We demonstrate a Brillouin optical frequency-domain reflectometry (BOFDR) technique, which can measure the strain and/or temperature along an optical fiber with one-end access, by detecting the spontaneous Brillouin scattering from a sinusoidally modulated pump light. Compared to the Brillouin optical frequency-domain analysis (BOFDA), we show that BOFDR measurements are free from the distorting components related to acoustic wave modulation, thus simplifying data processing.

We demonstrate, for the first time to our knowledge, a Brillouin optical time-domain analysis (BOTDA) sensor that is able to operate with a probe power larger than the Brillouin threshold of the deployed sensing fiber and that is free from detrimental nonlocal effects. The technique is based on a dual-probe-sideband setup in which an optical frequency modulation of the probe waves along the fiber is introduced. This makes the optical frequency of the Brillouin interactions induced by each probe wave on the pump vary along the fiber so that two broadband Brillouin gain and loss spectra that perfectly compensate are created. As a consequence, the pulse spectral components remain undistorted, avoiding nonlocal effects. Therefore, very large probe power can be injected, which improves the signal-to-noise ratio (SNR) in detection for long-range BOTDA. Moreover, the probe power can even exceed the Brillouin threshold limit due to its frequency modulation, which reduces the effective amplification of spontaneous Brillouin scattering in the fiber. Experiments demonstrate the technique in a 50-km sensing link in which 8 dBm of probe power is injected.

A system based on the use of artificial neural networks allowing discrimination of strain and temperature in a conventional Brillouin optical time domain analyzer setup is presented and demonstrated in this paper. This solution allows to perform an automatic discrimination of both parameters without compromising the complexity or cost of the interrogation unit. The classification results, achieved by considering a preprocessing stage with dimensionality reduction via principal component analysis and spatial filtering, improve those obtained in a previous feasibility study.