CEEMDAN在GNSS时间序列分析中的应用

doi:10.12196/j.issn.1000-3274.2022.03.007

地震 ›› 2022, Vol. 42 ›› Issue (3): 99-110.doi: 10.12196/j.issn.1000-3274.2022.03.007

CEEMDAN在GNSS时间序列分析中的应用

刘希康^1,2, 丁志峰¹, 李媛², 李进武²

1.中国地震局地球物理研究所, 北京 100081;
2.中国地震局第一监测中心, 天津 300180

收稿日期:2021-08-06 修回日期:2021-11-09 出版日期:2022-07-31 发布日期:2023-03-29
作者简介:刘希康(1988-), 男, 新疆伊犁人, 博士研究生, 主要从事地表形变与地下结构研究。
基金资助:
国家重点研发计划项目(2018YFC1503606); 中国地震局青年震情跟踪课题(2020010226, 2021010511); 国家自然科学基金项目(41472180)

Application of CEEMDAN in GNSS Time Series Analysis

LIU Xi-kang^1,2, DING Zhi-feng¹, LI Yuan², LI Jin-wu²

1. Institute of Geophysics, China Earthquake Administration, Beijing 100081, China;
2. First Crust Monitoring and Application Center, China Earthquake Administration, Tianjin 300180, China

Received:2021-08-06 Revised:2021-11-09 Online:2022-07-31 Published:2023-03-29

摘要/Abstract

摘要： 通过对模拟信号的处理, 验证了自适应完备经验模态分解(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, CEEMDAN)方法在资料分析处理过程中的有效性和可靠性。利用该方法对10个GNSS(Global Navigation Satellite System)连续站N、 E、 U三方向时间序列进行分解, 发现其不仅存在年周期和半年周期信号, 部分台站还存在更短或更长周期信号, 且垂向周期性比水平向更明显, 对应幅值更大。经周期项改正后, 相比于原始N、 E、 U方向时间序列的平均WRMS(Weighted Root Mean Square error)分别降低了19.8%、 19.0%和29.9%, 表明该方法对周期项修正的有效性。

关键词: GNSS, 周期项, 谐波模型, 自适应完备经验模态分解

Abstract: The validity and reliability of the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method in the data analysis and processing are verified by processing the simulated signals. Using this method to decompose the N, E and U direction time series of 10 GNSS (Global Navigation Satellite System) continuous stations, it is found that not only annual and semi-annual period signals exist, but also shorter or longer period signals exist in some stations, and the vertical periodicity is more obvious than the horizontal direction, and the corresponding amplitudes are larger. The average WRMS (Weighted Root Mean Square error) of the time series in the N, E, and U directions was reduced by 19.8%, 19.0%, and 29.9%, respectively, compared to the original N, E, and U directions after the periodic term correction, indicating the effectiveness of the method for periodic term correction.

Key words: GNSS, Periodic term, Harmonic model, CEEMDAN

中图分类号:

P315.7

刘希康, 丁志峰, 李媛, 李进武. CEEMDAN在GNSS时间序列分析中的应用[J]. 地震, 2022, 42(3): 99-110.

LIU Xi-kang, DING Zhi-feng, LI Yuan, LI Jin-wu. Application of CEEMDAN in GNSS Time Series Analysis[J]. EARTHQUAKE, 2022, 42(3): 99-110.

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CEEMDAN在GNSS时间序列分析中的应用

Application of CEEMDAN in GNSS Time Series Analysis

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