@conference {tu2015PIMSsls,
	title = {Sparse least-squares seismic imaging with source estimation utilizing multiples},
	booktitle = {PIMS Workshop on Advances in Seismic Imaging and Inversion},
	year = {2015},
	note = {(PIMS Workshop on Advances in Seismic Imaging and Inversion, University of Alberta, Edmonton)},
	month = {05},
	abstract = {We present an least-squares seismic imaging method that (1) is efficient by subsampling the monochromatic source experiments; (2) does not require the prior knowledge of the source wavelet, by inverting for the seismic image and the source wavelet simultaneously using variable projection; and (3) makes active use of surface-related multiples to increase the illumination and to resolve the scaling ambiguity in source estimation, by tightly integrating the well-established surface-related-multiple-prediction into the wave-equation based modelling. As a result, we are able to obtain ambiguity-resolved, high-fidelity least-squares inverted seismic images with computational costs that are comparable to conventional reverse-time migrations with all the data. We demonstrate the efficacy of the proposed method using synthetic examples.},
	keywords = {inversion, least-squares migration, multiples, PIMS, seismic imaging, source estimation, workshop},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/PIMS/2015/tu2015PIMSsls/tu2015PIMSsls_pres.pdf},
	author = {Ning Tu and Felix J. Herrmann}
}
@conference {tu2013EAGElsm,
	title = {Fast least-squares migration with multiples and source estimation},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2013},
	month = {06},
	abstract = {The advent of modern computing has made it possible to do seismic imaging using least-squares reverse-time migration. We obtain superior images by solving an optimization problem that recovers the true-amplitude images. However, its success hinges on overcoming several issues, including overwhelming problem size, unknown source wavelet, and interfering coherent events like multiples. In this abstract, we reduce the problem size by using ideas from compressive sensing, and estimate source wavelet by generalized variable projection. We also demonstrate how to invert for subsurface information encoded in surface-related multiples by incorporating the free-surface operator as an areal source in reverse-time migration. Our synthetic examples show that multiples help to improve the resolution of the image, as well as remove the amplitude ambiguity in wavelet estimation.},
	keywords = {EAGE, Imaging, multiples, source estimation, sparse},
	doi = {10.3997/2214-4609.20130727},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2013/tu2013EAGElsm/tu2013EAGElsm.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2013/tu2013EAGElsm/tu2013EAGElsm_pres.pdf},
	author = {Ning Tu and Aleksandr Y. Aravkin and Tristan van Leeuwen and Felix J. Herrmann}
}
@conference {herrmann2013EAGEfrtm,
	title = {Fast RTM with multiples and source estimation},
	booktitle = {EAGE/SEG Forum - Turning noise into geological information: The next big step?},
	year = {2013},
	month = {11},
	abstract = {During this talk, we present a computationally efficient (cost of 1-2 RTM{\textquoteright}s with all data) iterative sparsity-promoting inversion framework where surface-related multiples are jointly imaged with primaries and where the source signature is estimated on the fly. Our imaging algorithm is computationally efficient because it works during each iteration with small independent randomized subsets of data. The multiples are handled by introducing an areal source term that includes the upgoing wavefield. We update the source signature for each iteration using a variable projection method. The resulting algorithm removes imaging artifacts from surface-related multiples, estimates and removes the imprint of the source, recovers true amplitudes, is fast, and robust to linearization errors by virtue of the statistical independence of the subsets of data we are working with at each iteration.},
	keywords = {EAGE, multiples, RTM, SEG, source estimation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2013/herrmann2013EAGEfrtm/herrmann2013EAGEfrtm_pres.pdf},
	author = {Felix J. Herrmann and Ning Tu}
}
