Publications

Forecasting Marine Sediment Properties with Geospatial Machine Learning

Frederick, Jennifer M.; Eymold, William K.; Nole, Michael A.; Phrampus, Benjamin J.; Lee, Taylor R.; Wood, Warren T.; Fukuyama, David E.; Carty, Olin C.; Daigle, Hugh D.; Yoon, Hongkyu Y.; Conley, Ethan C.

Using a combination of geospatial machine learning prediction and sediment thermodynamic/physical modeling, we have developed a novel software workflow to create probabilistic maps of geoacoustic and geomechanical sediment properties of the global seabed. This new technique for producing reliable estimates of seafloor properties can better support Naval operations relying on sonar performance and seabed strength, can constrain models of shallow tomographic structure important for nuclear treaty compliance monitoring/detection, and can provide constraints on the distribution and inventory of shallow methane gas and gas hydrate accumulations on the continental shelves.