Wavelet Analysis-based Denoising and Early Warning of Monitoring Data in Luofuchong Landslide,Hunan Province
The existence of noise in the landslide automated monitoring data leads to serious landslide warning false alarms,and it is necessary to denoise the original monitoring data before warning.Taking Hunan Luofuchong landslide as a study case,firstly,the GNSS surface deformation data of the landslide is decomposed using wavelet analysis,and then the signal reconstruction is carried out using the decomposed low-frequency components to realize the denoising of the data,and finally the daily deformation speed is calculated using the original data and the denoised data to carry out the calculation of the early warning,and the effect of denoising is verified through the comparative analysis.The results show that denoising the original landslide automated monitoring data using wavelet analysis can effectively improve the accuracy of early warning,which is of great significance for the protection of people's lives and properties in landslide-affected areas.
万方数据
维普
