Robust and Fast Temperature Extraction for Brillouin Optical Time-Domain Analyzer by Using Denoising Autoencoder-Based Deep Neural Networks

Abstract

A method of robust and fast temperature extraction for Brillouin optical time-domain analyzer (BOTDA) sensing systems using the denoising autoencoder (DAE) based deep neural networks (DNN) is demonstrated. After appropriate training, the DAE suppresses the noise on the measured Brillouin gain spectrum (BGS), and improves the signal-to-noise ratio (SNR) by 9.96dB in our experiment. To extract temperature, the DAE as a basic block is stacked to form the DNN model. Since the DNN model is based on DAE, both denoising and fast temperature extraction can be simultaneously finished using only one DNN model. Thus, it is more robust and faster than the conventional Lorentzian curve fitting (LCF) method, especially when the input data has low SNR level. In the case of 4.6dB SNR, the standard deviation (SD) of the measured temperature at the end of 40km fiber under test (FUT) is reduced from 2.4°C to 1.2°C by using DAE based DNN when compared with that using the LCF method, and the corresponding root-mean-square error (RMSE) is reduced from 2.4°C to 1.3°C. Moreover, since the temperature information can be extracted directly from the experimental BGS data without the need of time-consuming curve fitting and subsequent conversion from Brillouin frequency shift (BFS) to temperature, the speed of temperature extraction using the DAE based DNN is faster by 500 times than that using LCF. Combining the advantages of both denoising and fast processing speed, the DAE based DNN would be a practical way of temperature extraction for the BOTDA systems.