@unpublished {park2026IMAGEnoa,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}
}
@conference {peters2014EAGEweb,
	title = {Wave-equation based inversion with the penalty method: adjoint-state versus wavefield-reconstruction inversion},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2014},
	month = {06},
	abstract = {In this paper we make a comparison between wave-equation based inversions based on the adjoint-state and penalty methods. While the adjoint-state method involves the minimization of a data-misfit and exact solutions of the wave-equation for the current velocity model, the penalty-method aims to first find a wavefield that jointly fits the data and honours the physics, in a least-squares sense. Given this reconstructed wavefield, which is a proxy for the true wavefield in the true model, we calculate updates for the velocity model. Aside from being less nonlinear{\textendash}the acoustic wave equation is linear in the wavefield and model parameters but not in both{\textendash}the inversion is carried out over a solution space that includes both the model and the wavefield. This larger search space allows the algortihm to circumnavigate local minima, very much in the same way as recently proposed model extentions try to acomplish. We include examples for low frequencies, where we compare full-waveform inversion results for both methods, for good and bad starting models, and for high frequencies where we compare reverse-time migration with linearized imaging based on wavefield-reconstruction inversion. The examples confirm the expected benefits of the proposed method.},
	keywords = {EAGE, Full-waveform inversion, Imaging, Optimization, penalty method},
	doi = {10.3997/2214-4609.20140704},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/peters2014EAGEweb/peters2014EAGEweb.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/peters2014EAGEweb/peters2014EAGEweb_pres.pdf},
	author = {Bas Peters and Felix J. Herrmann and Tristan van Leeuwen}
}
