Surfzone Water Depth Estimation From Surface Flows Using a Data‐Driven and Physics‐Informed Deep Conditional Generative Model

Abstract A physics‐informed deep conditional generative model driven with remotely sensed surface currents is shown to estimate surfzone water depths (bathymetry). The model encodes measured flow data as latent Gaussian parameters and decodes these distributions to estimate water depths over the domain, progressively refining its predictions via a loss‐minimization strategy. The model performance is evaluated on in‐distribution and out‐of‐distribution data sets collected in Duck, North Carolina, demonstrating promising site‐specific results, especially given the limited training data set used here (6 bathymetries with a total of 8 flow realizations). However, broader applicability requires transfer learning across a wider range of bathymetric observations.