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@phdthesis{oghenekohwo2017economic,
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@inproceedings{wei2018improve,
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  volume={76},
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@article{oghenekohwo2017low,
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@article{kumar2015efficient,
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@incollection{zhang2019high,
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  pages={3959--3963},
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@phdthesis{sharan2020large,
  title={LARGE SCALE HIGH-FREQUENCY SEISMIC WAVEFIELD RECONSTRUCTION, ACQUISITION VIA RANK MINIMIZATION AND SPARSITY-PROMOTING SOURCE ESTIMATION},
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  year={2020},
  school={Georgia Institute of Technology}
}

@article{lopez2022near,
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}

@inproceedings{zhang2020wavefield,
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  year={2020},
  organization={OnePetro}
}

@inproceedings{bhojanapalli2014universal,
  title={Universal matrix completion},
  author={Bhojanapalli, Srinadh and Jain, Prateek},
  booktitle={International Conference on Machine Learning},
  pages={1881--1889},
  year={2014},
  organization={PMLR}
}

@article{zhang2022simulation,
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  booktitle={SEG Technical Program Expanded Abstracts 2022},
  year={2022},
  publisher={Society of Exploration Geophysicists}
}

@article{lopez2022graph,
  title={Graph spectrum based seismic survey design},
  author={L{\'o}pez, Oscar and Kumar, Rajiv and Moldoveanu, Nick and Herrmann, Felix},
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  year={2022}
}

@article{wason2017low,
  title={Low-cost time-lapse seismic with distributed compressive sensing—Part 2: Impact on repeatability},
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  volume={82},
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}

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  journal={The Leading Edge},
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  number={8},
  pages={677--687},
  year={2017},
  publisher={Society of Exploration Geophysicists}
}

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  volume={36},
  number={8},
  pages={688--694},
  year={2017},
  publisher={Society of Exploration Geophysicists}
}

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  volume={36},
  number={5},
  pages={S237--S266},
  year={2014},
  publisher={SIAM}
}

@article{kumar2015efficient,
  title={Efficient matrix completion for seismic data reconstruction},
  author={Kumar, Rajiv and Da Silva, Curt and Akalin, Okan and Aravkin, Aleksandr Y and Mansour, Hassan and Recht, Benjamin and Herrmann, Felix J},
  journal={Geophysics},
  volume={80},
  number={5},
  pages={V97--V114},
  year={2015},
  publisher={Society of Exploration Geophysicists}
}

@article {tang2008CITEocs,
	title = {Optimized Compressed Sensing for Curvelet-based Seismic Data Reconstruction},
	journal = {CiteSeer},
	year = {2008},
	abstract = {Compressed sensing (CS) or compressive sampling provides a new sampling theory to reduce data acquisition, which says that compressible signals can be exactly reconstructed from highly incomplete sets of measurements. Very recently, the CS has been applied for seismic exploration and started to compact the traditional data acquisition. In this paper, we present an optimized sampling strategy for the CS data acquisition, which leads to better performance by the curvelet sparsity-promoting inversion in comparison with random sampling and jittered sampling scheme. One of motivation is to reduce the mutual coherence between measurement sampling schemes and curvelet sparse transform in the CS framework. The basic idea of our optimization is to directly redistribute the energy in frequency domain making original spectrum easily discriminated from the random noise induced by random undersampling, while offering control on the maximum gap size. Numerical experiments on synthetic and real seismic data show good performances of the proposed optimized CS for seismic data reconstruction.},
	keywords = {curvelet transform, Optimized Compressive Sensing},
	author = {Wen Tang and Jianwei Ma and Felix J. Herrmann}
}
@article{hennenfent2008simply,
  title={Simply denoise: Wavefield reconstruction via jittered undersampling},
  author={Hennenfent, Gilles and Herrmann, Felix J},
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  volume={73},
  number={3},
  pages={V19--V28},
  year={2008},
  publisher={Society of Exploration Geophysicists}
}
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  year={2010},
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}

@article{lopez2016off,
  title={Off-the-grid low-rank matrix recovery and seismic data reconstruction},
  author={L{\'o}pez, Oscar and Kumar, Rajiv and Y{\i}lmaz, {\"O}zg{\"u}r and Herrmann, Felix J},
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  volume={10},
  number={4},
  pages={658--671},
  year={2016},
  publisher={IEEE}
}