Abstract
Conference Title: 2017 Conference on Lasers and Electro-Optics Pacific Rim (CLEO-PR) Conference Start Date: 2017, July 31 Conference End Date: 2017, Aug. 4 Conference Location: Singapore, Singapore Extraction of temperature distribution using the method of deep neural networks (DNN) for Brillouin optical time domain analyzer (BOTDA) system is demonstrated experimentally. After appropriate training of DNN model, temperature distribution information along the fiber under test could be directly extracted from the experimentally obtained local Brillouin gain spectrums (BGS) using DNN without the need of calculating Brillouin frequency shift (BFS) and transforming it to temperature as conventional Lorentz curve fitting (LCF) method does. The results of Temperature extraction using DNN show comparable accuracy to that of using conventional LCF method.