@conference {rizzuti2019SEGadf,
	title = {A dual formulation for time-domain wavefield reconstruction inversion},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2019},
	note = {(SEG, San Antonio)},
	month = {09},
	pages = {1480-1485},
	abstract = {We illustrate a dual formulation for full-waveform inversion potentially apt to large 3-D problems. It is based on the optimization of the wave equation compliance, under the constraint of data misfit not exceeding a prescribed noise level. In the Lagrangian formulation, model and wavefield state variables are complemented with multipliers having the same dimension of data ("dual data" variables). Analogously to classical wavefield reconstruction inversion, the wavefield unknowns can be projected out in closed form, by solving a version of the augmented wave equation. This leads to a saddle-point problem whose variables are only model and dual data. As such, this formulation represents a model extension, and is potentially robust against local minima. The classical downsides of model extension methods and wavefield reconstruction inversion are here effectively mitigated: storage of the dual variables is affordable, the augmented wave equation is amenable to time-marching finite-difference schemes, and no continuation strategy for penalty parameters is needed, with the prospect of 3-D applications.},
	keywords = {3D, Full-waveform inversion, SEG, Time-domain},
	doi = {10.1190/segam2019-3216760.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/rizzuti2019SEGadf/rizzuti2019SEGadf.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/rizzuti2019SEGadf/rizzuti2019SEGadf_pres.pdf},
	software = {https://github.com/slimgroup/Software.rizzuti2019SEGadf},
	author = {Gabrio Rizzuti and Mathias Louboutin and Rongrong Wang and Emmanouil Daskalakis and Felix J. Herrmann}
}
@conference {sharan2018SEGada,
	title = {A debiasing approach to microseismic},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2018},
	note = {(SEG, Anaheim)},
	month = {10},
	pages = {2942-2946},
	abstract = {Microseismic data is often used to locate fracture locations and their origin in time created by fracking. Although surface microseismic data can have large apertures and is easier to acquire than the borehole data, it often suffers from poor signal to noise ratio (S/R). Poor S/R poses a challenge in terms of estimating the correct location and source-time function of a microseismic source. In this work, we propose a denoising step in combination with a computationally cheap debiasing based approach to locate microseismic sources and to estimate their source-time functions with correct amplitude from extremely noisy data. Through numerical experiments, we demonstrate that our method can work with closely spaced microseismic sources with source-time functions of different peak amplitudes and frequencies. We have also shown the ability of our method with the smooth velocity model.},
	keywords = {Full-waveform inversion, Imaging, microseismic, noise, SEG, wave equation},
	doi = {10.1190/segam2018-2997252.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/sharan2018SEGada/sharan2018SEGada.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/sharan2018SEGada/sharan2018SEGada_pres.pdf},
	author = {Shashin Sharan and Rajiv Kumar and Rongrong Wang and Felix J. Herrmann}
}
@conference {yang2018SEGrde,
	title = {Removing density effects in LS-RTM with a low-rank matched filter},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2018},
	note = {(SEG, Anaheim)},
	month = {10},
	pages = {4251-4255},
	abstract = {Least-squares reverse-time migration faces difficulties when it inverts the data containing strong components related to density variation with velocity-only Born modeling operator. The strong density perturbation will be inverted as strong dummy velocity perturbations, which influence the amplitudes and phase of the velocity perturbations in the inverted model. The traditional method is to invert the additional density variations by developing Born operator with respect to both density and velocity or modify the image condition. In this work, we develop a matched-filter based LS-RTM for velocity-only Born modeling operator, which removes the artifacts in the imaging created by the strong density variation. This method doesn{\textquoteright}t call for extra work of finite difference stencil and is more general. In the experiment part, we use a complex discontinuous layered medium with strong density variations at the ocean bottom, and show the efficacy of the propose formulation.},
	keywords = {density effect, Least square, low rank, SEG},
	doi = {10.1190/segam2018-2997814.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/yang2018SEGrde/yang2018SEGrde.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/yang2018SEGrde/yang2018SEGrde_pres.pdf},
	author = {Mengmeng Yang and Rajiv Kumar and Rongrong Wang and Felix J. Herrmann}
}
@conference {wang2017SEGdff,
	title = {A denoising formulation of full-waveform inversion},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2017},
	note = {(SEG, Houston)},
	month = {09},
	pages = {1594-1598},
	abstract = {We propose a wave-equation-based subsurface inversion method that in many cases is more robust than the conventional Full-Waveform Inversion. The new formulation is written in a denoising form that allows the synthetic data to match the observed ones up to a small error. Compared to the Full-Waveform Inversion, our method treats the noise arising from the data measuring/recording process and that from the synthetic modelling process separately. Comparing to the Wavefields Reconstruction Inversion, the new formulation mitigates the difficulty of choosing the penalty parameter λ. To solve the proposed optimization problem, we develop an efficient frequency domain algorithm that alternatively updates the model and the data. Numerical experiments confirm strong stability of the proposed method by comparisons between the results of our algorithm with that from both plain FWI and a weighted formulation of the FWI.},
	keywords = {denoising, FWI, SEG},
	doi = {10.1190/segam2017-17794690.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/wang2017SEGdff/wang2017SEGdff.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/wang2017SEGdff/wang2017SEGdff_pres.pdf},
	author = {Rongrong Wang and Felix J. Herrmann}
}
@conference {sharan2017SEGhrf,
	title = {High resolution fast microseismic source collocation and source time function estimation},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2017},
	note = {(SEG, Houston)},
	month = {09},
	pages = {2778-2783},
	abstract = {Sparsity promotion based joint microseismic source collocation and source time function estimation, using Linearized Bregman algorithm, although simple to implement, suffers from slow convergence. This is due to the fact that Linearized Bregman algorithm has only first order of convergence. In this work, we propose to accelerate the existing Linearized Bregman algorithm using the L-BFGS algorithm. Without any initial guess for the source location or source time function, our method is able to estimate the source location and source time function for kinematically correct smooth velocity model. We demonstrate the effectiveness of our method for multiple sources spaced within half a wavelength. We also compare our results with Linearized Bregman based method in {\textquoteleft}{\textquoteleft}2.5{\textquoteleft}{\textquoteleft}D instead of {\textquoteleft}{\textquoteleft}2{\textquoteleft}{\textquoteleft}D.},
	keywords = {algorithm, Full-waveform inversion, microseismic, passive imaging, SEG, wave equation},
	doi = {10.1190/segam2017-17787749.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/sharan2017SEGhrf/sharan2017SEGhrf.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/sharan2017SEGhrf/sharan2017SEGhrf_pres.pdf},
	author = {Shashin Sharan and Rongrong Wang and Felix J. Herrmann}
}
@conference {wang2016SEGfde,
	title = {Frequency down-extrapolation with TV norm minimization},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2016},
	note = {(SEG, Dallas)},
	month = {10},
	pages = {1380-1384},
	abstract = {In this work, we present a total-variation (TV)-norm minimization method to perform model-free low-frequency data extrapolation for the purpose of assisting full-waveform inversion. Low-frequency extrapolation is the process of extending reliable frequency bands of the raw data towards the lower end of the spectrum. To this end, we propose to solve a TV-norm based convex optimization problem that has a global minimum and is equipped with a fast solver. The approach takes into account both the sparsity of the reflectivity series associated with a single trace, as well as the inter-trace correlations. A favorable byproduct of this approach is that it allows one to work with coarsely sampled trace along time (as coarse as 0.02s per sample), hence substantially reduces the size of the proposed optimization problem. Last, we show the effectiveness of the method for frequency-domain FWI on the Marmousi model.},
	keywords = {extrapolation, FWI, SEG, sparse},
	doi = {10.1190/segam2016-13879674.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/wang2016SEGfde/wang2016SEGfde.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/wang2016SEGfde/wang2016SEGfde_pres.pdf},
	author = {Rongrong Wang and Felix J. Herrmann}
}
@conference {chai2016SEGlbm,
	title = {A linearized Bregman method for compressive waveform inversion},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2016},
	note = {(SEG, Dallas)},
	month = {10},
	pages = {1449-1454},
	abstract = {We present an implementation of a recently developed and relatively simple linearized Bregman method to solve the large-scale $\ell_1$-norm sparsity-promoting Gauss-Newton (GN) full-waveform inversion (FWI) problem. Numerical experiments demonstrate that: the much simpler linearized Bregman method does a comparable and even superior job compared to a state-of-the-art $\ell_1$-norm solver SPGL1, which is previously used in the modified Gauss-Newton FWI; the linearized Bregman method is also more efficient (faster) than SPGL1; the FWI result with the linearized Bregman method solving $\ell_1$-norm sparsity-promoting problems to get the model updates is better than that obtained by solving $\ell_2$-norm least-squares problems to get the model updates.},
	keywords = {Bregman method, SEG, waveform inversion},
	doi = {10.1190/segam2016-13848105.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/chai2016SEGlbm/chai2016SEGlbm.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/chai2016SEGlbm/chai2016SEGlbm_pres.pdf},
	author = {Xintao Chai and Mengmeng Yang and Philipp A. Witte and Rongrong Wang and Zhilong Fang 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}
}
