Predicting intermediate wavelength depth using a deep neural network combined with gravity data in the Sea of Japan

Abstract

Influenced by various geophysical factors, gravity and bathymetry exhibit significant correlations only at certain intermediate wavelengths. In this study, a deep neural network (DNN) combined with multi-source gravity data was used to recovery the intermediate wavelength (15–160 km) depth in the Sea of Japan (37–42°N, 130–138°E), to analyze the contributions of different gravity data to the bathymetric model. Subsequently, the gravity anomaly (GA), vertical gravity gradient (VGG), and vertical deflection were used as training data with a suitable DNN framework and designed training strategy. Evaluation results show that: the precision of the final DNN training models at test points surpasses that of the topo_25.1 model by 13 to 14 m; the TC1 model, with GA, VGG, and vertical deflection, is optimal, and the accuracy is 7.2 m higher than that of the (gravity geology method) GGM model on check cruises. The three types of gravity data can complement and constrain each other. In addition, predicting depths at intermediate wavelengths with significant coherence, as opposed to directly predicting absolute depths, results in higher accuracy by 20–30 m at test points, and an improvement of approximately 10 m in check accuracy.

Keywords

• Seafloor topography
• gravity data
• deep neural network
• Sea of Japan
• intermediate wavelength
• shipborne bathymetry