@presentation {rex2024ML4SEISMICfca,
	title = {FNO-charged ASPIRE},
	year = {2024},
	month = {11},
	abstract = {During this talk, we will demonstrate how extended re-migrations{\textemdash}i.e, formation of subsurface-offset Common-Image Gathers (CIGs) for a new velocity model, can be avoided altogether by training Fourier Neural Operators during training of ASPIRE {\textemdash} Amortized posteriors with Summaries that are Physics-based and Iteratively REfined. In this approach, FNOs are trained as surrogates capable of mapping CIGs for one migration-velocity model to the other. The approach is computationally feasible because it uses the same training set as used during ASPIRE. As a result, additional training costs are small and the inference costs are reduced by a factor equal to the number of ASPIRE refinements.},
	keywords = {deep learning, FNO, hierarchical tucker tensor, Inverse problems, kronecker product, ML4SEISMIC, SLIM, two-phase flow},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/rex2024ML4SEISMICfca},
	author = {Richard Rex and Yunlin Zeng and Ziyi Yin and Rafael Orozco and Felix J. Herrmann}
}
@presentation {orozco2024ML4SEISMICmev,
	title = {Machine-learning enabled velocity-model building with uncertainty quantification},
	year = {2024},
	month = {11},
	abstract = {Accurately characterizing subsurface properties is crucial for a wide range of geophysical applications, from hydrocarbon exploration to monitoring of CO2 sequestration projects. Traditional characterization methods such as Full-Waveform Inversion (FWI) represent powerful tools but often struggle with the inherent complexities of the inverse problem, including noise, limited bandwidth and aperture of data, limited azimuth and computational constraints. To address these challenges, we propose a scalable methodology that integrates generative modeling with physics-informed summary statistics, making it suitable for complicated imaging problems potentially including field datasets. Our approach leverages the power of conditional diffusion networks, and methodologically incorporates physics in the form of summary statistics, allowing for the computationally efficient generation of Bayesian posterior samples that offer an useful assessment of uncertainty of the inferred migration-velocity models. To validate our approach, we introduce a battery of tests that measure the quality of the image estimates as well as the quality of the inferred uncertainties. With modern synthetic datasets, we maximally leverage the advantages of using subsurface-offset Common Image Gathers (CIGs) as the conditioning observable. Next, we tackle the challenging SEAM salt model that requires incorporating salt flooding into our approach based on the iterative refinements of ASPIRE {\textemdash} Amortized posteriors with Summaries that are Physics-based and Iteratively REfined.},
	keywords = {Amortized Variational Inference, Bayesian inference, deep learning, diffusion models, FWI, Imaging, Inverse problems, ML4SEISMIC, RTM, SLIM, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/orozco2024ML4SEISMICmev},
	author = {Rafael Orozco and Huseyin Tuna Erdinc and Thales Souza and Yunlin Zeng and Ziyi Yin and Felix J. Herrmann}
}
