中文摘要：

高铁隧道的变形量较小,但受随机噪声的干扰较大,使得监测得到的沉降曲线不能反映实际的沉降情况。鉴于此,文章提出了基于小波变换与卡尔曼滤波相结合的RLG降噪方法,该方法既有小波变换的去相关作用和多分辨分析功能,又有卡尔曼滤波对未知信号的线性无偏最小方差估计的特点。采用GM（1,1）预测模型对降噪后的数据进行分析,得到的结论是：基于小波变换与卡尔曼滤波相结合的GM（1,1）模型的精度较基于卡尔曼滤波的GM（1,1）模型的精度高,可有效地运用于高铁隧道沉降分析中。

英文摘要：

Although the settlement deformation of high-speed railway tunnels is not high, it is possible that a settlement curve may not reflect actual settlement deformation due to random noise interference. An RLG denoising method combining a wavelet transform with a Kahnan filter is put forward that has the functions of noise-related interference removal and multiple resolution analysis by wavelet transform, and also has the advantages of linear unbiased minimum variance estimation to unknown signals by Kahnan filtering. By applying the GM（1, 1） model to analyze the data after denoising, it is detemined that the GM（1,1） model combining the wavelet transform and Kalman filtering is higher in precision than that of the GM（1,1） model based only on Kalman filtering and can be used for settlement analysis in high-speed railway tunnels.