Demonstration of the ACE (Arctic Coastal Erosion) model at Drew Point, AK during a permafrost bluff block collapse event in summer 2018
Abstract not provided.
Publications
Forecasting Marine Sediment Properties with Geospatial Machine Learning
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.
Methane Gas Prediction in Arctic Sediments along Alaska's North Slope
Abstract not provided.
Probabilistic Predictions of Gas Hydrate Formation near Blake Ridge using Dakota and PFLOTRAN
Abstract not provided.
Initial Validation Results for the Arctic Coastal Erosion (ACE) Model
Abstract not provided.
Probabilistic Predictions of Offshore Gas Hydrate and Submarine Permafrost Distribution Along the Alaskan North Slope
Abstract not provided.
Initial Validation Results for the Arctic Coastal Erosion (ACE) Model
Abstract not provided.
Probabilistic Predictions of Gas Hydrate Formation in Blake Ridge using Dakota and PFLOTRAN
Abstract not provided.
Probabilistic Predictions of Methane Hydrate using GPSM and PFLOTRAN
Abstract not provided.
A hybrid machine learning and reservoir simulation approach to modeling gas hydrate systems using open-source software
Abstract not provided.
A Probabilistic Modeling Framework for Forecasting Gas Hydrate Using Geospatial Machine Learning
Abstract not provided.
11 Results