Abstract:

Herein, a combined denoising model based on the dualtree complex wavelet transform (DTCWT) and an improved least mean square(LMS) algorithm is proposed to denoise monitoring signals collected by distributed fiber optic of Brillouin optical timedomain reflectometer(BOTDR) systems in tunnel monitoring sites. First, the original signals are decomposed using the DTCWT algorithm. Second, the sample entropy(SE) is used as the objective function to automatically select the model with the optimal wavelet decomposition level. The LMS algorithm’s convergence and convergence speed are improved based on the optimized hyperbolic cosine function. Finally, BOTDR temperature signal denoising experiments are conducted to validate the effectiveness of the proposed algorithm. The results show the following: (1) The denoising effect of the DTCWTLMS algorithm is significantly superior to that of the traditional wavelet threshold denoising method. The average values of signaltonoise ratio(SNR) for the six temperature gradients is 43.98%, 17.5%, and 8.4% higher; the average root mean square error is 33.18%, 17.14%, and 9.23% lower; and the average SE value is 29.04%, 21.17%, and 20.67% lower than those for wavelet domain denoising(WDD), empirical wavelet transform(EMT), and empirical mode decomposition(EMD), respectively. The DTCWTLMS algorithm is applied to the denoising of fiber optic monitoring signals in a metro tunnel monitoring project in Beijing, China, and the average decrease in the SE of the denoised signal is 64.03%, validating the feasibility of the proposed algorithm. (2) Compared with the conventional WDD, EMT, and EMD methods, the DTCWTLMS algorithm performs better on the three fibers. The SNR indices on fibers Nos. 1, 2, and 3 are 22%, 38%, and 27% higher than the average values of the other three algorithms, respectively, demonstrating that the proposed algorithm is an effective denoising method for fiber optic monitoring signals in tunnel engineering.

Key words: tunnel monitoring, signal denoising, distributed fiber optic technology, wavelet analysis