畳み込みニューラルネットワークを用いてブリルアン光相関領域反射計の空間分解能を向上させる手法を提案した論文が IEEE Access に掲載されました。シンガポール国立大学からの短期留学生 Jelah Nieva Caceres さんが中心となって取り組んだ内容です。

J. N. Caceres, K. Noda, G. Zhu, H. Lee, K. Nakamura, and Y. Mizuno, “Spatial resolution enhancement of Brillouin optical correlation-domain reflectometry using convolutional neural network: proof of concept,” IEEE Access, vol. 9, pp. 124701-124710 (2021).

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.