Node seismometer signals are often contaminated with substantial environmental noise due to the complex conditions encountered in geophysical exploration and seismic monitoring. This necessitates high precision in the pre-processing of ground motion signals, as inadequate processing may compromise subsequent operations, such as P-wave first-arrival time extraction, peak energy calculation, ground motion period determination, and magnitude estimation. To obtain more authentic seismic waveforms, a node seismometer signal denoising model is proposed. This model integrates grey relational analysis (GRA) with improved complete ensemble empirical modal decomposition adaptive noise (ICEEMDAN). This method first decomposes the noisy signal using ICEEMDAN to obtain multiple intrinsic mode functions (IMFs), which are then sequentially arranged and labeled. Subsequently, for each IMF, the correlation coefficient, mutual information, R2, adjusted R2, Jensen–Shannon divergence, cosine similarity, root mean squared error, mean absolute error, mean absolute percentage error, and sample entropy were calculated, forming an evaluation matrix for assessing the reliability of all IMFs. Finally, using GRA, the correlation coefficients and degrees of association between each evaluation metric and different IMF components were calculated. The IMF components were ranked based on their association degrees to determine the relative effectiveness of their signal components. Linear reconstruction on the top-ranked IMF components was performed to complete the signal denoising process proposed. Experiments on denoising simulated seismic signals, recorded seismic event signals, and recorded ground motion signals all demonstrate that the GRA–ICEEMDAN model outperforms classical denoising methods. The comprehensive denoising scores for the three experiments were 100, 98.0180, and 93.9056, respectively, with signal-to-noise ratio improvements reaching 24.0049 dB, 20.8926 dB, and 16.3523 dB, respectively. The model effectively distinguishes noise components from effective components, with minimal reconstruction errors and signal loss after original signal decomposition, making it suitable for seismic monitoring and geophysical exploration involving small-to-medium sample sizes.