Automatic segmentation of gas plumes from multibeam water column images using a U-shape network

Cold seeps are widely developed on the seabed of continental margins and can form gas plumes due to the upward migration of methane-rich fluids. The detection and automatic segmentation of gas plumes are of great significance in locating and studying the cold seep system that is usually accompanied by hydrate layers in the subsurface. A multibeam echo-sounder system (MBES) can record the complete backscatter intensity of the water column, and it is one of the most effective means for detecting cold seeps. However, the gas plumes recorded in multibeam water column images (WCI) are usually blurred due to the interference of the complicated water environment and the sidelobes of the MBES, making it difficult to obtain the effective segmentation. Therefore, based on the existing UNet semantic segmentation network, this paper proposes an AP-UNet network combining the convolutional block attention module and the pyramid pooling module for the automatic segmentation and extraction of gas plumes. Comparative experiments are conducted among three traditional segmentation methods and two deep learning methods. The results show that the AP-UNet segmentation model can effectively suppress complicated water column noise interference. The segmentation precision, the Dice coefficient, and the recall rate of this model are 92.09%, 92.00%, and 92.49%, respectively, which are 1.17%, 2.10%, and 2.07% higher than the results of the UNet.