Collaborative Research: CAIG: Discovering the Law of Stress Transfer and Earthquake Dynamics in a Fault Network using a Computational Graph Discovery Approach

Many geologic basins around the world are heavily faulted, with the number of faults in the hundreds. Predicting the response of such basins to earthquakes or other subsurface events requires understanding the mechanical response of the fault network to external forces like the flow of underground water. Dynamics of individual faults in the network can be different when compared with the dynamics of the overall network because the faults interact with each other via stress transfer mechanisms. This interactive behavior among faults affects the earthquake distribution and ground deformation pattern for the basin. Most existing geological models struggle to capture these dynamics because they lack the ability to account for such differences among faults within the network. This project builds an AI Model using satellite data, subsurface imagery, and other geological information to better understand fault dynamics. This work will enable assessment of regional earthquake and hazard probabilities in tectonically active regions. The model will further provide insight into sustainable and cleaner energy processes of the future. Joint workshops on AI in computational mechanics and seismology will be held to train, upskill, recruit, and reward a diverse body of undergraduate and graduate students. This project builds a multiphysics fault network model to discover reduced-order governing equations for the evolution of stress in complex fault systems. The study region is the Southern Permian Basin in the Netherlands. It uses a novel Computational Graph Discovery and Completion algorithm with Gaussian Process kernels to discover the reduced-order governing equations describing the evolution of stress and stability in the network. These network governing equations are hypothesized to provide orders of magnitude gain in computational speed relative to the current direct numerical simulation algorithms used in the field and will additionally provide insights into multiphysics effects of fluid injection/extraction on stress transfer mechanisms. This model will create new opportunities in subsurface imaging by assimilating flow, petrophysical, seismic, and geodetic data to discover hidden fault networks capable of hosting earthquakes. This award by the Division of Research, Innovation, Synergies, and Education within the Directorate for Geosciences is jointly supported by the Division of Mathematical Sciences within the Directorate for Mathematical and Physical Sciences. 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.