模平方阈值去噪法在沉降监测中的应用

Application of wavelet threshold method based on modular square in subsidence monitoring

针对现有去噪方法中存在的噪声信号提取、粗差定位等问题,该文基于小波阈值去噪的原理,提出一种基于软阈值改进的模平方阈值去噪法。通过仿真数据实验对比分析了软阈值去噪法、加权平均阈值去噪法及模平方阈值去噪法的去噪实际效果,并应用于汽车试验场沉降数据预处理。实验结果表明,基于模平方的阈值去噪法能够较好地保留观测信号原始信息,并且可以有效地去除噪声,其去噪效果优于软阈值和加权平均阈值去噪法,能在汽车试验场沉降数据处理中得到较好的应用。

Aiming at problems of existing de-noising methods, on the basis of the principle of wavelet threshold de-noising, a modular square threshold de-noising method which improved based on soft thresh- old was proposed. The de-noise effect of soft threshold de-noising method, the weighted average of the threshold de-noising method and modular square threshold de-noising method were compared by using the simulation data. Then three methods were applied in the proving ground subsidence data preprocessing. Experimental results showed that the modular square threshold de-noising method could retain the original information of observation signal more reasonable; its de-noising effect was better than that of soft threshold and weighted average threshold, which was well applicable for subsidence data processing in proving ground.