  In the field of Internet of Things, there is an urgent need for sensors with
large-scale sensing capability for scenarios such as intelligent monitoring of
production lines and urban infrastructure. Brillouin optical time domain
analysis (BOTDA) sensors, which can monitor thousands of continuous points
simultaneously, show great advantages in these applications. We propose a
convolutional neural network (CNN) to process the data of conventional
Brillouin optical time domain analysis (BOTDA) sensors, which achieves
unprecedented performance improvement that allows to directly retrieve higher
spatial resolution (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 BFS as the output target, the trained CNN is able to
obtain a 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
2-fold improvement in BFS uncertainty with only half the measurement time. In
addition, by changing the training datasets, the proposed CNN can obtain
tunable high SR retrieval based on conventional BOTDA sensors that use long
pulses without any requirement of hardware modifications. The proposed data
post-processing approach paves the way to enable novel high spatial resolution
BOTDA sensors, which brings substantial improvement over the state-of-the-art
techniques in terms of system complexity, measurement time and reliability,
etc.
