Theses: By Year
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Theses
- Sa da Fonseca, J. (2024). Probabilistic assessment of pore pressure prediction with Bayesian Geophysical Basin Modeling [PhD, Stanford University]. https://searchworks.stanford.edu/view/in00000069359
- Wang, Y. (2023). A beautiful marriage between POMDPs and subsurface applications : decision making for subsurface systems [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/in00000019458
- Kanfar, R. (2023). Stochastic geomodelling and analysis of karst morphology [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/in00000033193
- Kashefi, K. (2023). Deep learning algorithms for computational mechanics on irregular geometries [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/14732781
- Hall, T. (2023). Efficient greenfield mineral exploration [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/in00000019459
- Wang, L. (2023). Integrating data and models for sustainable decision-making in hydrology [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/14641244
- Miltenberger, A. (2022). A measure-theoretic approach to Bayesian hypothesis testing and inversion with geophysical data [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/14233991
- Yang, L. (2021). Quantifying and visualizing uncertainty of 3D geological structures with implicit methods [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/14641244
- Petrov, S. (2021). Seismic image segmentation with deep learning [MS Thesis, Stanford University]. https://searchworks.stanford.edu/view/qf836dh0076
- Athens, N. (2021). Stochastic inversion of gravity data in fault-controlled geothermal systems [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/13826071
- Pollack, A. (2020). Quantifying Geological Uncertainty and Optimizing Technoeconomic Decisions for Geothermal Reservoirs [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/13680060
- Pradhan, A. (2020). Statistical learning and inference of subsurface properties under complex geological uncertainty with seismic data [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/13753880
- Nesvold, E. (2019). Building informative priors for the subsurface with generative adversarial networks and graphs [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/13423377
- Muradov, R. (2019). Inference of Sub-Resolution Stacking Patterns from Seismic Data in Spatially Coupled Models [MS Thesis, Stanford University]. https://searchworks.stanford.edu/view/km001pf4033
- Al Ibrahim, M. (2019). Petroleum System Modeling of Heterogeneous Organic-Rich Mudrocks [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/13250212
- Park, J. (2019). Uncertainty quantification and sensitivity analysis of geoscientific predictions with data-driven approaches [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/13250154
- Mendes, J. (2018). Morphdynamic Analysis and Statistical Synthesis of Geomorphic Data [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/12746435
- Dutta, G. (2018). Value of Information Analysis for Time-Lapse Seismic Data in Reservoir Development [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/12742067
- Li, L. (2017). A Bayesian Approach to Causal and Evidential Analysis for Uncertainty Quantification throughout the Reservoir Forecasting Process [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/12137330
- Aydin, O. (2017). A Bayesian Framework for Quantifying Fault Network Uncertainty Using a Marked Point Process Model [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/11950552