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Helmert transformation strategies in analysis of GPS position time-series
Geophysical Journal International ( IF 2.8 ) Pub Date : 2020-08-05 , DOI: 10.1093/gji/ggaa371
Shiwei Guo 1 , Chuang Shi 1, 2 , Na Wei 3 , Min Li 3 , Lei Fan 4 , Cheng Wang 4 , Fu Zheng 4
Affiliation  

Global positioning system (GPS) position time-series generated using inconsistent satellite products should be aligned to a secular Terrestrial Reference Frame by Helmert transformation. However, unmodelled non-linear variations in station positions can alias into transformation parameters. Based on 17 yr of position time-series of 112 stations produced by precise point positioning (PPP), we investigated the impact of network configuration and scale factor on long-term time-series processing. Relative to the uniform network, the uneven network can introduce a discrepancy of 0.7–1.1 mm, 21.3–27.5 μas and 1.3 mm in terms of root mean square (RMS) for the translation, rotation and scale factor (if estimated), respectively, no matter whether the scale factor is estimated. The RMS of vertical annual amplitude differences caused by such network effect reaches 0.5–0.6 mm. Whether estimating the scale factor mostly affects the Z-translation and vertical annual amplitude, leading to a difference of 1.3 mm when the uneven network is used. Meanwhile, the annual amplitude differences caused by the scale factor present different geographic location dependences over the north, east and up components. The seasonal signals derived from the transformation using the uniform network and without estimating scale factor have better consistency with surface mass loadings with more than 41 per cent of the vertical annual variations explained. Simulation studies show that 40–50 per cent of the annual signals in the scale factor can be explained by the aliasing of surface mass loadings. Another finding is that GPS draconitic errors in station positions can also alias into transformation parameters, while different transformation strategies have limited influence on identifying the draconitic errors. We suggest that the uniform network should be used and the scale factor should not be estimated in Helmert transformation. It is also suggested to perform frame alignment on PPP time-series, even though the used satellite products belong to a consistent reference frame, as the origin of PPP positions inherited from satellite orbits and clocks is not so stable during a long period. With Helmert transformation, the seasonal variations would better agree with surface mass loadings, and noise level of time-series is reduced.

中文翻译:

GPS位置时间序列分析中的Helmert变换策略

使用不一致的卫星产品生成的全球定位系统(GPS)位置时间序列应通过Helmert变换与世俗的地面参考系对齐。但是,站位置中未建模的非线性变化会混淆为变换参数。基于精确点定位(PPP)产生的112个站点的17年位置时间序列,我们研究了网络配置和比例因子对长期时间序列处理的影响。相对于均匀网络,不均匀网络相对于平移,旋转和比例因子(如果估算)的均方根(RMS)差异分别为0.7–1.1 mm,21.3–27.5μas和1.3 mm,无论是否估计比例因子。由这种网络效应引起的垂直年振幅差的RMS达到0.5-0.6 mm。估计比例因子是否主要影响Z平移和垂直年振幅,导致使用不均匀网络时相差1.3 mm。同时,由比例因子引起的年振幅差异在北部,东部和上部地区呈现出不同的地理位置依赖性。使用统一网络从转换中得出的季节信号且未估计比例因子,与地面质量负荷具有更好的一致性,所解释的垂直年度变化超过41%。模拟研究表明,比例因子中年信号的40%至50%可以用表面质量载荷的混叠来解释。另一个发现是,站位中的GPS严重误差也可以混入转换参数中,而不同的转换策略对识别严重误差的影响有限。我们建议应在Helmert变换中使用统一网络,而不应估计比例因子。即使使用的卫星产品属于一致的参考帧,也建议对PPP时间序列进行帧对齐,因为从卫星轨道和时钟继承的PPP位置的来源在很长一段时间内都不太稳定。通过Helmert变换,季节变化将更好地与表面质量负荷相吻合,并降低了时间序列的噪声水平。而不同的转换策略对识别严重错误的影响有限。我们建议应在Helmert变换中使用统一网络,而不应估计比例因子。即使使用的卫星产品属于一致的参考帧,也建议对PPP时间序列进行帧对齐,因为从卫星轨道和时钟继承的PPP位置的来源在很长一段时间内都不太稳定。通过Helmert变换,季节变化将更好地与表面质量负荷相吻合,并降低了时间序列的噪声水平。而不同的转换策略对识别严重错误的影响有限。我们建议应在Helmert变换中使用统一网络,而不应估计比例因子。即使使用的卫星产品属于一致的参考帧,也建议对PPP时间序列进行帧对齐,因为从卫星轨道和时钟继承的PPP位置的来源在很长一段时间内都不太稳定。通过Helmert变换,季节变化将更好地与表面质量负荷相吻合,并降低了时间序列的噪声水平。即使使用的卫星产品属于一致的参考帧,也建议对PPP时间序列进行帧对齐,因为从卫星轨道和时钟继承的PPP位置的来源在很长一段时间内都不太稳定。通过Helmert变换,季节变化将更好地与表面质量负荷相吻合,并降低了时间序列的噪声水平。即使使用的卫星产品属于一致的参考帧,也建议对PPP时间序列进行帧对齐,因为从卫星轨道和时钟继承的PPP位置的来源在很长一段时间内都不太稳定。通过Helmert变换,季节变化将更好地与表面质量负荷相吻合,并降低了时间序列的噪声水平。
更新日期:2020-08-31
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