[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83595-en":3,"doc-seo-83595-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83595,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Fully Unsupervised Detection of Physical Contacts on Subsea Cables via State-of-Polarization Monitoring","Presents a fully unsupervised Fast-Slow DSVDD detector for continuous State-of-Polarization (SoP) monitoring on a deployed subsea cable. Without using event labels, the method ranks all five confirmed trawler contacts within the top 13 among 122,174 one-minute recordings and surfaces additional corroborated cable-contact events beyond the originally logged set. Post-hoc review cross-checks detections using DAS and AIS records where transponders were disabled during cable crossing.","Fully Unsupervised Detection of Physical Contacts on Subsea Cables via State-of-Polarization Monitoring  \nAgastya Raj(1) , Alvaro Doval(2) , Tian Tian(1) , Steinar Bjørnstad(2) , Marco Ruffini(1) ,  \n(1) School of Computer Science and Statistics, IRIS Research Group, ADAPT Research Centre, Trinity College Dublin, [Agastya.Raj@tcd.ie](Agastya.Raj@tcd.ie) (2) Tampnet AS, Stavanger, Norway.  \nAbstract We present a fully unsupervised Fast-Slow DSVDD detector for continuous State-of-Polarization monitoring on a deployed subsea cable. Trained without event labels, it ranks all five confirmed trawler contacts within the top 13 of 122,174 recordings and surfaces additional corroborated cable-contact events. ©2026 The Author(s)  \narXiv :2607 .0 1484v 1 [ cs .NI] 1 Jul 2026  \n1  \nIntroduction  \nSubsea fibre-optic cables carry over 97% of intercontinental data traffic, and trawler fishing and anchor dragging are leading causes of cable damage, with dragged-anchor incidents alone accounting for 30–40% of offshore cable faults [1] . Distributed Acoustic Sensing (DAS) and State of Polarization (SoP) fibre sensing technologies mitigate these risks by monitoring vibrations and physical disturbances along the cable [2] . DAS localises approaching trawlers [3], but suffers from saturation effects and limited dynamic range for strong signals such as direct impacts [4] . SoP monitoring provides a complementary approach [5, 6]: it can be extracted directly from existing coherent receivers at no marginal hardware cost, does not saturate during strong motions, is compatible withinline amplifiers [7], and produces unique signatures for distinct physical impacts [8] .  \nA recent long-term field trial on the Lowestoft–Lista subsea cable established that SoP responds to real trawler contacts, anchor drags, and environmental forces over multi-month observation periods [9] . However, this was performed manually by visual correlation of SoP waveforms, and does not scale to network-wide real-time monitoring. Furthermore, SoP monitoring through live transmission systems is impacted by environmental and equipment noise, within which physical contacts are rare and subtle. This motivates a Machine Learning (ML) approach over classical thresholding. However, the absence of labelled event data from real field trials has largely confined existing ML-based SoP detection to controlled settings: Supervised classifiers trained on labelled examples achieve high accuracy on short sequences with predefined event types [10–13], and semisupervised methods relax this to one-class fits on  \n1This paper is a preprint of a paper accepted in ECOC 2026 and is subject to Institution of Engineering and Technology Copyright. A copy of record will be available at IET Digital Library  \nFig. 1: Experimental Setup labelled baselines [14], but both evaluate on controlled testbed scenarios rather than continuous deployed data. Automated detection on deployed cables has been demonstrated in terrestrial settings [7, 15, 16] but relies on supervised training.  \nIn this work, we transition from manual and supervised approaches to fully unsupervised event detection on continuous long-duration SoP data from a deployed subsea cable. We use 92 days of continuous SoP recordings from the Lowestoft–Lista cable (Tampnet, North Sea)-the same system used in our previous field trial [9], comprising 122,174 one-minute recordings at 44.1 kHz. We evaluate a deep one-class detection model under unsupervised conditions: no trawler labels are used at any stage of training, model selection, or hyperparameter tuning. The model ranks all ground-truth confirmed trawler contacts within the top 13 of the full 122,174 archive, where lower ranks indicate recordings judged more anomalous by the detector. Beyond the 5 logged events, the framework produces additional recordings not previously identified during manual reviews of SoP data. Post-hoc review against the DAS and  \nAutomatic Identification System (AIS) record","cbCaikspE4v99jiy","https://ap.wps.com/l/cbCaikspE4v99jiy","pdf",1398198,3,1,5,"English","en",105,"# Introduction\n# Field Trial and Data Pipeline\n# Detection Framework\n# Evaluation Results\n# Post-hoc Validation","[{\"question\":\"What problem does the paper address in subsea cable monitoring?\",\"answer\":\"It targets automated identification of physical contacts on deployed subsea cables, such as trawler contacts, where manual waveform correlation does not scale and events are rare and subtle in live transmission conditions.\"},{\"question\":\"How does the proposed method avoid needing labeled event data?\",\"answer\":\"It trains a deep one-class Fast-Slow DSVDD model under fully unsupervised conditions, using continuous SoP recordings without any trawler labels for training, model selection, or tuning.\"},{\"question\":\"What evaluation outcome demonstrates effectiveness on continuous deployed data?\",\"answer\":\"Across a 92-day dataset of 122,174 one-minute recordings, the detector ranks all five ground-truth confirmed trawler contacts within the top 13, and it additionally produces other corroborated cable-contact events not previously identified during manual review.\"}]",1784189084,13,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"fully-unsupervised-detection-of-physical-contacts-on-subsea-cables-via-state-of-polarization-monitoring","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/fully-unsupervised-detection-of-physical-contacts-on-subsea-cables-via-state-of-polarization-monitoring/83595/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-22","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address in subsea cable monitoring?","Question",{"text":75,"@type":76},"It targets automated identification of physical contacts on deployed subsea cables, such as trawler contacts, where manual waveform correlation does not scale and events are rare and subtle in live transmission conditions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method avoid needing labeled event data?",{"text":80,"@type":76},"It trains a deep one-class Fast-Slow DSVDD model under fully unsupervised conditions, using continuous SoP recordings without any trawler labels for training, model selection, or tuning.",{"name":82,"@type":73,"acceptedAnswer":83},"What evaluation outcome demonstrates effectiveness on continuous deployed data?",{"text":84,"@type":76},"Across a 92-day dataset of 122,174 one-minute recordings, the detector ranks all five ground-truth confirmed trawler contacts within the top 13, and it additionally produces other corroborated cable-contact events not previously identified during manual review.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & 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