@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}
}
@presentation {yin2022ML4SEISMICutc,
	title = {Uncertainty-aware time-lapse CO$_2$ monitoring with learned end-to-end inversion},
	year = {2022},
	month = {11},
	abstract = {Seismic monitoring of CO2 sequestration is computationally expensive as it involves modeling of both fluid-flow physics modeling and wave physics and differentiation through the solvers with respect to the subsurface properties of interest. In this talk, we demonstrate the effectiveness of learned coupled inversion framework using a pre-trained Fourier neural operator as a learned surrogate for the fluid-flow simulator, which greatly reduces the cost associated with fluid-flow modeling and differentiation through the solver. We study the effectiveness and correctness of inversion based on Fourier neural operator surrogate and a normalizing flow prior. We also demonstrate the efficacy of this framework on monitoring the growth of CO2 plumes during sequestration, and on uncertainty quantification of the permeability and CO2 plumes with conditional normalizing flow. With this framework, we can further forecast the CO2 plume in the future without any acquired seismic data with uncertainty estimation.},
	keywords = {amortized Bayes, conditional normalizing flows, deep learning, Fourier neural operators, GCS, inversion, machine learning, ML4SEISMIC, Normalizing flows, SLIM, time-lapse, uncertain quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/yin2022ML4SEISMICutc/index.html},
	author = {Ziyi Yin and Rafael Orozco and Ali Siahkoohi and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {siahkoohi2021ML4SEISMICuqi,
	title = {Uncertainty quantification in imaging and automatic horizon tracking{\textemdash}a Bayesian deep-prior based approach},
	year = {2021},
	month = {11},
	abstract = {In inverse problems, uncertainty quantification (UQ) deals with a probabilistic description of the solution nonuniqueness and data noise sensitivity. Setting seismic imaging into a Bayesian framework allows for a principled way of studying uncertainty by solving for the model posterior distribution. Imaging, however, typically constitutes only the first stage of a sequential workflow, and UQ becomes even more relevant when applied to subsequent tasks that are highly sensitive to the inversion outcome. In this paper, we focus on how UQ trickles down to horizon tracking for the determination of stratigraphic models and investigate its sensitivity with respect to the imaging result. As such, the main contribution of this work consists in a data-guided approach to horizon tracking uncertainty analysis. This work is fundamentally based on a special reparameterization of reflectivity, known as {\textquotedblleft}deep prior{\textquotedblright}. Feasible models are restricted to the output of a convolutional neural network with a fixed input, while weights and biases are Gaussian random variables. Given a deep prior model, the network parameters are sampled from the posterior distribution via a Markov chain Monte Carlo method, from which the conditional mean and point-wise standard deviation of the inferred reflectivities are approximated. For each sample of the posterior distribution, a reflectivity is generated, and the horizons are tracked automatically. In this way, uncertainty on model parameters naturally translates to horizon tracking. As part of the validation for the proposed approach, we verified that the estimated confidence intervals for the horizon tracking coincide with geologically complex regions, such as faults.},
	keywords = {horizon picking, Imaging, machine learning, ML4SEISMIC, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/siahkoohi2021ML4SEISMICuqi/Mon-11-20-Siahkoohi.pdf},
	author = {Ali Siahkoohi and Gabrio Rizzuti and Felix J. Herrmann}
}
