Integrated denoising and extraction of both temperature and strain based on a single CNN framework for a BOTDA sensing system

Abstract

We have proposed and demonstrated a denoising and extraction convolutional neural network (DECNN) composed of 1D denoising convolutional autoencoder (DCAE) and 1D residual attention network (RANet) modules to extract temperature and strain simultaneously in a Brillouin optical time-domain analysis (BOTDA) system. With DCAE for high-fidelity denoising and RANet for accurate and robust information extraction, integrated denoising and extraction of both temperature and strain have been realized for the first time under a single CNN framework. Both simulation and experiment have been conducted to statistically analyze the performance of the proposed scheme and compare it with the conventional equation solving method (CESM), which show that DECNN has large noise tolerance and robustness over a wide range of temperature/strain and signal-to-noise ratio (SNR) conditions. The mean standard deviation (SD) and root mean square error (RMSE) of the temperature/strain extracted by DECNN over a wide range of SNRs are only 0.2°C/9.7µɛ and 2°C/32.3µɛ at the end of 19.38 km long sensing fiber, respectively. At a relatively low SNR of 8.8 dB, DECNN shows 196 times better temperature/strain uncertainty and 146 times faster processing speed when compared with CESM.