边坡变形监测数据小波去噪研究

变形监测中获得的观测资料不可避免地存在着噪声,如何从受噪声干扰的数据序列中提取特征信息,提高变形监测的精度是变形监测系统所涉及的关键技术之一。利用小波方法对边坡监测的数据进行去噪研究,着重研究了降噪方法(算法)、小波基函数以及阈值等的影响。硬阈值比软阈值处理后的信号更加粗糙,软阈效果好于硬阈,阈值和小波基函数都有重要影响。采用强制去噪后的效果很明显,重构层数选取对滤波效果有重要影响。

Investigation on Wavelet Denoising with Deformation Monitoring Data of Slopes

There inevitably exists noise in observation data obtained in deformation monitoring. How to extract diagnostic information from noise data sequence and improve deformation monitoring precision is the critical technology among the deformation monitoring system. This paper applies the wavelet theory method to study the denoise effect with the monitoring data, which mainly focuses on studies of the influence of the denoise method (algorism), wavelet base function and threshold value. The data processed by hard threshold value is rougher than that by soft threshold value. And the soft threshold value has a better effect. The threshold value and wavelet base function have an important influence. It achieves a good effect by forced denoise, and the select of reconstruction scale also has an important influence on filtering.