High spatial resolution Raman distributed fiber sensing based on neural network dispersion compensation

Raman distributed fiber sensing (RDFS) has garnered significant research attention due to its advantages such as wide measurement range, low cost, and rapid response. However, as the sensing distance of RDFS increases, dispersion effects in sensing fiber severely degrade the spatial resolution and temperature measurement accuracy. This paper proposes a RDFS based on a 1-dimensional dilated convolutional residual neural network (1D-DCRNN). Through analysis of multiple datasets affected by dispersion and combined with theoretical models, we construct training datasets to train the 1D-DCRNN, then employ the trained model to compensate for dispersion effects to enhance system performance. Experimental results depict that at a sensing distance of 16.8 km, the temperature error is reduced from over 16.0 ℃ to below 1.0 ℃ after dispersion compensation, while the spatial resolution improves from 3.0 m to 0.4 m. This technical achieves efficient dispersion compensation without requiring physical modifications to fiber transmission lines, providing a new technical avenue for long-distance, high-precision Raman distributed fiber sensing applications.