Noise reduction analysis of high slope deformation data based on MEEMD-SG modeling

The stability of high slopes is of great significance to engineering safety, and the accurate analysis of high slope deformation data is the key to assessing their stability. However, the deformation data of high slopes obtained in practice are often interfered by multiple noises, which affects the quality of the data and the subsequent analysis results. In order to improve the quality of high slope deformation data, this paper proposes a noise reduction method for high slope deformation data based on the set-averaged empirical modal decomposition (MEEMD) and Savitzky-Golay (SG) filtering. The effectiveness and superiority of the method is verified by processing and analyzing the actual high slope deformation data.