Spanning the land surface through deeper soils and into the bedrock, the subsurface critical zone represents a crucial area for resources such as critical minerals. Resource availability is shaped by complex geochemical, biological, and ecohydrological interactions, and predicting how biogeochemical cycling varies over space and time remains a major scientific hurdle. Current models are computationally expensive, lack critical subsurface processes, and have limited capacity to ingest observational data. Large datasets of subsurface moisture, temperature, and biogeochemistry such as those collected in Critical Zone Observatories and Critical Zone Networks, combined with emerging AI tools, represent an opportunity to build more efficient and accurate subsurface models. This project will combine mechanistic model simulations with extensive subsurface measurements to train a novel, AI-driven biogeochemical solver. The resulting AI-enabled system will integrate observational data with process knowledge to provide transformative improvements in computational efficiency and accuracy, enabling rapid, precise predictions of subsurface biogeochemical processes at scales relevant to DOE missions.
Partners
- Auburn University
- University of California Riverside
- Lawrence Berkeley National Laboratory
