{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T14:25:37Z","timestamp":1781533537715,"version":"3.54.5"},"reference-count":32,"publisher":"Optica Publishing Group","issue":"6","license":[{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/doi.org\/10.1364\/OA_License_v2#VOR"},{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/opg.optica.org\/policies\/opg-tdm-policy.json"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62271078"],"award-info":[{"award-number":["62271078"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"},{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":["opg.optica.org"],"crossmark-restriction":false},"short-container-title":["J. Opt. Commun. Netw."],"published-print":{"date-parts":[[2026,6,1]]},"abstract":"<jats:p>Optical fibers cannot actively perceive or report external risk events during transmission, which increases the threat posed by uncertain-condition multi-source (UC-MS) risk events to the reliability of intelligent optical networks. Distributed acoustic sensor technology has emerged as a key solution for real-time active monitoring of optical fiber risk events over long distances. UC-MS risk events, characterized by location, temporal, and category uncertainties, lead to signal mixing and masking among events, thereby severely degrading the event identification performance of distributed optical fiber sensing systems. To address this problem, this paper proposes a multi-level noise suppression algorithm (MNSA). It segments risk event fragments while effectively suppressing environmental and system noise, thereby eliminating location uncertainty. In addition, a separated multi-branch deep neural network (SMB-DNN) is designed to extract features of single-source and multi-source risk events with uncertain temporal overlap and uncertain category information within the segmented event fragments. Finally, after event segmentation, five types of event scenarios were rigorously tested and validated through field experiments. Results show that multi-LS achieved 95.21% accuracy in single-source event detection and 89.09% accuracy in multi-source event identification, outperforming existing methods significantly.<\/jats:p>","DOI":"10.1364\/jocn.584943","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T11:00:14Z","timestamp":1775559614000},"page":"B129","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-source fiber event identification under uncertain conditions in DAS-enabled intelligent optical networks"],"prefix":"10.1364","volume":"18","author":[{"given":"Wei","family":"Chen","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04w9fbh59","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4293-9107","authenticated-orcid":true,"given":"Zhiqun","family":"Gu","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04w9fbh59","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiajun","family":"Jiang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04w9fbh59","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1377-7938","authenticated-orcid":true,"given":"Jiawei","family":"Zhang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04w9fbh59","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6618-272X","authenticated-orcid":true,"given":"Yuefeng","family":"Ji","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04w9fbh59","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Ding","sequence":"additional","affiliation":[{"name":"China Telecom Research Institute"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2026,5,13]]},"reference":[{"key":"jocn-18-6-B129-R1","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1145\/3651890.3672233","article-title":"RDMA over Ethernet for distributed training at meta scale","volume-title":"Proceedings of the ACM SIGCOMM 2024 Conference (ACM SIGCOMM\u201924)","volume":"14","author":"Gangidi","year":"2024"},{"key":"jocn-18-6-B129-R2","doi-asserted-by":"publisher","first-page":"42850","DOI":"10.1364\/OE.506212","type":"journal-article","volume":"31","author":"Li","year":"2023","journal-title":"Opt. 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All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.","name":"copyright","label":"Copyright"}]}}