  Optical time-domain reflectometry (OTDR) is the basis for distributed
time-domain optical fiber sensing techniques. By injecting pulse light into an
optical fiber, the distance information of an event can be obtained based on
the time of light flight. The minimum distinguishable event separation along
the fiber length is called the spatial resolution, which is determined by the
optical pulse width. By reducing the pulse width, the spatial resolution can be
improved. However, at the same time, the signal-to-noise ratio of the system is
degraded, and higher speed equipment is required. To solve this problem, data
processing methods such as iterative subdivision, deconvolution, and neural
networks have been proposed. However, they all have some shortcomings and thus
have not been widely applied. Here, we propose and experimentally demonstrate
an OTDR deconvolution neural network based on deep convolutional neural
networks. A simplified OTDR model is built to generate a large amount of
training data. By optimizing the network structure and training data, an
effective OTDR deconvolution is achieved. The simulation and experimental
results show that the proposed neural network can achieve more accurate
deconvolution than the conventional deconvolution algorithm with a higher
signal-to-noise ratio.
