Abstract:Taking measured data of surface deformation transverse observation line of Qingdao Metro Line 3 as an example, this paper studies a wavelet de-noising combined model. First, we use wavelet theory to eliminate observation value errors. According to the principle of lowest mean square error and highest signal to noise ratio, the calculated results show that dmey wavelet decomposition and rigrsure soft threshold wavelet de-noising are optimal. Second, we present the surface deformation predicting model expression combined with gray and time series of the subway tunnel. The settlement value GM(1,1) model and residual time series model are selected to predict surface deformation. Last, we analyze and compare the wavelet de-noising time series model and the combined wavelet de-noising gray and time series prediction model, both pre and post. The results show the post wavelet de-noising gray and time series combined model has the highest prediction accuracy. We analyze the different causes of each model.
LUAN Yuanzhong,WENG Liyuan,DU Chao et al. Study on Surface Deformation Wavelet De-noising of Subway Tunnel and Combined Prediction Model with Gray and Time Series[J]. jgg, 2016, 36(8): 678-681.