@article {fang2017sewri,
	title = {Source estimation for wavefield-reconstruction inversion},
	journal = {Geophysics},
	volume = {83},
	number = {4},
	year = {2018},
	note = {(Geophysics)},
	pages = {R345-R359},
	abstract = {Source estimation is essential to all the wave-equation-based seismic inversions, including full-waveform inversion and the recently proposed wavefield-reconstruction inversion. When the source estimation is inaccurate, errors will propagate into the predicted data and introduce additional data misfit. As a consequence, inversion results that minimize this data misfit may become erroneous. To mitigate the errors introduced by the incorrect and pre-estimated sources, an embedded procedure that updates sources along with medium parameters is necessary for the inversion. So far, such a procedure is still missing in the context of wavefield-reconstruction inversion, a method that is, in many situations, less prone to local minima related to the so-called cycle skipping, compared to full-waveform inversion through exact data-fitting. While wavefield-reconstruction inversion indeed helps to mitigate issues related to cycle skipping by extending the search space with wavefields as auxiliary variables, it relies on having access to the correct source functions. In this paper, we remove the requirement of having the accurate source functions by proposing a source estimation technique specifically designed for wavefield-reconstruction inversion. To achieve this task, we consider the source functions as unknown variables and arrive at an objective function that depends on the medium parameters, wavefields, and source functions. During each iteration, we apply the so-called variable projection method to simultaneously project out the source functions and wavefields. After the projection, we obtain a reduced objective function that only depends on the medium parameters and invert for the unknown medium parameters by minimizing this reduced objective. Numerical experiments illustrate that this approach can produce accurate estimates of the unknown medium parameters without any prior information of the source functions.},
	keywords = {estimation, FWI, source, variable projection, WRI},
	doi = {10.1190/geo2017-0700.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2018/fang2017sewri/fang2017sewri.html},
	author = {Zhilong Fang and Rongrong Wang and Felix J. Herrmann}
}
@conference {sharan2018IEEEIUSspoa,
	title = {Sparsity-promoting photoacoustic imaging with source estimation},
	booktitle = {2018 IEEE International Ultrasonics Symposium (IUS)},
	year = {2018},
	note = {(IEEE IUS, Kobe)},
	month = {10},
	pages = {206-212},
	abstract = {Photoacoustics has emerged as a high-contrast imaging modality that provides optical absorption maps inside of tissues, therefore complementing morphological information of conventional ultrasound. The laser-generated photoacoustic waves are usually envelope-detected, thus disregarding the specific waveforms generated by each photoabsorber. Here we propose a sparsity-promoting image reconstruction method that allows the estimation of each photoabsorber{\textquoteright}s source-time function. Preliminary studies showed the ability to reconstruct the optical absorption map of an in silico vessel phantom. By using a sparsity-promoting imaging method, absorption maps and source-time functions can still be recovered even in situations where the number of transducers is decreased by a factor of six. Moreover, the recovery is able to attain higher resolution than conventional beamforming methods. Because our method recovers the source-time function of the absorbers, it could potentially also be used to distinguish different types of photoabsorbers, or the degree of aggregation of exogenous agents, under the assumption that these would generate different source-time functions at the moment of laser irradiation.},
	keywords = {Acceleration, estimation, IEEE, Image reconstruction, Phantoms, photoacoustic imaging, source-time function, sparsity promoting inversion, Transducers, wave equation},
	doi = {10.1109/ULTSYM.2018.8580037},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/IEEEIUS/2018/sharan2018IEEEIUSspoa/sharan2018IEEEIUSspoa.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/IEEEIUS/2018/sharan2018IEEEIUSspoa/sharan2018IEEEIUSspoa_pres.pdf},
	author = {Shashin Sharan and Rajiv Kumar and Diego S. Dumani and Mathias Louboutin and Rongrong Wang and Stanislav Emelianov and Felix J. Herrmann}
}
@conference {sharan2016SEGspj,
	title = {Sparsity-promoting joint microseismic source collocation and source-time function estimation},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2016},
	note = {(SEG, Dallas)},
	month = {10},
	pages = {2574-2579},
	abstract = {In this work, we propose a new method to simultaneously locate microseismic events (e.g., induced by hydraulic fracturing) and estimate the source signature of these events. We use the method of linearized Bregman. This algorithm focuses unknown sources at their true locations by promoting sparsity along space and at the same time keeping the energy along time in check. We are particularly interested in situations where the microseismic data is noisy, sources have different signatures and we only have access to the smooth background-velocity model. We perform numerical experiments to demonstrate the usability of the proposed method. We also compare our results with full-waveform inversion based microseismic event collocation methods. Our method gives flexibility to simultaneously get a more accurate source image along with an estimate of the source-time function, which carries important information on the rupturing process and source mechanism.},
	keywords = {microseismic, noise, SEG, sources, sparse, wave equation},
	doi = {10.1190/segam2016-13871022.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/sharan2016SEGspj/sharan2016SEGspj.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/sharan2016SEGspj/sharan2016SEGspj_pres.pdf},
	author = {Shashin Sharan and Rongrong Wang and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {sharan2015SINBADFscu,
	title = {Source collocation using the method of Linearized Bregman},
	year = {2015},
	publisher = {SINBAD},
	abstract = {In this work, we present a method to collocate sources from seismic reflections recorded at receiver locations. We use the method of linearized Bregman. This algorithm focuses unknown sources by promoting sparsity in the {\textquotedblleft}Helmholtz domain{\textquotedblright}{\textemdash}i.e., the wavefield under the action of the the Helmholtz system{\textemdash}under the constraint of a data misfit within a particular tolerance. We extend this method to noisy measurements. We are in particular interested in situations where the noise is coherent because it is in the range of the Helmholtz equation.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/sharan2015SINBADFscu/sharan2015SINBADFscu.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/sharan2015SINBADFscu/sharan2015SINBADFscu.mov},
	author = {Shashin Sharan and Rongrong Wang and Felix J. Herrmann}
}
