• Hamzah, A. E.
• Ibrahim, M. F.
• Arsad, N.
• Tanaka, Y.
• Almoosa, A. S. K.
• Zan, M. S. D.

Abstract

<jats:title>Abstract</jats:title><jats:p>To improve the Brillouin frequency shift (BFS) resolution measurement and processing time of the differential cross-spectrum Brillouin optical time domain reflectometry (DCS-BOTDR) fiber sensor, our team suggests employing the ensemble machine learning (EML) technique. Because it gave the best BFS resolution compared to the other T<jats:sub>L</jats:sub> cases, we used the BFS distribution data recorded by the pulse duration T<jats:sub>L</jats:sub> =14 ns case as ground truth to train the EML model in this work. After that, we tested the EML model for T<jats:sub>L</jats:sub> =4, 60, and 90 ns cases. We improved the BFS resolution for all T<jats:sub>L</jats:sub> situations by approximately 2.85 MHz, comparable to our resolution when T<jats:sub>L</jats:sub> was equal to 14 ns. This result demonstrates that the EML algorithm is reliable, efficient, and highly accurate in its predictive capabilities. Additionally, we have documented a rapid processing time of approximately one second. In addition, we have successfully demonstrated 20 cm spatial resolution measurement for T<jats:sub>L</jats:sub> =60 and 90 ns, which was not previously possible with the usual DCS-BOTDR signal processing method.</jats:p>

Topics

• impedance spectroscopy
• extraction
• machine learning
• reflectometry