Ambient Noise based Weakly Supervised Manhole Localization Methods over Deployed Fiber Networks

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Publication Date: 2/1/2023

Event: Optics Express

Reference: 31(6):9591-9607, 2023

Authors: Alexander Bukharin, NEC Laboratories America, Inc., Georgia Institute of Technology; Shaobo Han, NEC Laboratories America, Inc.; Yuheng Chen, NEC Laboratories America, Inc.; Ming-Fang Huang, NEC Laboratories America, Inc.; Yue-Kai Huang, NEC Laboratories America, Inc.; Yao Xie, Georgia Institute of Technology; Ting Wang, NEC Laboratories America, Inc.

Abstract: We present a manhole localization method based on distributed fiber optic sensing and weakly supervised machine learning techniques. For the first time to our knowledge, ambient environment data is used for underground cable mapping with the promise of enhancing operational efficiency and reducing field work. To effectively accommodate the weak informativeness of ambient data, a selective data sampling scheme and an attention-based deep multiple instance classification model are adopted, which only requires weakly annotated data. The proposed approach is validated on field data collected by a fiber sensing system over multiple existing fiber networks.

Publication Link: https://opg.optica.org/oe/fulltext.cfm?uri=oe-31-6-9591&id=527096