A 2D Fully Convolutional Neural Network for Nearshore And Surf-Zone Bathymetry Inversion from Synthetic Imagery of Surf-Zone using the Model Celeris

Bathymetry has a first order impact on nearshore and surfzone hydrodynamics. Typical survey techniques are expensive and time-consuming, require specialized equipment, and are not feasible in a variety of situations (e.g. limited manpower and/or site access). However, the emergence of nearshore remote sensing platforms (e.g. Unmanned Aircraft Systems (UAS), towers, and satellites) from which high-resolution imagery of the sea-surface can be collected at frequent intervals, has created the potential for accurate bathymetric estimation from wave-inversion techniques without in-situ measurements. While a variety of physics-based algorithms have been applied to nearshore and surfzone bathymetric inversion problems, the commonly used approaches do not account for non-linear hydrodynamics that are prevalent during breaking waves. Models for estimating non-linear wave dynamics are slow and often require large amounts of computational power which make them unfeasible for rapid estimations of depth. Fully convolutional neural networks (FCNs) are a branch of artificial intelligence algorithms that have proven effective at computer vision tasks in semantic segmentation and regression problems. In this work, we consider the use of FCNs for inferring bathymetry from video-derived imagery. The FCN model presented shows the feasibility of using an AI system to perform bathymetric inversion on time-averaged images (timex) of realistic-looking, synthetically generated surfzone imagery from the hydrodynamic wave model Celeris (Tavakkol and Lynett 2017). Ongoing work includes extending the FCN to incorporate synthetic video frames as input as well as testing with actual tower and satellite imagery.