@presentation {orozco2023ML4SEISMICuqs,
	title = {Uncertainty quantification so what? Leveraging probabilistic seismic inversion for experimental design},
	year = {2023},
	month = {11},
	abstract = {Combining physics with recent developments of generative machine learning enables a scalable probabilistic framework for tackling seismic inversion problems including Full-Waveform Inversion. These probabilistic results can be proven to be from the Bayesian posterior but how exactly can we use them for practical downstream tasks? In this talk, we answer the question with a practical application of the probabilistic framework towards designing ocean bottom node placement of seismic imaging. With a simple adjustment to the original training objective, we show that jointly optimizing for an experimental design corresponds to maximizing the expected information gain used by the Bayesian community. After verifying this novel joint optimization with a stylized problem, we demonstrate its application for optimizing the placement of ocean bottom nodes in a synthetic seismic imaging experiment.},
	keywords = {bayesian, experimental design, FWI, ML4SEISMIC, Normalizing flows, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/orozco2023ML4SEISMICuqs},
	author = {Rafael Orozco and Mathias Louboutin and Peng Chen and Felix J. Herrmann}
}
