Signal Extraction and Physical Mechanism Analysis of the Seasonal Vertical Displacement in North China
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摘要: 华北地区地表垂直位移呈现显著的季节性波动。为探究其驱动源信号，利用独立分量分析方法对全球导航定位系统20个基准站的垂直位移时间序列进行时空分解，获取前6个独立分量，并与环境负载造成的垂直位移进行比较分析。结果表明，第一个独立分量呈现显著周年变化和相对均匀的空间响应，与大气负载位移时间序列的平均相关系数为0.60，两者吻合良好。第二和第三个独立分量均是周年和年内信号的组合，其空间响应反映出局部集聚特征，与华北地区水资源的分布状况相类似，合并这两个独立分量后与水文负载位移时间序列的平均相关系数为0.50。由此表明大气和水文负载是驱动华北地区季节性垂直位移的主要源信号。Abstract:Objectives North China is an important social and economic area in China but suffers from severe surface deformation. Monitoring the surface displacement effectively and timely can provide data basis for understanding the deformation mechanism and predicting disasters. However, the surface vertical displacement in North China shows significant seasonal fluctuations. Hence, this paper aims to analyze the spatiotemporal feature of seasonal vertical displacement and determine the driving sources.Methods Firstly, the vertical time series of 20 global navigation satellite system (GNSS) stations from January 2011 to November 2019 are decomposed by independent component analysis. Secondly, the first six independent components (ICs) are derived and compared with the vertical displacements caused by environmental loadings.Results The results show that GNSS-IC1 presents obvious annual variation, as well as a relative uniform spatial response, which is highly consistent with the displacement time series derived from atmospheric loading, and the average correlation coefficient at all stations is 0.60. GNSS-IC2 and GNSS-IC3 are both the mixed signals constrained by annual and intra-annual periods, while the spatial responses reflect locally clustered characteristics, which corresponds to the distribution of water resources in North China. After combining GNSS-IC2 and GNSS-IC3 as GNSS-IC2/3, the average correlation coefficient between GNSS-IC2/3 and the hydrological loading displacement time series at all stations is 0.50.Conclusions The temporal component of each IC is generally less clustered by different time cycles and the spatial response shows distinct localized characteristics. Atmospheric and hydrological loadings are the primary driving sources of seasonal vertical displacement in North China. However, the seasonal displacements induced by other effects, including non-tide ocean loading and thermal expansion effect of bedrock and observation pillar, are not extracted and need further analysis.
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图 2 三种模型获取各站点平均垂直位移的时间序列
Figure 2. Time Series of Average Vertical Displacement Derived from Three Models
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图 3 各台站季节性垂直时间序列及拟合的周年和半年信号
Figure 3. Seasonal Vertical Time Series and Fitted Annual and Semi-annual Signals
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图 4 GNSS、大气和水文负载位移的主成分累计方差占比
Figure 4. Cumulative Variance Percentages of PCs in Decomposition of GNSS, ATML and HYDL Displacement
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图 5 前6个GNSS-IC的时间分量和空间响应
Figure 5. Temporal Components and Spatial Response of the First Six GNSS-ICs
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图 6 前6个GNSS-IC时间分量的功率谱
Figure 6. Power Spectra of Temporal Components of the First Six GNSS-ICs
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图 7 前3个ATML-IC的时间分量和空间响应
Figure 7. Temporal Components and Spatial Response of the First Three ATML-ICs
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图 8 前3个HYDL-IC的时间分量和空间响应
Figure 8. Temporal Components and Spatial Response of the First Three HYDL-ICs
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