@conference {herrmann2019EAGEHPCaii,
	title = {Accelerating ideation and innovation cheaply in the Cloud the power of abstraction, collaboration and reproducibility},
	booktitle = {4th EAGE Workshop on High-performance Computing},
	year = {2019},
	note = {(EAGE HPC Workshop, Dubai)},
	month = {10},
	keywords = {cloud, devito, EAGE, HPC, JUDI},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGEHPC/2019/herrmann2019EAGEHPCaii/herrmann2019EAGEHPCaii_pres.pdf},
	author = {Felix J. Herrmann and Charles Jones and Gerard Gorman and Jan H{\"u}ckelheim and Keegan Lensink and Paul H. J. Kelly and Navjot Kukreja and Henryk Modzelewski and Michael Lange and Mathias Louboutin and Fabio Luporini and James Selvages and Philipp A. Witte}
}
@conference {alfaraj2018EAGEasa,
	title = {Automatic statics and residual statics correction with low-rank approximation},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2018},
	note = {(EAGE, Copenhagen)},
	month = {06},
	abstract = {Seismic data, mainly on land, suffers from long-wavelength statics due to the laterally varying and heterogeneous nature of the near-surface weathering layers. We propose an automatic, data-driven and computationally efficient statics correction method based on low-rank approximation to correct for such statics. The method does not require a model to estimate static time shifts, which is the case for other static correction methods; rather it applies the appropriate static corrections on the data such that it becomes low rank in a certain domain. As of now, the method is applicable to data that has been corrected for elevation statics. Due to the near-surface irregularities and due to approximations used by static correction methods that lead to not fully correcting for statics, an iterative residual statics correction becomes necessary. Our proposed method corrects for residual statics without the necessity of the surface consistency assumption and a multi-iterate process. Additional benefits of the method include artifacts and noise suppression. We demonstrate the successful application of our method on several synthetic data examples.},
	keywords = {EAGE, land, Rank, residual statics, statics},
	doi = {10.3997/2214-4609.201801107},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2018/alfaraj2018EAGEasa/alfaraj2018EAGEasa.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2018/alfaraj2018EAGEasa/alfaraj2018EAGEasa_pres.pdf},
	author = {Ali M. Alfaraj and Rajiv Kumar and Felix J. Herrmann}
}
@conference {vanleeuwen2015EAGEafs,
	title = {Affordable full subsurface image volume{\textendash}-an application to WEMVA},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2015},
	note = {(EAGE Workshop on Wave Equation based Migration Velocity Analysis, Madrid)},
	month = {06},
	abstract = {Common image gathers are used in building velocity models, inverting for anisotropy parameters, and analyzing reservoir attributes. In this paper, we offer a new perspective on image gathers, where we glean information from the image volume via efficient matrix-vector products. The proposed formulation make the computation of full subsurface image volume feasible. We illustrate how this matrix-vector product can be used to construct objective functions for automatic MVA.},
	keywords = {EAGE, MVA, randomized trace estimation, wave-equation},
	doi = {10.3997/2214-4609.201413498},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/vanleeuwen2015EAGEafs/vanleeuwen2015EAGEafs.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/vanleeuwen2015EAGEafs/vanleeuwen2015EAGEafs_pres.pdf},
	author = {Tristan van Leeuwen and Rajiv Kumar and Felix J. Herrmann}
}
@conference {petrenko2014EAGEaih,
	title = {Accelerating an iterative Helmholtz solver with FPGAs},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2014},
	month = {06},
	abstract = {We implement the Kaczmarz row-projection algorithm (Kaczmarz (1937)) on a CPU host + FPGA accelerator platform using techniques of dataflow programming. This algorithm is then used as the preconditioning step in CGMN, a modified version of the conjugate gradients method (Bj{\"o}rck and Elfving (1979)) that we use to solve the time-harmonic acoustic isotropic constant density wave equation. Using one accelerator we achieve a speed-up of over 2{\texttimes} compared with one Intel core.},
	keywords = {CGMN, EAGE, FPGA, Helmholtz equation, Kaczmarz, reconfigurable computing},
	doi = {10.3997/2214-4609.20141141},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/petrenko2014EAGEaih/petrenko2014EAGEaih.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/petrenko2014EAGEaih/petrenko2014EAGEaih_pres.pdf},
	author = {Art Petrenko and Tristan van Leeuwen and Diego Oriato and Simon Tilbury and Felix J. Herrmann}
}
@conference {esser2014EAGEacp,
	title = {Application of a convex phase retrieval method to blind seismic deconvolution},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2014},
	month = {06},
	abstract = {A classical strategy for blind seismic deconvolution is to first estimate the autocorrelation of the unknown source wavelet from the data and then recover the wavelet by assuming it has minimum phase. However, computing the minimum phase wavelet directly from the amplitude spectrum can be sensitive to even extremely small errors, especially in the coefficients close to zero. Since the minimum phase requirement follows from an assumption that the wavelet should be as impulsive as possible, we propose to directly estimate an impulsive wavelet by minimizing a weighted l2 penalty subject to a constraint on its amplitude spectrum. This nonconvex model has the form of a phase retrieval problem, in this case recovering a signal given only estimates of the magnitudes of its Fourier coefficients. Following recent work on convex relaxations of phase retrieval problems, we propose a convex semidefinite program for computing an impulsive minimum phase wavelet whose amplitude spectrum is close to a given estimate, and we show that this can be robustly solved by a Douglas Rachford splitting method for convex optimization.},
	keywords = {blind deconvolution, convex phase retrieval, EAGE, source wavelet estimation},
	doi = {10.3997/2214-4609.20141590},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/esser2014EAGEacp/esser2014EAGEacp.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/esser2014EAGEacp/esser2014EAGEacp_poster.pdf},
	author = {Ernie Esser and Felix J. Herrmann}
}
@conference {zheglova2014EAGEams,
	title = {Application of matrix square root and its inverse to downward wavefield extrapolation},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2014},
	month = {06},
	abstract = {In this paper we propose a method for computation of the square root of the Helmholtz operator and its inverse that arise in downward extrapolation methods based on one-way wave equation. Our approach involves factorization of the discretized Helmholtz operator at each depth by extracting the matrix square root after performing the spectral projector in order to eliminate the evanescent modes. The computation of the square root of the discrete Helmholtz operator and its inverse is done using polynomial recursions and can be combined with low rank matrix approximations to reduce the computational cost for large problems. The resulting square root operator is able to model the propagating modes kinematically correctly at the angles of up to 90 degree. Preliminary results on convergence of iterations are presented in this abstract. Potential applications include seismic modeling, imaging and inversion.},
	keywords = {EAGE, extrapolation, Modelling, one-way wave equation, square root},
	doi = {10.3997/2214-4609.20141184},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/zheglova2014EAGEams/zheglova2014EAGEams.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/zheglova2014EAGEams/zheglova2014EAGEams_pres.pdf},
	author = {Polina Zheglova and Felix J. Herrmann}
}
