Monitoring vessel movement above critical offshore infrastructure using distributed acoustic sensing

Area of Science:

• Marine engineering
• Oceanography
• Acoustics

Background:

• Maritime surveillance relies on accurate vessel detection and classification.
• Distributed acoustic sensing (DAS) offers a novel approach to monitoring underwater environments.
• Existing methods may have limitations in real-time vessel characterization.

Purpose of the Study:

• To develop and validate a DAS-based methodology for detecting, classifying, and estimating the speed and length of vessels.
• To explore the use of hydrodynamic effects and acoustic emissions for vessel identification.
• To assess the feasibility of using ultralow frequency DAS data for efficient maritime monitoring.

Main Methods:

• Utilizing distributed acoustic sensing (DAS) to analyze hydrodynamic effects, including bow and stern waves.
• Applying frequency-wavenumber transformation to estimate vessel velocity.
• Calculating vessel length based on velocity and wave arrival time differences.
• Validating the method across diverse field deployments (wind farm export cables) and controlled environments (Port of Rotterdam, Trondheimsfjord).

Main Results:

• DAS effectively captures and classifies vessel velocity, showing consistent patterns in both field and experimental settings.
• Hydrodynamic water displacement is a viable indicator for estimating vessel speed and length.
• Focusing on ultralow frequencies (<1 Hz) significantly reduces DAS data volume while retaining crucial vessel movement information.

Conclusions:

• The proposed DAS methodology provides a robust and accurate system for maritime monitoring and vessel classification.
• This technique has significant potential for applications in infrastructure protection (e.g., subsea cables) and marine traffic analysis.
• Optimizing DAS data processing, particularly at ultralow frequencies, enhances its practical applicability for large-scale maritime surveillance.