale acquisition geometries.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/herrmann2006SINBADapow/herrmann2006SINBADapow.pdf},
	author = {Felix J. Herrmann}
}
@conference {sastry2007SINBADrfu,
	title = {Recovery from unstructured data},
	booktitle = {SINBAD 2006},
	year = {2006},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/sastry2007SINBADrfu/sastry2007SINBADrfu.pdf},
	author = {Challa S. Sastry and Gilles Hennenfent and Felix J. Herrmann}
}
@conference {herrmann2006SINBADsac,
	title = {Sparsity- and continuity-promoting seismic image recovery with curvelets},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {A nonlinear singularity-preserving solution to seismic image recovery with sparseness and continuity constraints is proposed. The method explicitly explores the curvelet transform as a directional frame expansion that, by virtue of its sparsity on seismic images and its invariance under the Hessian of the linearized imaging problem, allows for a stable recovery of the migration amplitudes from noisy data. The method corresponds to a preconditioning that corrects the amplitudes during a post-processing step. The solution is formulated as a nonlinear optimization problem where sparsity in the curvelet domain as well as continuity along the imaged reflectors are jointly promoted. To enhance sparsity, the l1-norm on the curvelet coefficients is minimized while continuity is promoted by minimizing an anisotropic diffusion norm on the image. The performance of the recovery scheme is evaluated with {\textquoteright}wave-equation{\textquoteright} migration code on a synthetic dataset. This is joint work with Peyman Moghaddam.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/herrmann2006SINBADsac/herrmann2006SINBADsac.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2006SINBADsra,
	title = {Stable recovery and separation of seismic data},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {During this presentation an overview will be given on how seismic data regularization and separation problems can be cast into the framework of stable signal recovery. It is shown that the successful solution of these two problems depends on the existence of signal expansions that are compressible. Preliminary examples will be shown.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/herrmann2006SINBADsra/herrmann2006SINBADsra.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2005CSEGnld,
	title = {Non-linear data continuation with redundant frames},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2005},
	month = {05},
	publisher = {CSEG},
	organization = {CSEG},
	abstract = {We propose an efficient iterative data interpolation method using continuity along reflectors in seismic images via curvelet and discrete cosine transforms. The curvelet transform is a new multiscale transform that provides sparse representations for images that comprise smooth objects separated by piece-wise smooth discontinuities (e.g. seismic images). The advantage of using curvelets is that these frames are sparse for high-frequency caustic-free solutions of the wave-equation. Since we are dealing with less than ideal data (e.g. bandwidth-limited), we compliment the curvelet frames with the discrete cosine transform. The latter is motivated by the successful data continuation with the discrete Fourier transform. By choosing generic basis functions we circumvent the necessity to make parametric assumptions (e.g., through linear/parabolic Radon or demigration) regarding the shape of events in seismic data. Synthetic and real data examples demonstrate that our algorithm provides interpolated traces that accurately reproduce the wavelet shape as well as the AVO behavior along events in shot gathers.},
	keywords = {Presentation, SLIM},
	url = {http://www.cseg.ca/assets/files/resources/abstracts/2005/101S0201-Herrmann_F_Non_Linear_Data_Continuation.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2005/Herrmann05CSEGnld/Herrmann05CSEGnld_pres.pdf},
	author = {Felix J. Herrmann and Gilles Hennenfent}
}
@conference {herrmann2005CSEGnlr,
	title = {Non-linear regularization in seismic imaging},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2005},
	month = {05},
	publisher = {CSEG},
	organization = {CSEG},
	abstract = {Two complementary solution strategies to the least-squares imaging problem with sparseness \& continuity continuity constraints are proposed. The applied formalism explores the sparseness of curvelets coefficients of the reflectivity and their invariance under the demigration-migration operator. We achieve the solution by jointly minimizing a weighted l1-norm on the curvelet coefficients and an anisotropic difussion or total variation norm on the imaged reflectivity model. The l1-norm exploits the sparsenss of the reflectivity in the curvelet domain whereas the anisotropic norm enhances the continuity along the reflections while removing artifacts residing in between reflectors. While the two optimization methods (convex versus non-convex) share the same type of regularization, they differ in flexibility how to handle additional constraints on the coefficients of the imaged reflectivity and in computational expense. A brief sketch of the theory is provided along with a number of synthetic examples.},
	keywords = {Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2005/Herrmann05CSEGnlr/Herrmann05CSEGnlr.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2005/Herrmann05CSEGnlr/Herrmann05CSEGnlr_pres.pdf},
	author = {Felix J. Herrmann and Peyman P. Moghaddam}
}
@conference {herrmann2005EAGErcd,
	title = {Robust curvelet-domain data continuation with sparseness constraints},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2005},
	month = {06},
	abstract = {A robust data interpolation method using curvelets frames is presented. The advantage of this method is that curvelets arguably provide an optimal sparse representation for solutions of wave equations with smooth coefficients. As such curvelets frames circum- vent {\textendash} besides the assumption of caustic-free data {\textendash} the necessity to make parametric assumptions (e.g. through linear/parabolic Radon or demigration) regarding the shape of events in seismic data. A brief sketch of the theory is provided as well as a number of examples on synthetic and real data.},
	keywords = {EAGE, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2005/Herrmann05EAGErcd/Herrmann05EAGErcd.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2005/Herrmann05EAGErcd/Herrmann05EAGErcd.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=1112},
	author = {Felix J. Herrmann and Gilles Hennenfent}
}
@conference {hennenfent2005EAGEsdr,
	title = {Seismic deconvolution revisited with curvelet frames},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2005},
	month = {06},
	abstract = {We propose an efficient iterative curvelet-regularized deconvolution algorithm that exploits continuity along reflectors in seismic images. Curvelets are a new multiscale transform that provides sparse representations for images (such as seismic images) that comprise smooth objects separated by piece-wise smooth discontinuities. Our technique combines conjugate gradient-based convolution operator inversion with noise regularization that is performed using non-linear curvelet coefficient shrinkage (thresholding). The shrinkage operation leverages the sparsity of curvelets representations. Simulations demonstrate that our algorithm provides improved resolution compared to the traditional Wiener-based deconvolution approach.},
	keywords = {EAGE, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2005/Hennenfent05EAGEsdr/Hennenfent05EAGEsdr.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2005/Hennenfent05EAGEsdr/Hennenfent05EAGEsdr_poster.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=1383},
	author = {Gilles Hennenfent and R. Neelamani and Felix J. Herrmann}
}
@conference {hennenfent2005SEGscd,
	title = {Sparseness-constrained data continuation with frames: applications to missing traces and aliased signals in 2/3-D},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2005},
	pages = {2162-2165},
	publisher = {SEG},
	organization = {SEG},
	abstract = {We present a robust iterative sparseness-constrained interpolation algorithm using 2-/3-D curvelet frames and Fourier-like transforms that exploits continuity along reflectors in seismic data. By choosing generic transforms, we circumvent the necessity to make parametric assumptions (e.g. through linear/parabolic Radon or demigration) regarding the shape of events in seismic data. Simulation and real data examples for data with moderately sized gaps demonstrate that our algorithm provides interpolated traces that accurately reproduce the wavelet shape as well as the AVO behavior. Our method also shows good results for de-aliasing judged by the behavior of the ($f-k$)-spectrum before and after regularization. \copyright2005 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.2148142},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2005/Hennenfent05SEGscd/Hennenfent05SEGscd.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2005/Hennenfent05SEGscd/Hennenfent05SEGscd_pres.pdf},
	author = {Gilles Hennenfent and Felix J. Herrmann}
}
@conference {hennenfent2005CSEGscs,
	title = {Sparseness-constrained seismic deconvolution with curvelets},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2005},
	month = {05},
	publisher = {CSEG},
	organization = {CSEG},
	abstract = {Continuity along reflectors in seismic images is used via Curvelet representation to stabilize the convolution operator inversion. The Curvelet transform is a new multiscale transform that provides sparse representations for images that comprise smooth objects separated by piece-wise smooth discontinuities (e.g. seismic images). Our iterative Curvelet-regularized deconvolution algorithm combines conjugate gradient-based inversion with noise regularization performed using non-linear Curvelet coefficient thresholding. The thresholding operation enhances the sparsity of Curvelet representations. We show on a synthetic example that our algorithm provides improved resolution and continuity along reflectors as well as reduced ringing effect compared to the iterative Wiener-based deconvolution approach.},
	keywords = {Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2005/Hennenfent05CSEGscs/Hennenfent05CSEGscs.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2005/Hennenfent05CSEGscs/Hennenfent05CSEGscs_pres.pdf},
	author = {Gilles Hennenfent and Felix J. Herrmann and R. Neelamani}
}
@conference {herrmann2004CSEGcia1,
	title = {Curvelet imaging and processing: adaptive multiple elimination},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2004},
	month = {05},
	publisher = {CSEG},
	organization = {CSEG},
	abstract = {Predictive multiple suppression methods consist of two main steps: a prediction step, in which multiples are predicted from the seismic data, and a subtraction step, in which the predicted multiples are matched with the true multiples in the data. The last step appears crucial in practice: an incorrect adaptive subtraction method will cause multiples to be sub-optimally subtracted or primaries being distorted, or both. Therefore, we propose a new domain for separation of primaries and multiples via the Curvelet transform. This transform maps the data into almost orthogonal localized events with a directional and spatial-temporal component. The multiples are suppressed by thresholding the input data at those Curvelet components where the predicted multiples have large amplitudes. In this way the more traditional filtering of predicted multiples to fit the input data is avoided. An initial field data example shows a considerable improvement in multiple suppression.},
	keywords = {Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2004/Herrmann04CSEGcia1/Herrmann04CSEGcia1_paper.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2004/Herrmann04CSEGcia1/Herrmann04CSEGcia1.pdf},
	author = {Felix J. Herrmann and D. J. Verschuur}
}
@conference {herrmann2004CSEGcia,
	title = {Curvelet imaging and processing: an overview},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2004},
	month = {05},
	publisher = {CSEG},
	organization = {CSEG},
	abstract = {In this paper an overview is given on the application of directional basis functions, known under the name Curvelets/Contourlets, to various aspects of seismic processing and imaging. Key conceps in the approach are the use of (i) directional basis functions that localize in both domains (e.g. space and angle); (ii) non-linear estimation, which corresponds to localized muting on the coefficients, possibly supplemented by constrained optimization (iii) invariance of the basis functions under the imaging operators. We will discuss applications that include multiple and ground roll removal; sparseness-constrained least-squares migration and the computation of 4-D difference cubes.},
	keywords = {Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2004/Herrmann04CSEGcia/Herrmann04CSEGcia_paper.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2004/Herrmann04CSEGcia/Herrmann04CSEGcia.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2004CSEGcia2,
	title = {Curvelet imaging and processing: sparseness-constrained least-squares migration},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2004},
	month = {05},
	publisher = {CSEG},
	organization = {CSEG},
	abstract = {A non-linear edge-preserving solution to the least-squares migration problem with sparseness constraints is introduced. The applied formalism explores Curvelets as basis functions that, by virtue of their sparseness and locality, not only allow for a reduction of the dimensionality of the imaging problem but which also naturally lead to a non-linear solution with significantly improved signal-to-noise ratio. Additional conditions on the image are imposed by solving a constrained optimization problem on the estimated Curvelet coefficients initialized by thresholding. This optimization is designed to also restore the amplitudes by (approximately) inverting the normal operator, which is, like-wise to the (de)-migration operators, almost diagonalized by the Curvelet transform.},
	keywords = {Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2004/Herrmann04CSEGcia2/Herrmann04CSEGcia2_paper.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2004/Herrmann04CSEGcia2/Herrmann04CSEGcia2.pdf},
	author = {Felix J. Herrmann and Peyman P. Moghaddam}
}
@conference {yarham2004CSEGcpa,
	title = {Curvelet processing and imaging: adaptive ground roll removal},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2004},
	month = {05},
	publisher = {CSEG},
	organization = {CSEG},
	abstract = {In this paper we present examples of ground roll attenuation for synthetic and real data gathers by using Contourlet and Curvelet transforms. These non-separable wavelet transforms are locoalized both (x,t)- and (k,f)-domains and allow for adaptive seperation of signal and ground roll. Both linear and non-linear filtering are discussed using the unique properties of these basis that allow for simultaneous localization in the both domains. Eventhough, the linear filtering techniques are encouraging the true added value of these basis-function techniques becomes apparent when we use these decompositions to adaptively substract modeled ground roll from data using a non-linear thesholding procedure. We show real and synthetic examples and the results suggest that these directional-selective basis functions provide a usefull tool for the removal of coherent noise such as ground roll.},
	keywords = {CSEG, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2004/Yarham04CSEGcpa/yarham04csegcpa.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2004/Yarham04CSEGcpa/Yarham04CSEGcpa_pres.pdf},
	author = {Carson Yarham and Daniel Trad and Felix J. Herrmann}
}
@conference {herrmann2004SEGcbn,
	title = {Curvelet-based non-linear adaptive subtraction with sparseness constraints},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {23},
	year = {2004},
	pages = {1977-1980},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this paper an overview is given on the application of directional basis functions, known under the name Curvelets/Contourlets, to various aspects of seismic processing and imaging, which involve adaptive subtraction. Key concepts in the approach are the use of directional basis functions that localize in both domains (e.g. space and angle); non-linear estimation, which corresponds to localized muting on the coefficients, possibly supplemented by constrained optimization. We will discuss applications that include multiple, ground-roll removal and migration denoising. \copyright2004 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.1851181},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2004/Herrmann04SEGcbn/Herrmann04SEGcbn.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2004/Herrmann04SEGcbn/Herrmann04SEGcbn_pres.pdf},
	author = {Felix J. Herrmann and Peyman P. Moghaddam}
}
@conference {herrmann2004EAGEcdl,
	title = {Curvelet-domain least-squares migration with sparseness constraints},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2004},
	month = {06},
	abstract = {A non-linear edge-preserving solution to the least-squares migration problem with sparseness constraints is introduced. The applied formalism explores Curvelets as basis functions that, by virtue of their sparseness and locality, not only allow for a reduction of the dimensionality of the imaging problem but which also naturally lead to a non-linear solution with significantly improved signal-to-noise ratio. Additional conditions on the image are imposed by solving a constrained optimization problem on the estimated Curvelet coefficients initialized by thresholding. This optimization is designed to also restore the amplitudes by (approximately) inverting the normal operator, which is like-wise the (de)-migration operators, almost diagonalized by the Curvelet transform.},
	keywords = {EAGE, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2004/Herrmann04EAGEcdl/Herrmann04EAGEcdl.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2004/Herrmann04EAGEcdl/Herrmann04EAGEcdl_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=2073},
	author = {Felix J. Herrmann and Peyman P. Moghaddam}
}
@conference {herrmann2004SEGcdm,
	title = {Curvelet-domain multiple elimination with sparseness constraints},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {23},
	year = {2004},
	pages = {1333-1336},
	publisher = {SEG},
	organization = {SEG},
	abstract = {Predictive multiple suppression methods consist of two main steps: a prediction step, in which multiples are predicted from the seismic data, and a subtraction step, in which the predicted multiples are matched with the true multiples in the data. The last step appears crucial in practice: an incorrect adaptive subtraction method will cause multiples to be sub-optimally subtracted or primaries being distorted, or both. Therefore, we propose a new domain for separation of primaries and multiples via the Curvelet transform. This transform maps the data into almost orthogonal localized events with a directional and spatial-temporal component. The multiples are suppressed by thresholding the input data at those Curvelet components where the predicted multiples have large amplitudes. In this way the more traditional filtering of predicted multiples to fit the input data is avoided. An initial field data example shows a considerable improvement in multiple suppression. \copyright2004 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.1851110},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2004/Herrmann04SEGcdm/Herrmann04SEGcdm.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2004/Herrmann04SEGcdm/Herrmann04SEGcdm_pres.pdf},
	author = {Felix J. Herrmann and D. J. Verschuur}
}
@conference {yarham2004CSEGgrr,
	title = {Ground roll removal using non-separable wavelet transforms},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2004},
	keywords = {Presentation},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2004/Yarham04CSEGgrr/Yarham04CSEGgrr.pdf},
	author = {Carson Yarham and Felix J. Herrmann and Daniel Trad}
}
@conference {moghaddam2004SEGmpw,
	title = {Migration preconditioning with curvelets},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {23},
	year = {2004},
	pages = {2204-2207},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this paper, the property of Curvelet transforms for preconditioning the migration and normal operators is investigated. These operators belong to the class of Fourier integral operators and pseudo-differential operator, respectively. The effect of this pre-conditioner is shown in term of improvement of sparsity, convergence rate, number of iteration for the Krylov-subspace solver and clustering of singular(eigen) values. The migration operator, which we employed in this work is the common-offset Kirchoff-Born migration. \copyright2004 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.1845213},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2004/Moghaddam04SEGmpw/Moghaddam04SEGmpw_paper.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2004/Moghaddam04SEGmpw/Moghaddam04SEGmpw_pres.pdf},
	author = {Peyman P. Moghaddam and Felix J. Herrmann}
}
@conference {hennenfent2004SEGtta,
	title = {Three-term amplitude-versus-offset (AVO) inversion revisited by curvelet and wavelet transforms},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2004},
	pages = {211-214},
	publisher = {SEG},
	organization = {SEG},
	abstract = {We present a new method to stabilize the three-term AVO inversion using Curvelet and Wavelet transforms. Curvelets are basis functions that effectively represent otherwise smooth objects having discontinuities along smooth curves. The applied formalism explores them to make the most of the continuity along reflectors in seismic images. Combined with Wavelets, Curvelets are used to denoise the data by penalizing high frequencies and small contributions in the AVO-cube. This approach is based on the idea that rapid amplitude changes along the ray-parameter axis are most likely due to noise. The AVO-inverse problem is linearized, formulated and solved for all (x, z) at once. Using densities and velocities of the Marmousi model to define the fluctuations in the elastic properties, the performance of the proposed method is studied and compared with the smoothing along the ray-parameter direction only. We show that our method better approximates the true data after the denoising step, especially when noise level increases. \copyright2004 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.1851201},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2004/Hennenfent04SEGtta/Hennenfent04SEGtta.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2004/Hennenfent04SEGtta/Hennenfent04SEGtta_pres.pdf},
	author = {Gilles Hennenfent and Felix J. Herrmann}
}
@conference {herrmann2003SPIEmsa,
	title = {Multifractional splines: application to seismic imaging},
	booktitle = {Proceedings of SPIE Technical Conference on Wavelets: Applications in Signal and Image Processing X},
	volume = {5207},
	year = {2003},
	pages = {240-258},
	publisher = {SPIE},
	organization = {SPIE},
	abstract = {Seismic imaging commits itself to locating singularities in the elastic properties of the Earth{\textquoteright}s subsurface. Using the high-frequency ray-Born approximation for scattering from non-intersecting smooth interfaces, seismic data can be represented by a generalized Radon transform mapping the singularities in the medium to seismic data. Even though seismic data are bandwidth limited, signatures of the singularities in the medium carry through this transform and its inverse and this mapping property presents us with the possibility to develop new imaging techniques that preserve and characterize the singularities from incomplete, bandwidth-limited and noisy data. In this paper we propose a non-adaptive Curvelet/Contourlet technique to image and preserve the singularities and a data-adaptive Matching Pursuit method to characterize these imaged singularities by Multi-fractional Splines. This first technique borrows from the ideas within the Wavelet-Vaguelette/Quasi-SVD approach. We use the almost diagonalization of the scattering operator to approximately compensate for (i) the coloring of the noise and hence facilitate estimation; (ii) the normal operator itself. Results of applying these techniques to seismic imaging are encouraging although many open fundamental questions remain.},
	keywords = {Presentation, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SPIE/2003/herrmann2003SPIEmsa/herrmann2003SPIEmsa.pdf},
	author = {Felix J. Herrmann},
	editor = {Michael A. Unser and Akram Aldroubi and Andrew F. Laine}
}
@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}
}
