Collaborative Research: CAIG: Physics-Informed Conditional Generative Inversion of Fracture Fields

Understanding how water moves through fractured underground rocks is essential for management of groundwater and other subsurface resources. This project will develop new artificial intelligence tools to create more accurate maps of underground fracture networks. The project will improve our ability to manage water and energy resources and reduce risks in subsurface engineering. It will help train the next generation of scientists and engineers and provide hands-on learning experiences to K–12 students. This project develops a physics-informed conditional generative deep learning framework for inverse characterization of fractured rock systems using hydraulic and tracer tomography. The research will develop and evaluate approaches to efficiently estimate fracture network geometry, hydraulic properties, and associated uncertainties. Governing physical laws will be incorporated through embedded numerical simulators to improve learning robustness and generalization under sparse observations. The methods will be validated using synthetic datasets, 3D-printed fractured rock experiments, and field observations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.