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

被引：10

作者：

Caceres, Jelah N. ^{[1
,2
]}

Noda, Kohei ^{[1
,3
]}

Zhu, Guangtao ^{[3
]}

Lee, Heeyoung ^{[4
]}

Nakamura, Kentaro ^{[1
]}

Mizuno, Yosuke ^{[3
]}

机构：

[1] Tokyo Inst Technol, Inst Innovat Res, Midori Ku, Yokohama, Kanagawa 2268503, Japan

[2] Natl Univ Singapore, Dept Phys, Singapore 119077, Singapore

[3] Yokohama Natl Univ, Fac Engn, Hodogaya Ku, Yokohama, Kanagawa 2408501, Japan

[4] Shibaura Inst Technol, Coll Engn, Koto Ku, Tokyo 1358548, Japan

来源：

IEEE ACCESS | 2021年 / 9卷

基金：

日本学术振兴会;

关键词：

Spatial resolution; Convolution; Training; Temperature measurement; Strain; Feature extraction; Optical fibers; Brillouin scattering; convolutional neural network; distributed strain sensing; optical fiber sensing; spatial resolution; MEASUREMENT RANGE ENLARGEMENT; STRAIN-MEASUREMENT; TEMPERATURE; PROPOSAL; BOTDA; PREDICTION; ACCURACY; ANALYZER;

D O I：

10.1109/ACCESS.2021.3110874

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：124701 / 124710

页数：10

共 58 条

[1] Convolutional Neural Networks for Speech Recognition [J].