@presentation {wang2017SINBADFnra,
	title = {Noise robust and time-domain formulations of Wavefield Reconstruction Inversion},
	year = {2017},
	publisher = {SINBAD},
	abstract = {We propose a wave-equation-based subsurface inversion method that in many cases is more robust than 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 regular Full-Waveform Inversion, our method treats the noise arising from the data meassuring/recording process and that from the synthetic modelling process separately. Compared to 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. We also discuss a new memory efficient time-domain formulation for Wavefield Reconstruction Inversion based on duality.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/wang2017SINBADFnra/wang2017SINBADFnra.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/wang2017SINBADFnra/wang2017SINBADFnra.mov},
	author = {Rongrong Wang and Mathias Louboutin and Bas Peters and Emmanouil Daskalakis and Felix J. Herrmann}
}
@presentation {sharan2017SINBADFtts,
	title = {Tracking the spatial-temporal evolution of fractures by microseismic source collocation},
	year = {2017},
	publisher = {SINBAD},
	abstract = {Unlike conventional reservoirs, unconventional plays are not naturally viable for economical production of oil and gas. They require stimulation by injecting high-pressure fluid causing fractures in the rocks. These fractures make the medium more permeable, hence, the extraction of oil and gas becomes feasible. For drilling purposes and to prevent potentially hazardous situations, we need to have good knowledge of the location of these fractures. Also, we need to have good knowledge about how these fractures originated in time. Hydraulic fracturing changes stress in rocks, which results in the emission of microseismic waves. The opening of cracks due to high pressure fluid injection during hydraulic fracturing mainly causes this change in stress in the rocks. Therefore, microseismic events are mostly localized along these fractures and have finite energy along time. To accurately track the evolution of fractures in both space and time, we need to locate closely spaced microseismic events along these fractures activating at very small time intervals. A naive approach can be the back propagation of the observed data to find out a point in space and time where maximum focusing of back propagating energy occurs. This point corresponds to the location and origin time of a microseismic source. This approach, although simpler, suffers from low resolution and requires scanning of complete 4D volume (3D in space and 1D in time). Hence, this method can be challenging when there are multiple closely spaced microseismic sources originating at different times. We in this work propose a sparsity promotion based method that can locate closely spaced microseismic events, with spatial separation as low as within half a wavelength, activating at small time intervals. We simultaneously estimate the origin time of microseismic events by estimating their source time functions. Our method exploits the fact that microseismic events are localized in space and have finite energy. We use accelerated Linearized Bregman algorithm with a preconditioning operator to arrive at a computationally feasible scheme.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/sharan2017SINBADFtts/sharan2017SINBADFtts.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/sharan2017SINBADFtts/sharan2017SINBADFtts.mov},
	author = {Shashin Sharan and Rongrong Wang and Felix J. Herrmann}
}
