The 95% Confidence Interval for GNSS-Derived Site Velocities

IF 1.8 3区工程技术 Q3 ENGINEERING, CIVIL

Journal of Surveying Engineering Pub Date : 2022-02-01 DOI:10.1061/(asce)su.1943-5428.0000390

Guoquan Wang

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引用次数: 6

Abstract

Linear trends, or site velocities, derived from global navigation satellite system (GNSS) positional time series have been commonly applied to site stability assessments, structural health monitoring, sea-level rise, and coastal submergence studies. The uncertainty of the velocity has become a big concern for stringent users targeting structural or ground deformation at a few millimeters per year. GNSSderived positional time series are autocorrelated. Consequently, conventional methods for calculating the standard errors of the linear trends result in unrealistically small uncertainties. This article presents an approach to accounting for the autocorrelation with an effective sample size (Neff). A robust methodology has been developed to determine the 95% confidence interval (95%CI) for the site velocities. It is found that the 95%CI fits an inverse power-law relationship over the time span of the time series (vertical direction: 95%CI 1⁄4 5.2T−1.25; east–west or north–south directions: 95%CI 1⁄4 1.8T−1.0). For static GNSS monitoring projects, continuous observations longer than 2.5 and 4 years are recommended to achieve a 95%CI below 1 mm=year for the horizontal and vertical site velocities, respectively; continuous observations longer than 7 years are recommended to achieve a 95%CI below 0.5 mm=year for the vertical land movement rate (subsidence or uplift). The 95%CI from 7-year GNSS time series is equivalent to the 95%CI of the sea-level trend derived from 60-year tide gauge observations. The method and the empirical formulas developed through this study have the potential for broad applications in geosciences, sea-level and coastal studies, and civil and surveying engineering. DOI: 10.1061/(ASCE)SU.1943-5428.0000390. © 2021 American Society of Civil Engineers. Author keywords: Autoregressive model; Effective sample size; Global navigation satellite system (GNSS); Linear trend; Site velocity; Sea-level rise; Uncertainty; 95% confidence interval.

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GNSS衍生站点速度的95%置信区间

根据全球导航卫星系统（GNSS）位置时间序列得出的线性趋势或站点速度通常应用于站点稳定性评估、结构健康监测、海平面上升和海岸淹没研究。对于以每年几毫米的结构或地面变形为目标的严格用户来说，速度的不确定性已经成为一个大问题。GNSS导出的位置时间序列是自相关的。因此，用于计算线性趋势的标准误差的传统方法导致不切实际的小不确定性。本文提出了一种利用有效样本量（Neff）来解释自相关的方法。已经开发了一种稳健的方法来确定现场速度的95%置信区间（95%CI）。研究发现，95%置信区间在时间序列的时间跨度上符合逆幂律关系（垂直方向：95%置信区间1⁄4 5.2T−1.25；东-西或北-南方向：95%可信区间1⁄4 1.8T−1.0）。对于静态GNSS监测项目，建议进行2.5年和4年以上的连续观测，以分别实现水平和垂直现场速度低于1毫米=年的95%置信区间；建议进行7年以上的连续观测，以实现垂直地面移动速率（沉降或隆起）低于0.5毫米=年的95%置信区间。全球导航卫星系统7年时间序列的95%置信区间相当于60年验潮仪观测得出的海平面趋势的95%置信度。通过这项研究开发的方法和经验公式在地球科学、海平面和海岸研究以及土木和测量工程中具有广泛应用的潜力。DOI:10.1061/（ASCE）SU.1943-5428.0000390。©2021美国土木工程师学会。作者关键词：自回归模型；有效样本量；全球导航卫星系统；线性趋势；场地速度；海平面上升；不确定性；95%置信区间。

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来源期刊

Journal of Surveying Engineering 工程技术-工程：土木

CiteScore

3.40

自引率

10.50%

发文量

审稿时长

>12 weeks

期刊介绍： The Journal of Surveying Engineering covers the broad spectrum of surveying and mapping activities encountered in modern practice. It includes traditional areas such as construction surveys, control surveys, photogrammetric mapping, engineering layout, deformation measurements, precise alignment, and boundary surveying. It also includes newer development such as satellite positioning; spatial database design, quality assurance, and information management of geographic information systems; computer applications involving modeling, data structures, algorithms, and information processing; digital mapping, coordinate systems, cartographic representations, and the role of surveying engineering professionals in an information society.