The University of Southampton
University of Southampton Institutional Repository

Low-noise distributed acoustic sensing using enhanced backscattering fiber with ultra-low-loss point reflectors

Low-noise distributed acoustic sensing using enhanced backscattering fiber with ultra-low-loss point reflectors
Low-noise distributed acoustic sensing using enhanced backscattering fiber with ultra-low-loss point reflectors
We present a low-noise distributed acoustic sensor using enhanced backscattering fiber with a series of localized reflectors. The point reflectors were inscribed in a standard telecom fiber in a fully automated system by focusing an ultra-fast laser through the fiber cladding. The inscribed reflectors provided a reflectance of −53 dB, significantly higher than the Rayleigh backscattering level of −70 dB/m, despite adding only 0.01 dB of loss per 100 reflection points. We constructed a coherent φ-OTDR system using a double-pulse architecture to probe the enhanced backscattering fiber. Using this system, we found that the point reflectors enabled an average phase noise of −91 dB (re rad2/Hz), 20 dB lower than sensors formed using Rayleigh backscattering in the same fiber. The sensors are immune to interference fading, exhibit a high degree of linearity, and demonstrate excellent non-local signal suppression (>50 dB). This work illustrates the potential for low-cost enhanced backscattering fiber to enable low-noise, long-range distributed acoustic sensing.
1094-4087
14638-14647
Redding, Brandon
c91fca68-1448-4269-9012-836f2650f083
Murray, Matthew
5a885908-7298-4af8-ae89-c40b72716e3c
Donko, Andrei
3786c6f9-efb1-4d38-8366-9df4e9bd4033
Beresna, Martynas
a6dc062e-93c6-46a5-aeb3-8de332cdec7b
Masoudi, Ali
8073fb9b-2e6c-46c9-89cf-cb8670d76dc0
Brambilla, Gilberto
815d9712-62c7-47d1-8860-9451a363a6c8
Redding, Brandon
c91fca68-1448-4269-9012-836f2650f083
Murray, Matthew
5a885908-7298-4af8-ae89-c40b72716e3c
Donko, Andrei
3786c6f9-efb1-4d38-8366-9df4e9bd4033
Beresna, Martynas
a6dc062e-93c6-46a5-aeb3-8de332cdec7b
Masoudi, Ali
8073fb9b-2e6c-46c9-89cf-cb8670d76dc0
Brambilla, Gilberto
815d9712-62c7-47d1-8860-9451a363a6c8

Redding, Brandon, Murray, Matthew, Donko, Andrei, Beresna, Martynas, Masoudi, Ali and Brambilla, Gilberto (2020) Low-noise distributed acoustic sensing using enhanced backscattering fiber with ultra-low-loss point reflectors. Optics Express, 28 (10), 14638-14647. (doi:10.1364/OE.389212).

Record type: Article

Abstract

We present a low-noise distributed acoustic sensor using enhanced backscattering fiber with a series of localized reflectors. The point reflectors were inscribed in a standard telecom fiber in a fully automated system by focusing an ultra-fast laser through the fiber cladding. The inscribed reflectors provided a reflectance of −53 dB, significantly higher than the Rayleigh backscattering level of −70 dB/m, despite adding only 0.01 dB of loss per 100 reflection points. We constructed a coherent φ-OTDR system using a double-pulse architecture to probe the enhanced backscattering fiber. Using this system, we found that the point reflectors enabled an average phase noise of −91 dB (re rad2/Hz), 20 dB lower than sensors formed using Rayleigh backscattering in the same fiber. The sensors are immune to interference fading, exhibit a high degree of linearity, and demonstrate excellent non-local signal suppression (>50 dB). This work illustrates the potential for low-cost enhanced backscattering fiber to enable low-noise, long-range distributed acoustic sensing.

Text
oe-28-10-14638 - Version of Record
Available under License Creative Commons Attribution.
Download (5MB)

More information

Accepted/In Press date: 9 April 0202
Published date: 29 April 2020

Identifiers

Local EPrints ID: 453517
URI: http://eprints.soton.ac.uk/id/eprint/453517
ISSN: 1094-4087
PURE UUID: 05339a23-dfa2-447d-8229-f66b5e4a0315
ORCID for Andrei Donko: ORCID iD orcid.org/0000-0003-1884-743X
ORCID for Ali Masoudi: ORCID iD orcid.org/0000-0003-0001-6080
ORCID for Gilberto Brambilla: ORCID iD orcid.org/0000-0002-5730-0499

Catalogue record

Date deposited: 18 Jan 2022 18:08
Last modified: 17 Mar 2024 03:25

Export record

Altmetrics

Contributors

Author: Brandon Redding
Author: Matthew Murray
Author: Andrei Donko ORCID iD
Author: Martynas Beresna
Author: Ali Masoudi ORCID iD
Author: Gilberto Brambilla ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×