Analysis and Research for Tunnel Monitoring Data Using Wavelet Transform Modulus Maxima
Shiyu Han, Xiangxing Kong · 2025
Wavelet analysis has emerged as a powerful tool across multiple disciplines including signal processing, pattern recognition, and structural health monitoring. This study conducts a comparative evaluation of three wavelet-based denoising techniques: modulus maxima, wavelet packet decomposition, and coefficient shrinkage. Through systematic analysis of their operational characteristics, we establish distinct applicability domains for each method. Experimental validation employs synthetic noise-contaminated signals to optimize critical parameters for the modulus maxima approach. The refined methodology demonstrates exceptional performance in processing shield tunnel monitoring data, effectively extracting structural response signatures while suppressing measurement noise. The denoised datasets provide reliable inputs for subsequent tunnel integrity assessments, confirming the method's engineering applicability.