香港GPS基准站坐标序列特征分析

Abstract

利用香港GPS连续运行参考站网络2001年1月至2007年8月的观测资料,全面深入地分析了12个基准站坐标序列特征.本文采用主成分空间滤波算法去除公共误差,来提高坐标序列的信噪比,并采用最大似然估计准则定量估计滤波后坐标序列的噪声特性,计算了地球表面质量负荷(包括大气、非潮汐海洋、积雪和土壤水)对香港GPS基准站坐标序列的影响.研究结果表明:香港GPS基准站坐标序列具有高度的空间相关性,其公共误差具有较强的季节性变化特征;地表质量负荷变化引起的香港地壳形变可以解释公共误差序列中约为3mm的垂向周年变化,经过质量负荷改正后的公共误差序列与高阶电离层误差高度相关;滤波后坐标序列的噪声特性可以用可变白噪声加闪烁噪声模型来描述,顾及闪烁噪声所计算的速度误差要比只考虑可变白噪声计算的速度误差大2～6倍;基准站间存在达1.5mm/yr的相对水平运动,揭示香港地区存在活动断层;部分基准站坐标具有明显的振幅为1～2mm本地季节性变化,所有测站的残差序列也表现出强烈的季节性变化. Characteristics of daily position time series from January,2001 to August,2007 at 12 stations in the Hong Kong GPS fiducial network are investigated in this paper.A spatial filtering algorithm based on principal component analysis is employed to remove the common mode errors from the daily position time series.The noise characteristics of the filtered position time series are assessed by the method of maximum likelihood estimation.Contributions from atmospheric,nontidal oceanic,snow and soil moisture mass loading are evaluated.The results indicate that spatial filtering is an effective way to improve the precision of position time series and provide better resolution for detecting local deformation signals.The common mode errors have strong seasonal variation.The observed ~3 mm annual vertical variation of the common mode errors can be explained by the joint contribution of these seasonal surface mass redistributions.After removing these surface mass loading effects the residual common mode errors are highly related to the higher-order ionospheric effects.The noise in the filtered position time series can be described as a combination of variable white noise plus flicker noise.The velocity uncertainties are about 2~6 times larger if only variable white noise is assumed.The maximum relative horizontal velocity between the sites is 1.5 mm/yr,which indicates some local fault activities.In addition,there are obvious 1~2 mm local seasonal signals in the filtered position time series of some sites.The residual scatters of all filtered time series also show strong seasonal characteristics.