%0 Journal Article
%T 基于EEMD-SpEn (样本熵)联合小波阈值对山西太原GNSS站点时间序列去噪分析<br>Time Series Denoising Analysis of GNSS Station in Taiyuan, Shanxi Province Based on EEMD-SpEn (Sample Entropy) Combined with Wavelet Threshold
%A 宫静芝
%A 冯宁
%A 吕永青
%A 陈常俊
%A 沈晓松
%J Advances in Geosciences
%P 1333-1340
%@ 2163-3975
%D 2024
%I Hans Publishing
%R 10.12677/ag.2024.1410123
%X 文章基于集合经验模态分解方法(EEMD)联合样本熵(SpEn)对山西太原GNSS站点时间序列降噪。首先&#65292;将原始站点时间序列进行EEMD分解&#65292;得到不同IMF (intrinsic mode function)分量&#65292;其次&#65292;计算每个IMF分量进行样本熵计算&#65292;根据样本熵值统计选择一个适当的去噪声阈值。最后&#65292;根据样本熵值去除小于阈值的小波系数&#65292;并重构IMF分量。得到去噪信号。计算结果显示&#65292;通过信噪比&#65292;相关系数评估去噪结果&#65292;得到结果可靠、高精度毫米级时间序列&#65292;为地震预报业务提供更好的服务。<br>In this paper, based on ensemble empirical Mode decomposition (EEMD) combined with sample entropy (SpEn), the time series of GNSS stations in Taiyuan, Shanxi Province is denoised. First, the original station time series was decomposed by EEMD to obtain different intrinsic mode function (IMF) components. Secondly, sample entropy was calculated for each IMF component, and an appropriate noise removal threshold was selected according to the sample entropy statistics. Finally, the wavelet coefficients smaller than the threshold are removed according to the sample entropy, and the IMF component is reconstructed. The denoised signal is obtained. The calculation results show that the denoising results are evaluated by signal-to-noise ratio and correlation coefficient, and the results are reliable and high-precision millimeter time series, which provides better service for earthquake prediction.
%K 集合经验模态分解(EEMD)&#65292
%K 样本熵(SpEn)&#65292
%K GNSS时间序列&#65292
%K 噪声<br>Ensemble Empirical Mode Decomposition (EEMD)
%K Sample Entropy (SpEn)
%K GNSS Time Series
%K Noise
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=98734