@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}
}
@presentation {herrmann2023SCAIMact,
	title = {Act normal that{\textquoteright}s crazy enough {\textemdash} an overview of seismic inversion with normalizing flows and surrogate modeling},
	year = {2023},
	note = {(SCAIM, Vancover)},
	month = {03},
	abstract = {During this talk, I will give an overview on how techniques from neural (conditional) density estimation and surrogate modeling can be used to solve challenging problems in seismic imaging and monitoring of geological carbon storage. I will start by outlining how (conditional) normalizing flows can be used as priors, to regularize inverse problems, and as low-fidelity amortized posteriors for wave-based inversions. To this end, I will uss techniques from simulation-based inference. When time, permits I will also talk about permeability inversion from time-lapse seismic data using neural surrogates (Fourier Neural Operators) to mimic solution operators of two-phase flow equations.},
	keywords = {CCS, FNO, GCS, monitoring, Normalizing flows, SCAIM, sequential Bayes, SLIM, surrogate, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SCAIM/2023/herrmann2023SCAIMact/index.html},
	author = {Felix J. Herrmann}
}
