Sweep frequency method with variance weight probability for temperature extraction of the Brillouin gain spectrum based on an artificial neural network.

The development of optical fiber sensors has led to the possibility of accumulating vast, real-time databases of acoustic and other measurements throughout fiber networks, which brings even more widespread concern on improving the sampling effectiveness. In this paper, we present two kinds of sweep frequency methods based on using a neural network to extract temperature from the Brillouin gain spectrum (BGS). Gauss centralization and variance weight probability methods are proposed to compare with the uniform sweep frequency method. By analyzing formulas of the ideal BGS model, we find the gain near the peak of Brillouin gain spectrum has greater correlation with temperature extraction than other positions. Therefore, the Gaussian centralized sweep method is proposed. We further investigate the variation of the weights in the neural network and Brillouin data distribution in different positions and find that the variance is positively correlated with the weights in hidden layers. So, we propose the sweep frequency method based on variance weight probability and make a complement to interpret the rationality of this method in neural network. In all the aforementioned approaches, 281 points are obtained between the 9.07 GHz to 9.35 GHz range under the same condition. The data of each method is trained ten times and tested through the same neural network structure. All the RMSE of each test stage covers all data collecting the passage. The result shows that the RMSE of variance weight probability sweep frequency method is 0.5277, which is superior to the Gauss centralization sweep frequency method that was 0.6864 and the uniform sweep frequency method that was 0.9140.