Enabling Variable High Spatial Resolution Retrieval From a Long Pulse BOTDA Sensor

Abstract

Spatial resolution (SR) is one of the most important parameters of Brillouin optical time-domain analysis (BOTDA) sensors, which determines the minimum length that a perturbation event can be distinguished. In the field of Internet of Things (IoT), there is an urgent need for sensors with large-scale high-precision sensing capability for scenarios, such as intelligent monitoring of production lines and urban infrastructure. Conventionally, the SR is normally restricted to be longer than 1 m due to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 10$ </tex-math></inline-formula> -ns acoustic lifetime limitation in silica optical fibers. For long-distance smart monitoring systems, the SR is generally on the order of several meters or even worse. However, it does not meet the needs of many applications. Therefore, there is an urgent need to achieve SR in the submeter magnitude. In this work, for the first time to the best of our knowledge, we propose a convolutional neural network (CNN) to process the data of conventional BOTDA sensors, which achieves unprecedented performance improvement that allows to directly retrieve submeter SR from the sensing system that use long pump pulses. By using the simulated Brillouin gain spectrums (BGSs) as the CNN input and the corresponding high SR Brillouin frequency shift (BFS) as the output target, the trained CNN is able to obtain an SR higher than the theoretical value determined by the pump pulse width. In the experiment, the CNN accurately retrieves 0.5-m hotspots from the measured BGS with pump pulses from 20 to 50 ns, and the acquired BFS is in great agreement with 45/40 ns differential pulse-width pair (DPP) measurement results. Compared with the DPP technique, the proposed CNN demonstrates a twofold improvement in BFS uncertainty with only half the measurement time. In addition, by changing the training data sets, the proposed CNN can obtain tunable high SR retrieval based on conventional BOTDA sensors that use long pulses without any requirement of hardware modifications. It is worth mentioning that the proposed method is also applicable to larger pulse widths to retrieve a submeter SR. The proposed data post-processing approach paves the way to enable novel high SR BOTDA sensors, which brings substantial improvement over the state-of-the-art techniques in terms of system complexity, measurement time, reliability, etc.