@unpublished {park2026IMAGEnoa,
	title = {Neural Operators with Accurate Jacobian for High-Fidelity Image-Domain Seismic Inversion},
	year = {2026},
	month = {3},
	abstract = {Neural operators (NOs) provide efficient surrogates for wave-equation-based imaging operators, but accurate forward prediction alone does not guarantee reliable inversion. As inversion updates depend on the adjoint Jacobian, errors in learned sensitivities lead to unphysical updates and degraded quality in recovered models. To this end, we propose a Jacobian-informed training strategy that enforces accuracy in Jacobian in the directions where gradient descent occurs during Least-Squares. Numerical experiments with Fourier Neural Operators demonstrate that supervising only a small fraction ($0.1 \sim 0.01\%$) of Jacobian information significantly improves gradient quality and leads to more accurate subsurface velocity reconstruction. In particular, the proposed approach reduces mid to deep layer artifacts and improves structural continuity compared to models trained only with forward misfit term. These results highlight that learning inversion-relevant sensitivities, rather than forward mappings alone, is critical for reliable surrogate-assisted seismic inversion.},
	keywords = {deep learning, derivative-informed, Fisher-information, FNO, IMAGE, Imaging, Inverse problems, reduced-order modeling, RTM, SEG, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Submitted/2026/park2026IMAGEnoa/abstract.html},
	url2 = {https://slim.gatech.edu/Publications/Public/Submitted/2026/park2026IMAGEnoa/IMAGE_Jayjay.pdf},
	author = {Jeongjin Park and Huseyin Tuna Erdinc and Grant Bruer and Richard Rex and Nisha Chandramoorthy and Felix J. Herrmann}
}
@presentation {bruer2025GSCSsmc,
	title = {Seismic monitoring of CO2 using ensemble Kalman filtering},
	year = {2025},
	note = {(GSCS, Gatech)},
	month = {02},
	keywords = {CCS, EnKF, fluid flow, GCS, GSCS, Imaging, monitoring, RTM, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Posters/GSCS/2025/bruer2025GSCSsmc/2025GSCS_poster.pdf},
	author = {Grant Bruer and Abhinav Prakash Gahlot and Felix Herrmann and Edmond Chow}
}
@presentation {bruer2024ML4SEISMICsmp,
	title = {Seismic monitoring of CO2 plume dynamics using ensemble Kalman filtering},
	year = {2024},
	month = {11},
	abstract = {Monitoring CO 2  injected and stored in subsurface reservoirs is critical for avoiding failure scenarios and enables real-time optimization of CO 2  injection rates. Sequential Bayesian data assimilation (DA) is a statistical method for combining information over time from multiple sources to estimate a hidden state, such as the spread of the subsurface CO 2  plume. An example of scalable and efficient sequential Bayesian DA is the ensemble Kalman filter (EnKF). We improve upon existing DA literature in the seismic-CO 2  monitoring domain by applying this scalable DA algorithm to a high-dimensional CO 2  reservoir using two-phase flow dynamics and time-lapse full waveform seismic data with a realistic surface-seismic survey design. We show more accurate estimates of the CO 2  saturation field using the EnKF compared to using either the seismic data or the fluid physics alone. Furthermore, we test a range of values for the EnKF hyperparameters and give guidance on their selection for seismic CO 2  reservoir monitoring.},
	keywords = {Bayesian inference, data assimilation, ensemble Kalman filter, FWI, Imaging, Inverse problems, ML4SEISMIC, RTM, SLIM, two-phase flow, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/bruer2024ML4SEISMICsmp},
	author = {Grant Bruer and Abhinav Prakash Gahlot and Edmond Chow and Felix J. Herrmann}
}
