Noise in GPS displacement measurements from Southern California and Southern Nevada
Noise in GPS displacement measurements from Southern California and Southern Nevada
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DOI：
10.1029/2007jb005247
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发表时间：
2008-05-16
影响因子：
3.9
通讯作者：
Langbein, John
中科院分区：
文献类型：
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作者：
Langbein, John
从236个连续记录的GPS接收机在南加州和南内华达州的数据估计的位置变化的时间序列进行评估的噪声模型，其时间相关性的特点。时间序列的长度在3.5至10年之间。在调整这些数据的震后变形，偏移，和年度周期性，我发现，大约有一半的时间序列的时间相关性被归类为闪烁或随机游走噪声。其余的时间序列可以最好地分类为闪烁和随机游走的组合;幂律噪声;一阶高斯-马尔可夫加随机游走噪声;或幂律加宽带，季节性噪声。在南加州和内华达州使用了各种大地测量标志物，包括深支撑设计、水泥墩、在露头中钻孔的销和建筑物。当我根据速度的标准误差估计来评估每个时间序列的噪声时，我发现误差最小的地点是位于内华达州的那些使用深支撑纪念碑的地点。安装在地下水和石油的积极抽水区域内的站点，速度的标准误差最大。纪念碑的稳定性，测量的标准误差率，与平均，附近的年降雨量的比较表明一个轻微的显着相关性。此外，即使区域滤波去除了这些时间序列中的大部分共模信号，仍然存在可以并且应该去除的共模季节性信号。
Time series of position changes estimated from data from 236 continuously recording GPS receivers operating in Southern California and Southern Nevada are evaluated for noise models that characterize their temporal correlations. The lengths of the time series range between 3.5 and 10 years. After adjusting these data for postseismic deformation, offsets, and annual periodicities, I find that about one-half of the time series have temporal correlations that are categorized as either flicker or random-walk noise. The remaining time series can be best categorized as either a combination of flicker and random-walk; power law noise; first-order Gauss-Markov plus random-walk noise; or power law plus broadband, seasonal noise. A variety of geodetic monuments are used in Southern California and Nevada, including deeply braced designs, cement piers, pins drilled in outcrop, and buildings. When I evaluate the noise for each time series in terms of an estimate of the standard error in velocity, I find that the sites with the smallest errors are those located in Nevada using deeply braced monuments. Sites that are installed within regions of active pumping, both for groundwater and oil, had the largest standard errors in velocity. Comparison of monument stability, as measured by standard error in rate, with average, annual rainfall nearby indicates a marginally significant correlation. In addition, even though regional filtering removed much of the common-mode signals in these time series, there still remains a common-mode seasonal signal which can and should be removed.