  Distributed optical fiber Brillouin sensors detect the temperature and strain
along a fiber according to the local Brillouin frequency shift, which is
usually calculated by the measured Brillouin spectrum using Lorentzian curve
fitting. In addition, cross-correlation, principal component analysis, and
machine learning methods have been proposed for the more efficient extraction
of Brillouin frequency shifts. However, existing methods only process the
Brillouin spectrum individually, ignoring the correlation in the time domain,
indicating that there is still room for improvement. Here, we propose and
experimentally demonstrate a full convolution neural network to extract the
distributed Brillouin frequency shift directly from the measured
two-dimensional data. Simulated ideal Brillouin spectrum with various
parameters are used to train the network. Both the simulation and experimental
results show that the extraction accuracy of the network is better than that of
the traditional curve fitting algorithm with a much shorter processing time.
This network has good universality and robustness and can effectively improve
the performances of existing Brillouin sensors.
