@conference {herrmann2025AIDT4EScad,
	title = {Context-aware Digital Twin for Underground Storage},
	booktitle = {Artificial Intelligence and Digital Twins for Earth Systems 2025, Austin, TX},
	year = {2025},
	month = {9},
	keywords = {AIDT4ES, Amortized Variational Inference, Bayesian inference, conditional normalizing flows, data assimilation, deep learning, digital twin, experimental design, GCS, Imaging, Inverse problems, permeability, reservoir simulation, rock physics, RTM, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/AIDT4ES/2025/herrmann2025AIDT4EScad},
	author = {Abhinav Prakash Gahlot and Haoyun Li and Ipsita Bhar and Huseyin Tuna Erdinc and Rafael Orozco and Ziyi Yin and Felix J. Herrmann}
}
@conference {herrmann2025IMSIcad,
	title = {Context-Aware Digital Twin for Underground Storage Operations and Decision Making},
	booktitle = {Data Assimilation and Inverse Problems for Digital Twins Workshop 205, IMSI, Chicago},
	year = {2025},
	month = {10},
	keywords = {Amortized Variational Inference, Bayesian inference, conditional normalizing flows, data assimilation, decision making, digital twin, experimental design, GCS, Imaging, Inverse problems, permeability, reservoir simulation, rock physics, RTM, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/IMSI/2025/herrmann2025IMSIcad},
	author = {Abhinav Prakash Gahlot and Haoyun Li and Ipsita Bhar and Huseyin Tuna Erdinc and Rafael Orozco and Ziyi Yin and Felix J. Herrmann}
}
@conference {yin2024MINESGIGACO2cpi,
	title = {Coupled Permeability Inversion from Time-Lapse Seismic Data},
	booktitle = {Geophysical Research for Gigatonnes CO2 Storage, Colorado School of Mines},
	year = {2024},
	month = {7},
	keywords = {coupled inversion, end-to-end, fluid-flow, GCS, inversion, monitoring},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/MINESGIGACO2/2024/yin2024MINESGIGACO2cpi},
	author = {Ziyi Yin and Mathias Louboutin and Olav M{\o}yner and Felix J. Herrmann}
}
@conference {yin2023IMAGEcpi,
	title = {Coupled physics inversion for geological carbon storage monitoring},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2023},
	note = {(IMAGE, Houston)},
	month = {08},
	abstract = {Understanding CO2 plume behavior is key to the success of geological carbon storage projects. While two-phase flow equations provide a good model to make predictions on future CO2 plume behavior, these equations rely on having access to the true permeability model. Unfortunately, accurate information on the permeability is unavailable, which greatly jeopardizes our ability to predict CO2 plume behavior. To overcome this problem, we estimate the permeability from time-lapse seismic data via a coupled inversion methodology that improve as more monitoring data becomes available over time.},
	keywords = {CCS, coupled inversion, end-to-end, fluid-flow, inversion, monitoring, SEG},
	url = {https://slimgroup.github.io/IMAGE2023/yin2023IMAGEend2end/abstract.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2023/yin2023IMAGEcpi},
	author = {Ziyi Yin and Mathias Louboutin and Olav M{\o}yner and Felix J. Herrmann}
}
@conference {yin2021SEGcts,
	title = {Compressive time-lapse seismic monitoring of carbon storage and sequestration with the joint recovery model},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2021},
	note = {(IMAGE, Denver)},
	month = {09},
	pages = {3434-3438},
	abstract = {Time-lapse seismic monitoring of carbon storage and sequestration is often challenging because the time-lapse signature of the growth of CO$_2$ plumes is weak in amplitude and therefore difficult to detect seismically. This situation is compounded by the fact that the surveys are often coarsely sampled and not replicated to reduce costs. As a result, images obtained for different vintages (baseline and monitor surveys) often contain artifacts that may be attributed wrongly to time-lapse changes. To address these issues, we propose to invert the baseline and monitor surveys jointly. By using the joint recovery model, we exploit information shared between multiple time-lapse surveys. Contrary to other time-lapse methods, our approach does not rely on replicating the surveys to detect time-lapse changes. To illustrate this advantage, we present a numerical sensitivity study where CO$_2$ is injected in a realistic synthetic model. This model is representative of the geology in the southeast of the North Sea, an area currently considered for carbon sequestration. Our example demonstrates that the joint recovery model improves the quality of time-lapse images allowing us to monitor the CO$_2$ plume seismically.},
	keywords = {CCS, Compressive Sensing, Imaging, JRM, marine, SEG, time-lapse},
	doi = {10.1190/segam2021-3569087.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2021/yin2021SEGcts/yin2021SEGcts.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2021/yin2021SEGcts/Tue-9-28-Yin.html},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2021/yin2021SEGcts/yin2021SEGcts.mp4},
	software = {https://github.com/slimgroup/Software.SEG2021},
	author = {Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
