@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 {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}
}
