Submarine cable positioning using residual convolutional neural network based on magnetic features

Abstract

Accurate positioning is important for improving the efficiency of repairing submarine cables and reducing the related repairing cost. The magnetic anomaly produced by a submarine cable can be used to estimate its vertical and horizontal positions. A novel approach using magnetic data for estimating the position of submarine cables based on the 1-D residual convolutional neural network (RCNN) is investigated. Infinitely long ferromagnetic cylinder models with different parameters are used to generate datasets for the model training and testing. Tests on noisy synthetic datasets show that the developed 1-D RCNN method can capture detailed features related to the magnetic source position information, which are more accurate than the conventional Euler method in estimating the position of submarine cables. The developed 1-D RCNN method has also been successfully applied to processing field data. Furthermore, the processing workflow of our 1-D RCNN method is less noise sensitive compared with the conventional Euler method. The proposed 1-D RCNN method and its workflow open a new window for estimating the position of submarine cables using magnetic data.