基于小波分析的湖南罗富冲滑坡监测数据去噪与预警

Wavelet Analysis-based Denoising and Early Warning of Monitoring Data in Luofuchong Landslide,Hunan Province

滑坡自动化监测数据中噪声的存在导致滑坡预警误报严重,在预警之前有必要对原始监测数据进行去噪。以湖南罗富冲滑坡为研究案例,首先使用小波分析对该滑坡GNSS地表变形数据进行分解,然后使用分解的低频分量进行信号重建以实现数据去噪,最后分别使用原始数据和去噪数据计算日变形速度以进行预警计算,通过对比分析验证去噪效果。研究结果表明,对原始滑坡自动化监测数据使用小波分析进行去噪处理能够有效提升预警的准确性,对于保障滑坡影响区域的人民群众生命财产安全具有重要意义。

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.