Application of EMD-KF model in noise reduction of high slope deformation data

The accuracy of high slope deformation data is crucial for slope stability assessment and disaster early warning, however, the actual monitoring data often contain a large amount of noise, which affects the analysis and application of the data. In this paper, a combined model (EMD-KF) based on empirical modal decomposition (EMD) and Kalman filter (KF) is proposed for noise cancellation of high slope deformation data. The feasibility and effectiveness of the EMD-KF model are verified by comparing the noise-canceled data with wavelet noise-canceling methods with different thresholds. The experimental results show that the EMD-KF model has a better performance in noise-canceling of high slope deformation data, which is able to remove the noise effectively and improve the quality and reliability of the data.