顾及GNSS坐标时间序列中季节信号的CEEMD降噪方法

针对互补集合经验模态分解（complementary ensemble empirical mode decomposition，CEEMD）在GNSS坐标时间序列降噪过程中需要选取筛选准则的问题，提出一种顾及GNSS坐标时间序列中季节信号的CEEMD降噪方法。首先利用CEEMD方法对GNSS坐标时间序列进行分解，然后计算各本征模态函数（intrinsic mode function，IMF）的平均周期，将平均周期低于120 d的IMF分量作为噪声分量扣除，并重构剩余分量为信号分量。利用该方法对中国大陆227个GNSS垂向坐标时间序列进行降噪，并与以连续均方误差和相关系数为筛选准则的CEEMD降噪方法的结果进行对比。结果表明，本文方法未出现过度降噪，而其他2种方法均导致部分测站的季节信号被扣除；在未过度降噪站点，本文方法GNSS坐标时间序列的RMS、幂律噪声和速度不确定度的平均改正率分别为19.13%、88.29%和86.46%，优于其他2种方法。

CEEMD Denoising Method with Seasonal Signals in GNSS Coordinate Time Series

To address the issue of the complementary ensemble empirical mode decomposition(CEEMD) having to set screening criteria, this paper takes seasonal signals into consideration when using CEEMD to denoise GNSS coordinate time series. The improved method firstly deconstructs GNSS coordinate time series into numerous intrinsic mode function(IMF), calculates their average periods, then utilizes IMFs with an average period of less than 120 days as noise components, while reconstructing the remaining components as signal components. This study applies the method to denoise 227 GNSS vertical coordinate time series stations over mainland China; it compares the results of CEEMD with the continuous mean square error and correlation coefficient methods. The results indicate that the described method does not denoise excessively, whereas the other two methods do. At the stations without excessive noise reduction, the average correction rates of RMS, power law noise, and velocity uncertainty of the GNSS coordinate time series of our methed are 19.13%, 88.29% and 86.46%, which are better than the other two methods.