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中文摘要：
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。
英文摘要：
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.
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