Abstract: The acoustic environment in polar sea areas is characterized by numerous high-intensity transient ice noise events, and the confusion between mooring noise and ice-generated noise in passive acoustic monitoring systems undermines data reliability. This study proposes a collaborative detection and suppression method for mooring noise. First, a detection model based on K-means clustering and principal component analysis is developed to extract spectral-level features for noise detection. Then, an improved median filtering-based harmonic separation algorithm is introduced, combining an adaptive separation factor with a time-frequency domain soft mask dynamic reconstruction mechanism for interference suppression. Experimental results demonstrate that the method effectively reduces the average spectral level by approximately 10 dB in typical mooring noise frequency bands while preserving environmental sound sources. The study also finds that the occurrence of mooring noise exhibits periodic variations consistent with equipment temperature and depth, and supplementary pool experiments confirm that the mechanical movement of the equipment’s vibration damping apparatus is the primary cause of the noise. Overall, this method addresses the decoupling challenge between specific mooring noise and natural sound sources in polar acoustic data, achieving a detection accuracy of 88.9% with a false alarm rate below 1.1%, thereby providing a reliable technical means and theoretical basis for polar environmental acoustic monitoring.