@presentation {zhang2022ML4SEISMICtss,
	title = {Time-lapse seismic survey design by maximizing the spectral gap},
	year = {2022},
	month = {11},
	abstract = {While time-lapse seismic has been applied successfully to CO2 sequestration monitoring, it remains a challenging problem since replicated dense surveys come at a very high cost in the field. Wavefield reconstruction based on matrix completion (MC) from randomized subsampled data is an efficient way to reduce operational costs. This technique allows for accurate time-lapse reconstruction by employing the joint recovery model (JRM), which capitalizes on the fact that different vintages share a common component. However, combining JRM with optimal time-lapse acquisition survey design remains an unexplored area of research. In expander graph theory, spectral gap (SG) reveals the source-receiver layout connectivity and is related to reconstruction quality during MC. Building on these insights, we proposed a simulation free time-lapse survey design based on JRM that aims to get similar reconstructed quality without insisting on replicate surveys, which significantly reduces the cost in the field. This approach uses the simulated annealing algorithm to find subsampling masks for each vintage. Numerical experiments confirm a direct correlation between increased spectral gap and promising time-lapse reconstruction quality.},
	keywords = {Acquisition, JRM, matrix factorization, ML4SEISMIC, SLIM, spectral gap, survey design, time-lapse, wavefield reconstruction},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/zhang2022ML4SEISMICtss/index.html},
	author = {Yijun Zhang and Mathias Louboutin and Ali Siahkoohi and Ziyi Yin and Rajiv Kumar and Oscar Lopez and Felix J. Herrmann}
}
@article {kumar2018toi,
	title = {Target-oriented imaging using extended image volumes{\textemdash}a low-rank factorization approach},
	journal = {Geophysical Prospecting},
	volume = {67},
	number = {5},
	year = {2019},
	note = {(Geophysical Prospecting)},
	pages = {1312-1328},
	abstract = {Imaging in geological challenging environments has led to new developments, including the idea of generating reflection responses by means of interferometric redatuming at a given target datum in the subsurface, when the target datum lies beneath a complex overburden. One way to perform this redatuming is via conventional model-based wave-equation techniques. But those techniques can be computationally expensive for large-scale seismic problems since the number of wave-equation solves is equal to two-times the number of sources involved during seismic data acquisition. Also conventional shot-profile techniques require lots of memory to save full subsurface extended image volumes. Therefore, they only form subsurface image volumes in either horizontal or vertical directions. We now present a randomized singular value decomposition based approach built upon the matrix probing scheme, which takes advantage of the algebraic structure of the extended imaging system. This low-rank representation enables us to overcome both the computational cost associated with the number of wave-equation solutions and memory usage due to explicit storage of full subsurface extended image volumes employed by conventional migration methods. Experimental results on complex geological models demonstrate the efficacy of the proposed methodology and allow practical reflection-based extended imaging for large-scale 5D seismic data.},
	keywords = {extended image volumes, randomized linear algebra, target-oriented imaging},
	doi = {10.1111/1365-2478.12779},
	url = {https://slim.gatech.edu/Publications/Public/Journals/GeophysicalProspecting/2019/kumar2018toi/kumar2018toi.html},
	author = {Rajiv Kumar and Marie Graff-Kray and Ivan Vasconcelos and Felix J. Herrmann}
}
@conference {kumar2016SEGtjm,
	title = {Time-jittered marine acquisition{\textendash}-a rank-minimization approach for 5D source separation},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2016},
	note = {(SEG, Dallas)},
	month = {10},
	pages = {119-123},
	abstract = {Simultaneous source marine acquisition has been recognized as an economic way of improving spatial sampling and speedup acquisition time, where a single- (or multiple-) source vessel fires at jittered source locations and time instances. Consequently, the acquired simultaneous data volume is processed to separate the overlapping shot records resulting in densely sampled data volume. It has been shown in the past that the simultaneous source acquisition design and source separation process can be setup as a compressed sensing problem, where conventional seismic data is reconstructed from simultaneous data via a sparsity-promoting optimization formulation. While the recovery quality of separated data is reasonably well, the recovery process can be computationally expensive due to transform-domain redundancy. In this paper, we present a computationally tractable rank-minimization algorithm to separate simultaneous data volumes. The proposed algorithm is suitable for large-scale seismic data, since it avoids singular-value decompositions and uses a low-rank based factorized formulation instead. Results are illustrated for simulations of simultaneous time-jittered continuous recording for a 3D ocean-bottom cable survey.},
	keywords = {5D, marine, SEG, source separation, time-jittered acquisition},
	doi = {10.1190/segam2016-13878249.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/kumar2016SEGtjm/kumar2016SEGtjm.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/kumar2016SEGtjm/kumar2016SEGtjm_pres.pdf},
	author = {Rajiv Kumar and Shashin Sharan and Haneet Wason and Felix J. Herrmann}
}
@techreport {kumar2015EAGEtjm,
	title = {Time-jittered marine acquisition: low-rank v/s sparsity},
	number = {TR-EOAS-2015-2},
	year = {2015},
	month = {01},
	institution = {UBC},
	abstract = {Time-jittered simultaneous marine acquisition has been recognized as an economic way of improving the spatial sampling, and speedup acquisition, where a single (or multiple) source vessel fires at {\textendash} jittered source locations and instances in time. It has been shown in the past that this problem can be setup as a {\textendash} compressed sensing problem, where conventional seismic data is reconstructed from blended data via a sparsity-promoting optimization formulation. While the recovery quality of deblended data is very good, the recovery process is computationally very expensive. In this paper, we present a computationally efficient rank-minimization algorithm to deblend the seismic data. The proposed algorithm is suitable for large-scale seismic data, since it avoids SVD computations and uses a low-rank factorized formulation instead. Results are illustrated with simulations of time-jittered marine acquisition, which translates to jittered source locations for a given speed of the source vessel, for a single source vessel with two airgun arrays.},
	keywords = {deblending, low-rank},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2015/kumar2015EAGEtjm/kumar2015EAGEtjm.html},
	author = {Rajiv Kumar and Haneet Wason and Felix J. Herrmann}
}
@conference {oghenekohwo2015IIPFWItlf,
	title = {Time-lapse FWI with distributed compressed sensing},
	booktitle = {Inaugural Full-Waveform Inversion Workshop},
	year = {2015},
	note = {(Natal, Brazil)},
	month = {08-09},
	abstract = {In this talk, I will discuss our most recent application of full-waveform inversion (FWI) to time-lapse seismic data inversion. Specifically, I will illustrate how a joint recovery model (JRM) can be used in a joint inversion framework, leveraging ideas from distributed compressed sensing (DCS). The key idea involves exploiting the common information in the baseline and monitor gradients during the inversion.},
	keywords = {distributed compressed sensing, FWI, time lapse},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/IIPFWI/oghenekohwo2015IIPFWItlf/oghenekohwo2015IIPFWItlf_pres.pdf},
	author = {Felix Oghenekohwo and Rajiv Kumar 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}
}
