We experimentally demonstrate the use of artificial neural network (ANN) to process sensing signals obtained from Brillouin optical time domain analyzer (BOTDA). The distributed temperature information is extracted directly from the local Brillouin gain spectra (BGSs) along the fiber under test without the process of determination of Brillouin frequency shift (BFS) and hence conversion from BFS to temperature. Unlike our previous work for short sensing distance where ANN is trained by measured BGSs, here we employ ideal BGSs with different linewidths to train the ANN in order to take the linewidth variation due to different conditions from the training and testing phases into account, making it feasible for long distance sensing. Moreover, the performance of ANN is compared with other two techniques, Lorentzian curve fitting and cross-correlation method, and our results show that ANN has higher accuracy and larger tolerance to measurement error, especially at large frequency scanning step. We also show that the temperature extraction from BOTDA measurements employing ANN is significantly faster than the other two approaches. Hence ANN can be an excellent alternative tool to process BGSs measured by BOTDA and obtain temperature distribution along the fiber, especially when large frequency scanning step is adopted to significantly reduce the measurement time but without sacrifice of sensing accuracy.

We have experimentally analyzed and compared the performance of Brillouin optical time-domain analyzer (BOTDA) sensors assisted by non-local means (NLM) and wavelet denoising (WD) techniques in terms of measurement accuracy and experimental spatial resolution, respectively. Degradation of the measurement accuracy and experimental spatial resolution after denoising by NLM and WD are observed, which originate from the fact that higher signal-to-noise ratio (SNR) improvement is achieved at the expense of sacrificing the details of BOTDA data, and smaller data sampling point number (SPN) gives rise to insufficient redundant information for denoising. The two parameters degrade to different extents depending on the amount of SNR improvement and SPN adopted in data acquisition. Compared with WD, NLM relies more on the features of the raw data, which makes its performance highly dependent on the level of neighbouring data similarity. Also, for the first time we propose and demonstrate a BOTDA assisted by advanced Block-Matching and 3D filtering (BM3D) denoising technique, which minimizes the degradation of the two parameters even under higher SNR improvement and smaller SPN. BM3D takes the advantage of NLM and WD and utilizes the spatial-domain non-local principle to enhance the denoising in the transform domain, thus it shows the least degradation of measurement accuracy/experimental spatial resolution after denoising. Thus the BOTDA assisted by BM3D maintains the best measurement accuracy/experimental spatial resolution compared with those by NLM and WD. We also show that BM3D has the advantage of temperature independent performance, unlike NLM where the accuracy is affected by the temperature value. We believe BM3D would be an excellent denoising technique for state-of-the-art BOTDA sensors. In addition, this work is also valuable for practical applications of image denoising techniques in BOTDA sensors with respect to the appropriate choice of image denoising techniques, design of SNR improvement and the adoption of SPN to maintain optimal measurement accuracy/experimental spatial resolution/data acquisition speed.

Simultaneous temperature and strain measurement with enhanced accuracy by using Deep Neural Networks (DNN) assisted Brillouin optical time domain analyzer (BOTDA) has been demonstrated. After trained by using combined ideal clean and noisy BGSs, the DNN is applied to extract both the temperature and strain directly from the measured double-peak BGS in large-effective-area fiber (LEAF). Both simulated and experimental data under different temperature and strain conditions have been used to verify the reliability of DNN-based simultaneous temperature and strain measurement, and demonstrate its advantages over BOTDA with the conventional equations solving method. Avoiding the small matrix determinant-induced large error, our DNN approach significantly improves the measurement accuracy. For a 24-km LEAF sensing fiber with a spatial resolution of 2m, the root mean square error (RMSE) and standard deviation (SD) of the measured temperature/strain by using DNN are improved to be 4.2°C/134.2με and 2.4°C/66.2με, respectively, which are much lower than the RMSE of 30.1°C/710.2με and SD of 19.4°C/529.1με for the conventional equations solving method. Moreover, the temperature and strain extraction by DNN from 600,000 BGSs along 24-km LEAF requires only 1.6s, which is much shorter than that of 5656.3s by the conventional equations solving method. The enhanced accuracy and fast processing speed make the DNN approach a practical way of achieving simultaneous temperature and strain measurement by the conventional BOTDA system without adding system complexity.

Brillouin optical time-domain analyzer (BOTDA) assisted by support vector machine (SVM) for ultrafast temperature extraction is proposed and experimentally demonstrated. The temperature extraction is treated as a supervised classification problem and the Brillouin gain spectrum (BGS) is classified into each temperature class according to the support vectors and hyperplane of the SVM model after training. Ideal pseudo-Voigt curve-based BGS is used to train the SVM to get the support vectors and hyperplane. The performance of SVM is investigated in both simulation and experiment under various conditions for BGS collection. Both simulation and experiment results show that SVM is more robust to a wide range of signal-to-noise ratios, averaging times, pump pulse widths, frequency scanning steps, and temperatures. In addition to better accuracy, the processing speed for temperature extraction using SVM is 100 times faster than that using conventional Lorentzian curve and pseudo-Voigt curve fitting techniques in our experiment. The fast processing speed together with good accuracy and robustness makes SVM a highly competitive candidate for future high-speed BOTDA sensors.

We realize a scanning-free Brillouin optical time domain analyzer (BOTDA) based on an ultra-fine digital optical frequency comb (DOFC) with 1.95MHz frequency spacing and 2GHz bandwidth. The DOFC can be used to reconstruct the Brillouin gain spectrum (BGS) and locate the Brillouin frequency shift (BFS) without frequency scanning and thus can improve the measurement speed about 100 times compared with the conventional BOTDA. This scanning-free BOTDA scheme has also been demonstrated experimentally with 51.2m spatial resolution over 10km standard single mode fiber (SSMF) and with resolution of 1.5°C for temperature and 43.3με for strain measurement respectively.

The maximum detectable vibration frequency by phase-sensitive optical time-domain reflectometer ( $\Phi$ -OTDR) is limited by the repetition rate of the pump pulse. Additionally, the intensity-demodulation based $\Phi$ -OTDR sensor suffers from the generally nonlinear dependency of the backscattered optical intensity on vibration. This makes quantitative measurement of vibration frequency difficult. In order to mitigate these limitations, we propose and demonstrate a multicore fiber (MCF) based space-division multiplexed (SDM) $\Phi$ -OTDR and Mach-Zehnder interferometer (MZI) hybrid sensor, enabling truly uninterrupted distributed vibration sensing with broad vibration frequency response range and high spatial resolution. By taking advantage of highly integrated multiple spatial cores offered by a MCF, $\Phi$ -OTDR implemented in one of the cores is used to locate the vibration. Meanwhile, a MZI is constructed using another two parallel spatial cores with sufficient continuous-wave (CW) optical power from a coherent narrow linewidth laser, which is used to retrieve the vibration frequency by processing the acquired interference signal. Compared with the single mode fiber based hybrid systems, the proposed SDM configuration allows implementation of $\Phi$ -OTDR and MZI independently without any crosstalk between them. Consequently it offers many unique advantages, such as simple data processing procedure, good signal-to-noise ratio (SNR) of the demodulated vibration frequency spectrum, no frequency dead zone, and single-end access. This cost-effective SDM hybrid sensor provides significant potential for distributed and accurate monitoring of vibrations.