Seafloor topography inversion from multi-source marine gravity data using multi-channel convolutional neural network

Abstract

Seafloor topography is extremely important for marine scientific surveys and research. Current physical methods have difficulties in integrating multi-source marine gravity data and recovering non-linear features. To overcome this challenge, a multi-channel convolutional neural network (MCCNN) is employed to establish the seafloor topography model. Firstly, the MCCNN model is trained using the input data from the 64 × 64 grid points centered around the control points. The input data includes the differences in position between calculation points and surrounding grid points, gravity anomaly, vertical gravity gradient, east component of deflection of the vertical and north component of deflection of the vertical, as well as the reference terrain information. Then, the data from the 64 × 64 grid points centered around the predicted points is inputted into the trained MCCNN model to obtain the predicted depth at those points. Finally, the predicted depth is utilized to establish the seafloor topography model of the study area. This method is tested in a local area located in the southern part of the Emperor Seamount Chain in the Northwest Pacific (31°N −37°N, 169°E −175°E). The root mean square of the differences between the resultant seafloor topography model and ship-borne bathymetric values at the check points is 88.48 m. This performance is commendable compared to existing models.