CAJ | 학술논문

针对高铁隧道沉降变形监测数据具有沉降量小、受随机噪声干扰大且具有较明显的多尺度特征和非平稳趋势性等特点，为克服Kalman 滤波算法的不稳定性，运用基于小波多尺度分析的Kalman滤波对原始监测数据进行去噪，并用高铁沉降评估方法对基于小波多尺度Kalman滤波去噪后的数据进行预测和评估。结果表明：小波多尺度Kalman滤波去噪后提高了动态变形监测数据精度；沉降曲线更平滑且更逼近真实沉降情况；沉降变形评估的相关系数及可靠性均有所提高。
침대고철수도침강변형감측수거구유침강량소、수수궤조성간우대차구유교명현적다척도특정화비평은추세성등특점，위극복Kalman 려파산법적불은정성，운용기우소파다척도분석적Kalman려파대원시감측수거진행거조，병용고철침강평고방법대기우소파다척도Kalman려파거조후적수거진행예측화평고。결과표명：소파다척도Kalman려파거조후제고료동태변형감측수거정도；침강곡선경평활차경핍근진실침강정황；침강변형평고적상관계수급가고성균유소제고。
According to characteristics of high-speed railway tunnel subsidence deformation monitoring data with a small settlement,obvious multi-scale features,periodicity and non-stationary trend,the instability of Kalman filtering,wavelet multi-scale Kalman filtering was used to de-noise the original monitoring data,to predict and assess the data de-noised by wavelet multi-scale Kalman filtering with the high-speed railway settlement evalua-tion method.The accuracy of dynamic deformation monitoring data were improved after wavelet multi-scale Kal-man filtering.Settlement curve becomes smooth and approaches the real settlement.Correlation coefficient and reliability of the settlement deformation assessment are improved.