Research on noise reduction algorithm for high slope deformation data based on CEEMD-KF modeling

The accuracy of high slope deformation data is crucial for slope stability assessment and disaster early warning, but a large amount of noise often exists in the actual monitoring data, which affects the quality of the data and the reliability of the analysis results. In view of this, this paper proposes a noise elimination algorithm for high slope deformation data (CEEMD-KF) based on complementary ensemble empirical modal decomposition (CEEMD) and Kalman filtering (KF). The algorithm firstly uses CEEMD to decompose the deformation data into multiple intrinsic modal function (IMF) components, and then performs KF filtering for noise reduction on the decomposed IMF components, and finally reconstructs the noise-reduced IMF components to obtain the noise-reduced deformation data. The effectiveness and superiority of CEEMD-KF model is verified by comparing with wavelet noise reduction methods with different thresholds.