Accurate acquisition of blasting vibration signals is essential for analyzing the harmful effects of blasting operations. However, geological conditions, electromagnetic interference, and instrument errors can introduce significant high-frequency noise into the collected signals, leading to distortion and inaccurate data interpretation. To address this issue, a signal decomposition algorithm based on Ospley Optimization Algorithm (OOA) is proposed to optimize Variational Mode Decomposition (VMD). Multiscale Permutation Entropy (MPE) is also employed to construct a noise reduction model for tunnel blasting vibration signals. OOA is iteratively applied to determine the optimal VMD parameters (K & α) and obtain the intrinsic mode formula (IMF) using the maximum information coefficient as the fitness function. The MPE values of each decomposed signal are then used to identify the noise components, which are removed to reconstruct the denoised signal. This coupled algorithm was applied to analyze the blasting effects in Dashan Tunnel, Yunnan Province. The results demonstrate that the proposed optimization algorithm effectively decomposes the signal and eliminates noise without significantly affecting the low-frequency energy. The OOA-VMD denoising method's performance is superior to the complete ensemble empirical mode decomposition (CEEMD) and conventional VMD algorithm, thereby verifying its reliability.