Predicting bathymetry using multisource differential marine geodetic data with multilayer perceptron neural network

We propose a method for enhancing the accuracy of bathymetry models based on a multilayer perceptron (MLP) neural network that integrates the differences in multisource marine geodetic data (MMGD) (longitude, latitude, reference bathymetry, slope, the meridional and prime components of vertical deflection, gravity anomaly, vertical gravity gradient, and mean dynamic topography). First, we use the MMGD differences between the shipborne sounding control points within 8′ × 8′ grid points and shipborne sounding control points as input data, as well as the differences between the topo_24.1 model and the measured bathymetric values at the control points as output data to train the MLP model. Second, we feed the input data from the central point of a 1′ × 1′ grid into the MLP model to obtain predictions, and then use the topo_24.1 model to recover the predicted bathymetry at the prediction point. We focus on the Caribbean Sea, and construct a Caribbean Bathymetric Chart of the Oceans (CBCO1) model using MLP neural network. The reliability of MMGD, a CBCO2 model using MMGD, and the reliability and effectiveness of the overall method are demonstrated through comparisons with the CBCO2, GEBCO_2022, topo_24.1, DTU18 models at the checkpoints.