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This article focuses on a specialized machine learning dataset designed for vessel detection using Distributed Acoustic Sensing (DAS) to protect submarine cables. By providing labeled acoustic data paired with AIS information, it enables reproducible research into monitoring underwater threats without relying solely on traditional optical fiber sensing methods. The work bridges geophysics and computer science by establishing benchmarks specifically tailored for the AI-driven analysis of complex marine environments.
A Distributed Acoustic Sensing Dataset for Vessel Detection and Localization in Submarine Cable Protection
arXiv:2607.28306v1 Announce Type: cross 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.
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.
19 pages, 8 figures, 3 tables. Submitted to be considered for publication as a Data Descriptor in the Scientific Data Journal
Geophysics (physics.geo-ph); Machine Learning (cs.LG)
arXiv:2607.28306 [physics.geo-ph]
(or arXiv:2607.28306v1 [physics.geo-ph] for this version)
https://doi.org/10.48550/arXiv.2607.28306 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 272 words
