@article {yang2020tdsp,
	title = {Time-domain sparsity promoting least-squares reverse time migration with source estimation},
	journal = {Geophysical Prospecting},
	volume = {68},
	number = {9},
	year = {2020},
	note = {(Geophysical Prospecting)},
	month = {08},
	pages = {2697-2711},
	abstract = {Least-squares reverse time migration is well-known for its capability to generate artifact-free true-amplitude subsurface images through fitting observed data in the least-squares sense. However, when applied to realistic imaging problems, this approach is faced with issues related to overfitting and excessive computational costs induced by many wave-equation solves. The fact that the source function is unknown complicates this situation even further. Motivated by recent results in stochastic optimization and transform-domain sparsity-promotion, we demonstrate that the computational costs of inversion can be reduced significantly while avoiding imaging artifacts and restoring amplitudes. While powerfull, these new approaches do require accurate information on the source-time function, which is often lacking. Without this information, the imaging quality deteriorates rapidly. We address this issue by presenting an approach where the source-time function is estimated on the fly through a technique known as variable projection. Aside from introducing negligible computational overhead, the proposed method is shown to perform well on imaging problems with noisy data and problems that involve complex settings such as salt. In either case, the presented method produces high resolution high-amplitude fidelity images including an estimates for the source-time function. In addition, due to its use of stochastic optimization, we arrive at these images at roughly one to two times the cost of conventional reverse time migration involving all data.},
	keywords = {penalty, source estimation, sparsity inversion},
	doi = {10.1111/1365-2478.13021},
	url = {https://slim.gatech.edu/Publications/Public/Journals/GeophysicalProspecting/2020/yang2020tdsp/yang2020tdsp.html},
	author = {Mengmeng Yang and Zhilong Fang and Philipp A. Witte and Felix J. Herrmann}
}
@techreport {witte2017TRcls,
	title = {Compressive least-squares migration with source estimation},
	number = {TR-EOAS-2017-3},
	year = {2017},
	institution = {UBC},
	abstract = {Least-squares reverse-time migration is a powerful approach for true-amplitude seismic imaging of complex geological structures. The successful application of this method is hindered by its exceedingly large computational cost and required prior knowledge of the generally unknown source wavelet. We address these problems by introducing an algorithm for low-cost sparsity-promoting least-squares migration with source estimation. We adapt a recent algorithm from sparse optimization, which allows to work with randomized subsets of shots during each iteration of least-squares migration, while still converging to an artifact-free solution. We modify the algorithm to incorporate on-the-fly source estimation through variable projection, which lets us estimate the wavelet without additional PDE solves. The algorithm is easy to implement and allows imaging at a fraction of the cost of conventional least squares reverse-time migration, requiring only around two passes trough the data, making the method feasible for large-scale imaging problems with unknown source wavelets.},
	keywords = {LSRTM, migration, source estimation, sparsity, time domain},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2017/witte2017TRcls/witte2017TRcls.html},
	author = {Philipp A. Witte and Mengmeng Yang and Felix J. Herrmann}
}
@conference {yang2016SEGtds,
	title = {Time-domain sparsity-promoting least-squares migration with source estimation},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2016},
	note = {(SEG, Dallas)},
	month = {10},
	pages = {4225-4229},
	abstract = {Traditional reverse-time migration (RTM) gives images with wrong amplitudes and low resolution. Least-squares RTM (LS-RTM) on the other hand, is capable of obtaining true-amplitude images as solutions of $\ell_2$-norm norm minimization problems by fitting the synthetic and observed reflection data. The shortcoming of this approach is that solutions of these $\ell_2$ problems are typically smoothed, tend to be overfitted, and computationally too expensive because it requires compared to standard RTM too many iterations. By working with randomized subsets of data only, the computational costs of LS-RTM can be brought down to an acceptable level while producing artifact-free high-resolution images without overfitting the data. While initial results of these "compressive imaging" methods were encouraging various open issues remain including guaranteed convergence, algorithmic complexity of the solver, and lack of on-the-fly source estimation for LS-RTMs with wave-equation solvers based on time-stepping. By including on-the-fly source-time function estimation into the method of Linearized Bregman (LB), on which we reported before, we tackle all these issues resulting in a easy-to-implement algorithm that offers flexibility in the trade-off between the number of iterations and the number of wave-equation solves per iteration for a fixed total number of wave-equation solves. Application of our algorithm on a 2D synthetic shows that we are able to obtain high-resolution images, including accurate estimates of the wavelet, for a single pass through the data. The produced image, which is by virtue of the inversion deconvolved with respect to the wavelet, is roughly of the same quality as the image obtained given the correct source function.},
	keywords = {least squares, RTM, SEG, source estimation, time domain},
	doi = {10.1190/segam2016-13850609.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/yang2016SEGtds/yang2016SEGtds.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/yang2016SEGtds/yang2016SEGtds_pres.pdf},
	author = {Mengmeng Yang and Philipp A. Witte and Zhilong Fang and Felix J. Herrmann}
}
