Global Predicted Bathymetry Using Neural Networks

Abstract

Abstract A coherent portrayal of global bathymetry requires that depths are inferred between sparsely distributed direct depth measurements. Depths can be interpolated in the gaps using alternate information such as satellite‐derived gravity and a mapping from gravity to depth. We designed and trained a neural network on a collection of 50 million depth soundings to predict bathymetry globally using gravity anomalies. We find the best result is achieved by pre‐filtering depth and gravity in accordance with isostatic admittance theory described in previous predicted depth studies. When training the model, if the training and testing split is a random partition at the same resolution as the data, the training and testing sets will not be independent, and model misfit is underestimated. We solve this problem by partitioning the training and testing set with geographic bins. Our final predicted depth model improves on old predicted depth model RMSE by 16%, from 165 to 138 m. Among constrained grid cells, 80% of the predicted values are within 128 m of the true value. Improvements to this model will continue with additional depth measurements, but predictions at higher spatial resolution, being limited by upward continuation of gravity, should not be attempted with this method.