@conference {rex2024IMAGEvc,
	title = {Velocity Continuation for Common Image Gathers with Fourier Neural Operators},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2024},
	month = {8},
	abstract = {Common-image gathers (CIGs) are pivotal in migration-velocity analysis (MVA). However, MVA is often hindered by the computational burden of traditional migration methods. To bypass these limitations, we introduce a neural-surrogate learning approach that utilizes Fourier Neural Operators (FNOs, Li et al. 2020) to accelerate MVA. Following the velocity-continuation scheme of Siahkoohi, Louboutin, and Herrmann (2022), we train a survey-specific FNO to map the CIGs associated with one migration-velocity model to another without remigration. This methodology leverages the capacity of FNOs to approximate complex PDE-based mappings, rendering computational cost at inference negligible, thereby expediting MVA. By enabling rapid generation and evaluation of CIGs across various velocity models, it offers a pathway to quickly examine velocity models according to preferred properties and to quantify uncertainties in imaged reflectivities at the same time. Additionally, this methodology paves the way for inverse design optimization, updating velocity models to produce CIGs with desirable characteristics.},
	keywords = {CIG, deep learning, FNO, IMAGE, Imaging, SEG, Uncertainty quantification, velocity continuation},
	url = {https://slimgroup.github.io/IMAGE2024/Rex2024SEG/paper.html},
	author = {Richard Rex and Ziyi Yin and Felix J. Herrmann}
}
@conference {siahkoohi2022SEGvcw,
	title = {Velocity continuation with Fourier neural operators for accelerated uncertainty quantification},
	booktitle = {International Meeting for Applied Geoscience and Energy Expanded Abstracts},
	year = {2022},
	note = {(IMAGE, Houston)},
	month = {05},
	abstract = {Seismic imaging is an ill-posed inverse problem that is challenged by noisy data and modeling inaccuracies{\textendash}-due to errors in the background squared-slowness model. Uncertainty quantification is essential for determining how variability in the background models affects seismic imaging. Due to the costs associated with the forward Born modeling operator as well as the high dimensionality of seismic images, quantification of uncertainty is computationally expensive. As such, the main contribution of this work is a survey-specific Fourier neural operator surrogate to velocity continuation that maps seismic images associated with one background model to another virtually for free. While being trained with only 200 background and seismic image pairs, this surrogate is able to accurately predict seismic images associated with new background models, thus accelerating seismic imaging uncertainty quantification. We support our method with a realistic data example in which we quantify seismic imaging uncertainties using a Fourier neural operator surrogate, illustrating how variations in background models affect the position of reflectors in a seismic image.},
	keywords = {Fourier neural operators, SEG, Uncertainty quantification, velocity continuation},
	doi = {10.1190/image2022-3750475.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/siahkoohi2022SEGvcw/abstract.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/siahkoohi2022SEGvcw/Tue-16-30-Siahkoohi.pdf},
	software = {https://github.com/slimgroup/fno4vc},
	author = {Ali Siahkoohi and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {orozco2021ML4SEISMICvia,
	title = {Variational inference for artifact removal of adjoint solutions in photoacoustic problems},
	year = {2021},
	month = {11},
	abstract = {Photoacoustic is a medical imaging modality which combines light and ultrasound waves to image internal structures of biological tissue. The inverse problem reconstructs the tissue initially excited by light given propagated ultrasound data at receivers outside the tissue. Due to noisy, limited-view and sparse receiver data, traditional time-reversal adjoint solutions are highly ill-posed. This necessitates uncertainty quantification to communicate to practitioners which areas of the image can be trusted. We propose a framework which leverages a machine learning based method (Conditional Normalizing Flows) to learn the full posterior distribution of viable solutions given the time-reversal adjoint solution. We show that areas of calculated uncertainty correlate with structures that are known to be difficult to image. In addition, we also propose a MAP based solution, which solves the variational least-squares problem while using the trained Conditional Normalizing Flow as a prior distribution.},
	keywords = {conditional prior, deep image, MAP, ML4SEISMIC, normalizing flow, Photoacoustic, SLIM, Variational Inference},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/orozco2021ML4SEISMICvia/Tue-09-00-Orozco.html},
	author = {Rafael Orozco and Ali Siahkoohi and Gabrio Rizzuti and Felix J. Herrmann}
}
