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塞浦路斯永久全球导航卫星系统网络中的线性和非线性变形效应。

Linear and Nonlinear Deformation Effects in the Permanent GNSS Network of Cyprus.

机构信息

Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol 3036, Cyprus.

ERATOSTHENES Centre of Excellence, Limassol 3036, Cyprus.

出版信息

Sensors (Basel). 2020 Mar 22;20(6):1768. doi: 10.3390/s20061768.

DOI:10.3390/s20061768
PMID:32235810
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7146398/
Abstract

Permanent Global Navigation Satellite Systems (GNSS) reference stations are well established as a powerful tool for the estimation of deformation induced by man-made or physical processes. GNSS sensors are successfully used to determine positions and velocities over a specified time period, with unprecedented accuracy, promoting research in many safety-critical areas, such as geophysics and geo-tectonics, tackling problems that torment traditional equipment and providing deformation products with absolute accuracy. Cyprus, being located at the Mediterranean fault, exhibits a very interesting geodynamic regime, which has yet to be investigated thoroughly. Accordingly, this research revolves around the estimation of crustal deformation in Cyprus using GNSS receivers. CYPOS (CYprus POsitioning System), a network of seven permanent GNSS stations has been operating since 2008, under the responsibility of the Department of Lands and Surveys. The continuous flow of positioning data collected over this network, offers the required information to investigate the behavior of the crustal deformation field of Cyprus using GNSS sensors for the first time. This paper presents the results of a multi-year analysis (11/2011-01/2017) of daily GNSS data and provides inferences of linear and nonlinear deforming signals into the position time series of the network stations. Specifically, 3D station velocities and seasonal periodic displacements are jointly estimated and presented via a data stacking approach with respect to the IGb08 reference frame.

摘要

永久性全球导航卫星系统 (GNSS) 参考站已被广泛应用于估计人为或物理过程引起的变形,是一种强大的工具。GNSS 传感器已成功用于在特定时间段内确定位置和速度,具有前所未有的精度,促进了地球物理学和大地构造学等许多安全关键领域的研究,解决了传统设备所困扰的问题,并提供了具有绝对精度的变形产品。

塞浦路斯位于地中海断层上,具有非常有趣的地球动力学状态,但尚未得到彻底研究。因此,这项研究围绕使用 GNSS 接收器估计塞浦路斯的地壳变形。CYPOS(塞浦路斯定位系统)是一个由七个永久性 GNSS 站组成的网络,自 2008 年以来一直在土地和测量部的负责下运行。该网络收集的定位数据连续流动,为首次使用 GNSS 传感器研究塞浦路斯地壳变形场的行为提供了所需的信息。

本文介绍了对多年(2011 年 11 月至 2017 年 1 月)GNSS 数据进行的分析结果,并对网络站位置时间序列中的线性和非线性变形信号进行了推断。具体来说,通过与 IGb08 参考框架相关的数据集方法,联合估计了 3D 站速度和季节性周期位移,并进行了展示。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bf9/7146398/56819d534ddc/sensors-20-01768-g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bf9/7146398/cec91693f0e2/sensors-20-01768-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bf9/7146398/be7480c4f30a/sensors-20-01768-g006.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bf9/7146398/d8aee6352f31/sensors-20-01768-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bf9/7146398/eaeb324b4694/sensors-20-01768-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bf9/7146398/13a42c4ef04b/sensors-20-01768-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bf9/7146398/822bd5bf9152/sensors-20-01768-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bf9/7146398/56819d534ddc/sensors-20-01768-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bf9/7146398/a8e0f62d9751/sensors-20-01768-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bf9/7146398/54c47e39b8b4/sensors-20-01768-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bf9/7146398/9830d1580c56/sensors-20-01768-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bf9/7146398/148eec4e37c5/sensors-20-01768-g004.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bf9/7146398/be7480c4f30a/sensors-20-01768-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bf9/7146398/ec078d559268/sensors-20-01768-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bf9/7146398/d8aee6352f31/sensors-20-01768-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bf9/7146398/eaeb324b4694/sensors-20-01768-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bf9/7146398/13a42c4ef04b/sensors-20-01768-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bf9/7146398/822bd5bf9152/sensors-20-01768-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bf9/7146398/56819d534ddc/sensors-20-01768-g012.jpg

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