Microseismic signals generated by minor fracturing or deformation in rock masses are often weak and significantly affected by environmental noise, making it challenging to accurately identify effective signals and locate the fracturing source spatially. To eliminate noise superimposed on the fracturing signals and improve the Signal-to-Noise Ratio (SNR) of weak microseismic signals, this paper proposes a denoising method that combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Singular Spectrum Analysis (SSA). First, CEEMDAN is used to obtain the Intrinsic Mode Functions (IMFs) of the signal, and energy entropy is employed to optimize the signal components, removing low-frequency noise. Then, SSA is applied to the reconstructed signal to decompose it into components corresponding to different singular values. Using singular values, the reconstructed components are determined, and the final reconstructed signal achieves secondary filtering. The study is of significant importance for analyzing the location of weak microseismic events induced by fracturing in rock slopes and monitoring the dynamics of landslide hazards.Based on the theoretical and experimental results, the following conclusions can be drawn: (1) The traditional EMD method shows poor frequency separation effect when decomposing weak signals. Due to the strong coupling between microseismic weak signals and random noise, modal aliasing occurs in the components.(2) The simulation results of noisy sinusoidal function waveforms indicate that the SNR of the simulated waveform before denoising was 11.34 dB. After applying this method, the SNR improved to 21.53 dB, the root mean square error was reduced by 74.24%, and the signal energy was maintained at 98%. This method demonstrates a significant denoising effect.(3) Denoising of microseismic signals generated by hydraulic fracturing in the SF-6 well of the Fuling shale gas field in Chongqing shows that the high-frequency band denoising effect is superior to that of EMD and EMD-wavelet threshold methods.(4) Denoising experiments on three microseismic signals effectively removed the background noise, preserving the characteristics of the microseismic weak signals.