Bathymetry Prediction With SWOT Gravity Anomaly Using Machine Learning Methods: Paper 2—Model Evaluation and Uncertainty Analysis
TL;DR
A global features‐of‐interest map is presented that, if mapped by ship soundings, would yield the largest improvement of the global bathymetry, primarily located in regions with sparse multibeam coverage and associated with large gravity anomalies in the marine gravity field, indicating a potential presence of complex seafloor topography.
Abstract
Despite millions of ship soundings, bathymetry from satellite derived gravity is still necessary to fill in approximately three quarters of the global oceans. The methods used to carry out this inference have not changed significantly since the 1990s; however new methodology involving Machine Learning (ML) improves the bathymetric predictions considerably. Here we utilize five independent ML models from a workshop at the Technical University of Denmark (DTU). We highlight the benefits achieved by either (a) inclusion of a new highly‐accurate gravity field from the Surface Water and Ocean Topography (SWOT) satellite (∼22% improvement in spatial resolution), or (b) highly flexible ML methods capable of inferring bathymetric regimes not previously possible. By taking advantage of these five independent models we can determine regions of high confidence in our bathymetric inversion as well as regions with challenging conditions. Building on model prediction confidence and features revealed from the dense gravity field obtained by SWOT, we present a global features‐of‐interest map that, if mapped by ship soundings, would yield the largest improvement of the global bathymetry. These features are primarily located in regions with sparse multibeam coverage and associated with large gravity anomalies in the marine gravity field, indicating a potential presence of complex seafloor topography.