摘要
中国境内目前已建立了超过4000个GNSS连续运行基准站。为了确保基准站稳定性,大部分基准站天线安装在一定高度的观测墩或楼顶,这些水泥或金属制观测墩可能会由于环境温度的季节性或周日周期性变化而产生热弹性形变位移,在GNSS基准站坐标时间序列中表现为周期性的上下震荡,导致计算的基准站位置不能精确反映真实的地表形变,尤其是在垂直方向上。通过处理由3个间隔不足20 m的IGS基准站组成的两条短基线数据,基于短基线时间序列,分析了不同时间尺度下由温度变化驱动的观测墩热弹性位移的时变特性。结果表明:GNSS天线观测墩会受环境温度季节性变化产生周年周期位移,位移大小和观测墩高度、基准站所处环境温度周年振幅线性相关,其表面温度的昼夜周期变化也会产生周日周期的热弹性位移,水平方向高频热弹性位移会由于采样率不足的原因混频为虚假的长周期信号。
There are over 4000 continuous GNSS reference stations in China nowadays.To ensure the stability,most of the aerials of the reference stations are settled on the top of a monument or a building roof.The concrete or metal monument may generate thermoelastic displacement due to the seasonal and daily ambient temperature variations,which exhibits large periodical oscillation in GNSS position time series.This may bias the result of surface deformation,especially in the vertical direction.In this paper,we processed two baselines data coming from 3 IGS stations with intervals less than 20 meters,and analyzed the time-varying characteristics of the thermoelastic displacement of the monument based on the baseline position time series on seasonal and daily scales.The results showed that the monuments would generate thermoelastic displacement with the seasonal ambient temperature variations,and the displacements were related to annual temperature amplitude and monument height.The temperature variation of the monument surface could also induce daily thermal displacement,and the horizontal high-frequency thermal displacement may propagate into spurious long-term signal into the time series due to the inadequate sampling rate.
作者
张俊文
王锴华
ZHANG Junwen;WANG Kaihua(Bureau of Lushui Experiment Hydropower Complex Management,Changjiang Water Resources Commission,Xianning 437300,China;Changjiang Spatial Information Technology Engineering Co.,Ltd.,Wuhan 430010,China)
出处
《人民长江》
北大核心
2020年第11期140-145,共6页
Yangtze River
基金
长江勘测规划设计研究院自主创新项目(CX2019Z57)
武汉大学地球空间环境与大地测量教育部重点实验室测绘基础研究基金项目(19-01-09)。
关键词
GNSS观测墩
温度变化
短基线时间序列
时变特性
GNSS monument
temperature variation
short-baseline time series
time-varying characteristics