Bathymetric Prediction Using Multisource Gravity Data Derived From a Parallel Linked BP Neural Network

Abstract Gravity anomalies (GAs) and vertical gravity gradient anomalies (VGGs) are regularly used to predict bathymetry. However, few studies have explored the combination of the GAs and VGGs to predict bathymetry. We introduce the back propagation (BP) neural network into bathymetric prediction field and propose a method to predict depth from GAs and VGGs. The method was tested in the Mariana Trench region, and a neural network bathymetry model was constructed using feature data obtained from GAs and VGGs as the input of the BP neural network. Additionally, single‐beam sounding data were used as label data. By comparing the neural network and the gravity‐geologic method (GGM), the neural network was found to provide better performance with an accuracy improvement of 19%. The root‐mean‐square of the absolute difference between the neural network bathymetry model and the single‐beam sounding data was 72.40 m, with a relative accuracy of 1.71%. Approximately 50% of the differences were distributed within ±20 m, and 90% were distributed within ±100 m. The neural network bathymetry model was also compared with the GGM bathymetry model for different depths and topographies, and the results verified the feasibility and effectiveness of the BP neural network method.