AI-enabled detection of vessels in distributed acoustic sensing (DAS) data using submarine fiber-optic cables

Europe's Critical Underwater Infrastructure (CUI) is both economically vital and vulnerable to sabotage. Recent incidents in the Baltic Sea suggest a deliberate strategy to exploit these vulnerabilities, by dragging anchors across seabed cables. The vast spatial extent of CUI makes such attacks difficult to detect and deter. Distributed Acoustic Sensing (DAS) enables continuous monitoring by detecting vibrations along submarine fiber-optic cables, offering maritime situational awareness. These vibrations are caused by surface vessels, submarines, or unmanned underwater vehicles. To enable real-time monitoring, we combined publicly available DAS and AIS datasets to label ship signals in the DAS data. Using this dataset, we trained a convolutional neural network (CNN) to detect vessels in large volumes of DAS data. We evaluated the CNN using two datasets, two data processing techniques, and augmentation strategies. Our CNN effectively distinguishes weak vessel signals from substantial background noise, demonstrating its potential for real-time maritime threat detection. Based on our findings, we offer recommendations for applying DAS-based monitoring to protect CUI in the North Sea.