@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 {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 {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 {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 {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 {erlangga2009SEGswi,
	title = {Seismic waveform inversion with Gauss-Newton-Krylov method},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {28},
	year = {2009},
	month = {10},
	pages = {2357-2361},
	publisher = {SEG},
	organization = {SEG},
	abstract = {This abstract discusses an implicit implementation of the Gauss-Newton method, used for the frequency-domain full-waveform inversion, where the inverse of the Hessian for the update is never formed explicitly. Instead, the inverse of the Hessian is computed approximately by a conjugate gradient (CG) method, which only requires the action of the Hessian on the CG search direction. This procedure avoids an excessive computer storage, usually needed for storing the Hessian, at the expense of extra computational work in CG. An effective preconditioner for the Hessian is important to improve the convergence of CG, and hence to reduce the overall computational work.},
	keywords = {SEG},
	doi = {10.1190/1.3255332},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/erlangga09SEGswi/erlangga09SEGswi.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/erlangga09SEGswi/erlangga09SEGswi_pres.pdf},
	author = {Yogi A. Erlangga and 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}
}
