Monitoring vessel movement above critical offshore infrastructure using distributed acoustic sensing

This study presents a methodology for detecting and classifying vessel movements using distributed acoustic sensing (DAS) by analyzing hydrodynamic effects, water displacement induced by bow and stern waves, and comparing these effects with acoustic emissions. Furthermore, the velocity of passing vessels is estimated using a frequency-wavenumber transformation. These velocities are then used to calculate the length of the vessel by multiplying the velocity by the time difference in arrival time between the bow and the stern wave as a vessel sails near a cable. This is done for multiple cargo vessels. The proposed method is validated through multiple deployments: on alternating current and direct current wind farm export cables going through the Wadden Sea and into the North Sea and in a controlled small-scale environment in the Port of Rotterdam, the Netherlands, and the Trondheimsfjord, Norway. The results demonstrate that DAS can effectively capture and classify the velocity of the ship, with consistent patterns observed in both the field and experimental settings, further highlighting the potential of using water displacement to estimate the speed and length of the vessel. Additionally, this study shows that by focusing on ultralow frequencies <1 Hz DAS data can be reduced in volume size, while still being able to capture vessel movements. This work highlights the potential of DAS systems for maritime monitoring and vessel classification, offering valuable applications in infrastructure protection and marine traffic analysis.