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Related Experiment Video

Updated: Jul 7, 2026

Implementation of a Reference Interferometer for Nanodetection

Implementation of a Reference Interferometer for Nanodetection

Published on: April 26, 2014

Fiber-Optic Telecommunication Network Wells Monitoring by Phase-Sensitive Optical Time-Domain Reflectometer with

Andrey A Zhirnov¹, German Y Chesnokov², Konstantin V Stepanov¹

¹Bauman Moscow State Technical University, 2-nd Baumanskaya 5-1, 105005 Moscow, Russia.

Sensors (Basel, Switzerland)

|July 11, 2023

Summary

Phase-sensitive optical time-domain reflectometry (phi-OTDR) effectively monitors urban telecommunication wells. Convolutional neural networks achieved 98.55% accuracy in classifying events within this infrastructure.

Keywords:

acoustic monitoring distributed fiber optic sensor fiber optic sensor machine learning phi-OTDR telecommunication well

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Last Updated: Jul 7, 2026

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Area of Science:

Engineering
Computer Science
Telecommunications

Background:

Urban infrastructure, particularly telecommunication well networks, requires robust monitoring solutions.
Traditional monitoring methods face challenges with complex, branched network structures.

Purpose of the Study:

To apply phase-sensitive optical time-domain reflectometry (phi-OTDR) for monitoring branched urban telecommunication well networks.
To evaluate machine learning algorithms for classifying events detected by phi-OTDR.

Main Methods:

Deployment of a phi-OTDR system for data acquisition in urban telecommunication well networks.
Application and evaluation of various machine learning algorithms for event classification.
Utilizing experimental data to calculate numerical performance metrics for the algorithms.

Main Results:

Convolutional neural networks (CNNs) demonstrated superior performance in event classification.
CNNs achieved a high probability of correct classification, reaching 98.55%.
The study details the tasks, difficulties, and potential applications of phi-OTDR in this context.

Conclusions:

phi-OTDR is a viable technology for monitoring complex urban telecommunication infrastructure.
Machine learning, particularly CNNs, significantly enhances the accuracy of event detection and classification in such systems.
The findings support the integration of advanced sensing and AI for intelligent infrastructure management.