Abstract For the South China Sea region, we use three types of gravity field signals(vertical deflection, gravity anomaly, and vertical gravity gradient anomaly) to train a convolutional neural network model, which is compared and analyzed with shipborne depth and foreign models. The three gravity signals are divided into four groups: gravity anomaly, gravity anomaly combined with vertical gravity gradient anomaly, gravity anomaly combined with vertical deflection, and gravity anomaly combined with vertical deflection and vertical gravity gradient anomaly. The standard deviations between the inversion results of four combinations and shipborne depths are 104.780 m, 102.778 m, 93.788 m, and 88.289 m, respectively, indicating that the accuracy of bathymetry prediction improves significantly with the increase of different types of gravity data. At depth greater than 2 000 m, the accuracy improvement of inversion results is more significant. By setting the proportion of training set to total dataset to 80%, 70%, 60% and 50%, respectively, the standard deviations between the inversion results and shipborne depths are 88.289 m, 91.256 m, 92.833 m and 96.022 m, respectively, indicating that the increase of data can effectively improve the accuracy of model learning results.
WANG Huaibing,WAN Xiaoyun,Richard Fiifi Annan. Seafloor Topography Prediction in the South China Sea Based on Convolutional Neural Network[J]. jgg, 2024, 44(3): 287-292.
WANG Huaibing,WAN Xiaoyun,Richard Fiifi Annan. Seafloor Topography Prediction in the South China Sea Based on Convolutional Neural Network[J]. jgg, 2024, 44(3): 287-292.
