@conference {gahlot2023IMAGEtsm,
	title = {Time-lapse seismic monitoring of geological carbon storage with the nonlinear joint recovery model},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2023},
	note = {(IMAGE, Houston)},
	month = {08},
	abstract = {During time-lapse seismic monitoring, weak 4D signal below the level of inversion or migration artifacts poses challenges. To address these, low-cost randomized non-replicated acquisitions and linear joint recovery model (JRM) have been introduced to take advantage of the shared information between different vintages in the time-lapse seismic data and subsurface structure undergoing localized changes. Since the relationship between seismic data and subsurface properties is seldomly linear, we propose a more versatile nonlinear JRM, which extends linear JRM to nonlinear forward modeling.},
	keywords = {CCS, JRM, monitoring, SEG},
	url = {https://slimgroup.github.io/IMAGE2023/NonLinear-JRM/abstract.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2023/gahlot2023IMAGEtsm},
	author = {Abhinav Prakash Gahlot and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {gahlot2023ML4SEISMICtsm,
	title = {Time-lapse seismic monitoring of geological carbon storage with the nonlinear joint recovery model},
	year = {2023},
	month = {11},
	abstract = {During time-lapse seismic monitoring of CO2 plumes, a weak 4D signal below the level of inversion or migration artifacts poses challenges. To address these, low-cost randomized non-replicated acquisitions and a linear joint recovery model (JRM) have been introduced. It takes advantage of the shared information between different vintages in the time-lapse seismic data and subsurface structure undergoing localized changes. Since the relationship between seismic data and subsurface properties is seldom linear, we propose a more versatile nonlinear JRM (nJRM) to invert for the squared slowness of the vintages. The nJRM takes advantage of the full nonlinear relation between these squared slownesses and time-lapse data through the wave equation. Also, careful derivation of the gradients makes the computational cost of nJRM equivalent to the independent recovery. We present a synthetic study for geological carbon storage (GCS) which shows that the non-replication can be beneficial to time-lapse imaging, making seismic monitoring of GCS less costly for the long term sustainability of the technology.},
	keywords = {CCS, JRM, ML4SEISMIC, monitoring, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/gahlot2023ML4SEISMICtsm},
	author = {Abhinav Prakash Gahlot and Mathias Louboutin and Ziyi Yin and Felix J. Herrmann}
}
