@mastersthesis {orozco2024THgmf,
	title = {GENERATIVE MODELS FOR UNCERTAINTY QUANTIFICATION OF MEDICAL AND SEISMIC IMAGING},
	year = {2024},
	note = {(PhD)},
	month = {12},
	school = {Georgia Institute of Technology},
	type = {phd},
	address = {Atlanta},
	abstract = {This thesis investigates the intersection of machine learning-based generative models with physics-based methods to address imaging problems, with an emphasis on accelerat- ing computations while incorporating uncertainty quantification. Throughout the chapters, a key conclusion emerges: machine learning methods, though powerful, are insufficient when used in isolation. They must be combined with the trusted domain knowledge con- tained in numerical physics simulations to achieve robust results. The methods presented bridge two of the most impactful areas in modern computer science: numerical simula- tions methods rooted in linear algebra and the transformative potential of deep learning, particularly as exemplified by recent advancements in generative modeling. The focus of this work is on scenarios where the underlying physics is computation- ally expensive, requiring frugal use of simulations. This is particularly relevant in high- dimensional, ill-posed inverse problems, such as those encountered in the applications shown in this thesis: medical imaging and seismic exploration, where the forward oper- ator is governed by complex partial differential equations (PDEs). To address these challenges, this thesis introduces techniques that blend practical ma- chine learning approaches with theoretical insights, particularly through the use of physics- based summary statistics. These statistics enable efficient extraction of meaningful in- formation from physics simulations, reducing computational overhead while preserving the critical elements of the physical model. Theoretical foundations underpin the design choices, ensuring that the methods are both efficient and ameliorate the potential bias that would arise from using physics-based summary statistics instead of raw observations. As an engineering-focused work, the thesis places a strong emphasis on practicality and robustness. The proposed methods are stress-tested through validation experiments, on increasingly complex scenarios with a clear pathway toward deployment in the real-world. This applied perspective reflects the ultimate goal of leveraging these methods to create tangible, impactful changes in domains such as healthcare and geophysics. By bridging the gap between advanced machine learning and trusted physics-based methods, this work contributes to the development of innovative tools that balance computational efficiency, uncertainty quantification, and practical applicability.},
	keywords = {Bayesian inference, CCS, CIG, conditional normalizing flows, FWI, GCS, Generative models, Inverse problems, medical imaging, monitoring, Normalizing flows, PhD, RTM, scientific machine learning, time-lapse, Uncertainty quantification, Variational Inference, WISE, WISER},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2024/orozco2024THgmf/Thesis_Orozco_pdf.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Thesis/2024/orozco2024THgmf/defense-real},
	author = {Rafael Orozco}
}
@mastersthesis {oghenekohwo2017THetl,
	title = {Economic time-lapse seismic acquisition and imaging{\textendash}-Reaping the benefits of randomized sampling with distributed compressive sensing},
	year = {2017},
	note = {(PhD)},
	month = {08},
	school = {The University of British Columbia},
	type = {phd},
	address = {Vancouver},
	abstract = {This thesis presents a novel viewpoint on the implicit opportunities randomized surveys bring to time-lapse seismic - which is a proven surveillance tool for hydrocarbon reservoir monitoring. Time-lapse (4D) seismic combines acquisition and processing of at least two seismic datasets (or vintages) in order to extract information related to changes in a reservoir within a specified time interval. The current paradigm places stringent requirements on replicating the 4D surveys, which is an expensive task often requiring uneconomical dense sampling of seismic wavefields. To mitigate the challenges of dense sampling, several advances in seismic acquisition have been made in recent years including the use of multiple sources firing at near simultaneous random times, and the adaptation of Compressive Sensing (CS) principles to design practical acquisition engines that improve sampling efficiency for seismic data acquisition. However, little is known regarding the implications of these developments for time-lapse studies. By conducting multiple experiments modelling surveys adhering to the principles of CS for 4D seismic, I propose a model that demonstrates the feasibility of randomized acquisitions for time-lapse seismic. The proposed joint recovery model (JRM), which derives from distributed CS, exploits the common information in time-lapse data during recovery of dense wavefields from measured subsampled data, providing highly repeatable and high-fidelity vintages. I show that we obtain better vintages when randomized surveys are not replicated, in contrast to standard practice, paving the way for an opportunity to relax the rigorous requirement to replicate surveys precisely. We assert that the vintages obtained using our proposed model are of sufficient quality to serve as inputs to processes that extract time-lapse attributes from which subsurface changes are deduced. Additionally, I show that recovery with the JRM is robust with respect to errors due to differences between actual and recorded postplot information. Finally, I present an opportunity to adapt our model to problems related to time-lapse seismic imaging where the main finding is that we can better delineate time-lapse changes by adapting the joint recovery model to wave-equation based inversion methods.},
	keywords = {Acquisition, Compressive Sensing, distributed compressive sensing, joint recovery, PhD, thesis, time lapse},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2017/oghenekohwo2017THetl/oghenekohwo2017THetl.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Thesis/2017/oghenekohwo2017THetl/oghenekohwo2017THetl_pres.pdf},
	author = {Felix Oghenekohwo}
}
