Spatial Resolution Enhancement of Brillouin Optical Correlation-Domain Reflectometry Using Convolutional Neural Network: Proof of Concept

Jelah N. Caceres, Kohei Noda, Guangtao Zhu, Heeyoung Lee, Kentaro Nakamura, Yosuke Mizuno

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Brillouin optical correlation-domain reflectometry (BOCDR) is a fiber-optic distributed sensing technique with single-end accessibility and high spatial resolution. In BOCDR, the measured Brillouin gain spectrum (BGS) distribution is generally given by a convolution of the intrinsic BGS distribution and the beat-power spectrum. In most conventional implementations, the Brillouin frequency shift (BFS) distribution is directly obtained using the measured BGS distribution. Determining the BFS distribution on the basis of the intrinsic BGS distribution will give potentially higher spatial resolution, which can be achieved by deconvolution of the measured BGS distribution. In this work, we employ a convolutional neural network to perform this deconvolution processing in BOCDR and show its potential for spatial resolution enhancement. A spatial resolution which is 5 times higher than the nominal value is demonstrated.

Original languageEnglish
Pages (from-to)124701-124710
Number of pages10
JournalIEEE Access
Volume9
DOIs
Publication statusPublished - 2021

Keywords

  • Brillouin scattering
  • convolutional neural network
  • distributed strain sensing
  • optical fiber sensing
  • spatial resolution

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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