@conference {zhang2019SEGhfw,
	title = {High-frequency wavefield recovery with weighted matrix factorizations},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2019},
	note = {(SEG, San Antonio)},
	month = {09},
	pages = {3959-3963},
	abstract = {Acquired seismic data is normally not the fully sampled data we would like to work with since traces are missing due to physical constraints or budget limitations. Rank minimization is an effective way to recovering the missing trace data. Unfortunately, this technique{\textquoteright}s performance may deteriorate at higher frequency because high-frequency data can not necessarily be captured accurately by low-rank matrix factorizations albeit remedies exist such as hierarchical semi-separable matrices. As a result, recovered data often suffers from low signal to noise ratio S/Rs at the higher frequencies. To deal with this situation, we propose a weighted recovery method that improves the performance at the high frequencies by recursively using information from matrix factorizations at neighboring lower frequencies. Essentially, we include prior information from previously reconstructed frequency slices during the wavefield reconstruction. We apply our method to data collected from the Gulf of Suez, which shows that our method performs well compared to the traditional method without weighting.},
	keywords = {algorithm, data reconstruction, frequency-domain, interpolation, Processing, SEG},
	doi = {10.1190/segam2019-3215103.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/zhang2019SEGhfw/zhang2019SEGhfw.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/zhang2019SEGhfw/zhang2019SEGhfw_pres.pdf},
	author = {Yijun Zhang and Shashin Sharan and Felix J. Herrmann}
}
@conference {sharan2017SEGhrf,
	title = {High resolution fast microseismic source collocation and source time function estimation},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2017},
	note = {(SEG, Houston)},
	month = {09},
	pages = {2778-2783},
	abstract = {Sparsity promotion based joint microseismic source collocation and source time function estimation, using Linearized Bregman algorithm, although simple to implement, suffers from slow convergence. This is due to the fact that Linearized Bregman algorithm has only first order of convergence. In this work, we propose to accelerate the existing Linearized Bregman algorithm using the L-BFGS algorithm. Without any initial guess for the source location or source time function, our method is able to estimate the source location and source time function for kinematically correct smooth velocity model. We demonstrate the effectiveness of our method for multiple sources spaced within half a wavelength. We also compare our results with Linearized Bregman based method in {\textquoteleft}{\textquoteleft}2.5{\textquoteleft}{\textquoteleft}D instead of {\textquoteleft}{\textquoteleft}2{\textquoteleft}{\textquoteleft}D.},
	keywords = {algorithm, Full-waveform inversion, microseismic, passive imaging, SEG, wave equation},
	doi = {10.1190/segam2017-17787749.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/sharan2017SEGhrf/sharan2017SEGhrf.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/sharan2017SEGhrf/sharan2017SEGhrf_pres.pdf},
	author = {Shashin Sharan and Rongrong Wang 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 {lebed2008SEGhggt,
	title = {A hitchhiker{\textquoteright}s guide to the galaxy of transform-domain sparsification},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2008},
	publisher = {SEG},
	organization = {SEG},
	abstract = {The ability to efficiently and sparsely represent seismic data is becoming an increasingly important problem in geophysics. Over the last decade many transforms such as wavelets, curvelets, contourlets, surfacelets, shearlets, and many other types of {\textquoteright}x-lets{\textquoteright} have been developed to try to resolve this issue. In this abstract we compare the properties of four of these commonly used transforms, namely the shift-invariant wavelets, complex wavelets, curvelets and surfacelets. We also briefly explore the performance of these transforms for the problem of recovering seismic wavefields from incomplete measurements.},
	keywords = {SEG},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/lebed08SEGhggt/lebed08SEGhggt.pdf},
	author = {Evgeniy Lebed and Felix J. Herrmann}
}
