改进的启发式分割算法在GNSS坐标时间序列阶跃探测中的应用

Application of Improved Heuristic Segmentation Algorithm to Step Detection of GNSS Coordinate Time Series

  • 摘要: 在启发式分割算法的基础上,引入z检验和标准正态均一性检验(standard normal homogeneity test,SNHT),提出了一种新的阶跃探测法,并将其应用于20个陆态网(Crustal Movement Observation Network ofChina,CMONOC)和10个国际全球卫星导航系统服务(International Global Navigation Satellite System Ser-vice,IGS)参考站近5 a的坐标时间序列。结果显示,对于IGS站东(east,E)、北(north,N)、垂直(up,U)3个方向分量已知阶跃的平均准确探测率分别为68.5%、71.3%、64.8%,且利用阶跃点前后25 d初始拟合残差的平均值之差得到修复后的坐标时间序列保持了良好的连续性。基于剔除粗差后的坐标时间序列,利用坐标时间序列分析软件(coordinate time series analysis software,CATS)估计出的速度显示:对于由已知阶跃与改进算法探测出的阶跃历元的组合方式,所有测站E、N、U方向的估计速度与CMONOC官网发布的速度的平均偏差分别为2.86 mm/a、1.14 mm/a、2.31 mm/a,明显小于由已知阶跃和肉眼判断明显阶跃历元的组合方式获得的平均速度偏差。上述结果表明,改进的启发式分割算法可应用于坐标时间序列的阶跃探测。

     

    Abstract: Based on the heuristic segmentation algorithm, we introduce z-test and standard normal homogeneity(SNHT) test, proposes a new step detection method, which is applied to 20 CMONOC (Crustal Movement Observation Network of China) and 10 IGS (International GNSS Service) reference stations for nearly 5 years of coordinate time series. For the known step of IGS stations, the detection results show that the average accurate detection rates for three components of E, N, U are 68.5%, 71.3% and 64.8%, respectively, and we use the difference of the initial fitting residuals with average of 25 days before and after the step respectively, to repair the coordinates time series, the results are presented in good continuity. Based on the coordinate time series after eliminating the gross error, for the combination of known and detected step epochs, the results of estimation from coordinate time series analysis software show that:The mean standard deviation of velocity between the estimated velocity and the published velocity of CMONOC's stations for the E, N and U directions of all stations are 2.86 mm/a, 1.14 mm/a, 2.31 mm/a, respectively, which are significantly smaller than the average velocity standard deviation obtained by a combination of known and manually picked step epochs. The above results show that the improved heuristic segmentation algorithm can be applied to detect the step of coordinate time series.

     

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