一个改进的高斯过程来填补GNSS位置时间序列中缺少的数据,考虑到相邻站的影响
Xiaomeng Qiu1, Fengwei Wang2, Qiuxi Zhang1
1Gandong University, Fuzhou, People's Republic of China.
Scientific reports
|August 20, 2024
概括
一种改进的高斯过程 (GP) 方法通过结合相邻站数据,有效地填补缺少的全球导航卫星系统 (GNSS) 数据. 这种方法在精确的时间序列分析中优于传统的GP和立方线方法.
科学领域:
- 地质测量是指地质测量.
- 地质物理学 地质物理学
- 数据科学数据科学数据科学
背景情况:
- 全球导航卫星系统 (GNSS) 位置时间序列中缺少数据是一个常见的问题,阻碍了准确的分析.
- 传统的数据插值方法往往忽略了附近的GNSS站之间的空间相关性.
- 准确地归算缺失的GNSS数据对于可靠的地质物理和地测学研究至关重要.
研究的目的:
- 开发和评估一种改进的高斯过程 (GP) 方法来填补GNSS位置时间序列中缺少的数据.
- 评估改进的GP方法的性能与传统的GP和立方支线方法相比.
- 为了证明将相邻站数据纳入归算过程的有效性.
主要方法:
- 开发了一种增强的高斯过程 (GP) 模型,利用从相邻站时间序列中得出的冲击因子.
- 为了评估性能,进行了模拟实验,使用不同的缺失数据百分比 (5-30%) 来评估性能.
- 关键性能指标包括根平均平方误差 (RMSE),平均绝对误差 (MAE) 和相关系数 (R).
- 分析了来自八个站的现实世界GNSS数据,以验证该方法的有效性.
主要成果:
- 改进的GP方法在填补缺少的GNSS数据方面表现出比传统的GP和立方线方法更高的准确性.
- 在北方,东方和上方组件中观察到RMSE和MAE的显著改善,相对RMSE的改善范围从8.3%到21.3%.
- 对现实数据的主要组件分析表明,改进的GP捕获的差异比传统方法要大得多 (高达99.8%).
结论:
- 通过利用相邻站信息,改进的GP方法为归纳缺失的GNSS数据提供了更准确和更强大的解决方案.
- 这种方法提高了GNSS时间序列分析的可靠性,这对于地力学和地变形监测的应用至关重要.
- 这些发现表明,GNSS数据分析的数据预处理技术取得了重大进展.
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