A Distributed Acoustic Sensing Dataset for Vessel Detection and Localization in Submarine Cable Protection

2026-07-30

AI Summary

This study addresses the frequent damage to subsea communication and power cables caused by vessel activities, highlighting the urgent need for effective monitoring solutions. To this end, the authors construct and release the Marlinks-NS dataset, which comprises ten days of continuous acoustic signals recorded via distributed acoustic sensing (DAS) over a 2,554-meter segment of a 28-kilometer buried fiber-optic cable in the North Sea. Integrated with Automatic Identification System (AIS) vessel data, the dataset supports two benchmark tasks: vessel detection and vessel-to-cable distance estimation. It includes 74,771 annotated samples across 250 sensing channels, representing the first large-scale, real-world marine DAS dataset with ground-truth labels. Provided in HDF5 format with spectral energy features, anonymized distance labels, and example code, Marlinks-NS enables reproducible research and lays a foundational data resource for intelligent cable protection systems.

Abstract

Recent incidents of accidental damage and suspected sabotage to submarine telecommunication and power cables, particularly in the Baltic Sea, have underscored their vulnerability and the need for continuous monitoring solutions. Distributed acoustic sensing (DAS) applied to submarine optical-fiber cables enables wide-area monitoring of underwater acoustic activity. We present the Marlinks-NS DAS dataset, comprising processed submarine DAS measurements and AIS-derived vessel information curated for cable-protection research. The dataset defines two machine-learning tasks (vessel detection and vessel-to-cable distance estimation) allowing reproducible research under realistic marine conditions. The dataset contains 74,771 labeled data instances from ten days of continuous recording along a 2,554 m segment in a 28 km buried fiber-optic cable in the North Sea. Each instance includes spectral-energy features from 250 sensing channels, together with anonymized distance measurements and metadata from AIS information. The released HDF5 data, documentation, processing description, and example code support reproducible development and evaluation of DAS-based vessel-monitoring methods for submarine cable protection.

Problem

• submarine cable protection
• vessel detection
• vessel localization
• distributed acoustic sensing
• underwater monitoring

Innovation

• Distributed Acoustic Sensing
• Submarine Cable Protection
• Vessel Detection
• Machine Learning Dataset
• AIS Integration