Spatial Variations of Stochastic Noise Properties in GPS Time Series

The noise in position time series of 568 GPS (Global Position System) stations across North America with an observation span of ten years has been investigated using solutions from two processing centers, namely, the Pacific Northwest Geodetic Array (PANGA) and New Mexico Tech (NMT). It is well know...

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Autores principales:	Xiaoxing He, Machiel Simon Bos, Jean-Philippe Montillet, Rui Fernandes, Tim Melbourne, Weiping Jiang, Wudong Li
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	GPS geodetic time series stochastic noise episodic tremor and slip monuments tectonic rate Science Q
Acceso en línea:	https://doaj.org/article/6e5c7490c08643f18879a717772d7827
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spelling	oai:doaj.org-article:6e5c7490c08643f18879a717772d78272021-11-25T18:54:00ZSpatial Variations of Stochastic Noise Properties in GPS Time Series10.3390/rs132245342072-4292https://doaj.org/article/6e5c7490c08643f18879a717772d78272021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4534https://doaj.org/toc/2072-4292The noise in position time series of 568 GPS (Global Position System) stations across North America with an observation span of ten years has been investigated using solutions from two processing centers, namely, the Pacific Northwest Geodetic Array (PANGA) and New Mexico Tech (NMT). It is well known that in the frequency domain, the noise exhibits a power-law behavior with a spectral index of around −1. By fitting various noise models to the observations and selecting the most likely one, we demonstrate that the spectral index in some regions flattens to zero at long periods while in other regions it is closer to −2. This has a significant impact on the estimated linear rate since flattening of the power spectral density roughly halves the uncertainty of the estimated tectonic rate while random walk doubles it. Our noise model selection is based on the highest log-likelihood value, and the Akaike and Bayesian Information Criteria to reduce the probability of over selecting noise models with many parameters. Finally, the noise in position time series also depends on the stability of the monument on which the GPS antenna is installed. We corroborate previous results that deep-drilled brace monuments produce smaller uncertainties than concrete piers. However, if at each site the optimal noise model is used, the differences become smaller due to the fact that many concrete piers are located in tectonic/seismic quiet areas. Thus, for the predicted performance of a new GPS network, not only the type of monument but also the noise properties of the region need to be taken into account.Xiaoxing HeMachiel Simon BosJean-Philippe MontilletRui FernandesTim MelbourneWeiping JiangWudong LiMDPI AGarticleGPSgeodetic time seriesstochastic noiseepisodic tremor and slipmonumentstectonic rateScienceQENRemote Sensing, Vol 13, Iss 4534, p 4534 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	GPS geodetic time series stochastic noise episodic tremor and slip monuments tectonic rate Science Q
spellingShingle	GPS geodetic time series stochastic noise episodic tremor and slip monuments tectonic rate Science Q Xiaoxing He Machiel Simon Bos Jean-Philippe Montillet Rui Fernandes Tim Melbourne Weiping Jiang Wudong Li Spatial Variations of Stochastic Noise Properties in GPS Time Series
description	The noise in position time series of 568 GPS (Global Position System) stations across North America with an observation span of ten years has been investigated using solutions from two processing centers, namely, the Pacific Northwest Geodetic Array (PANGA) and New Mexico Tech (NMT). It is well known that in the frequency domain, the noise exhibits a power-law behavior with a spectral index of around −1. By fitting various noise models to the observations and selecting the most likely one, we demonstrate that the spectral index in some regions flattens to zero at long periods while in other regions it is closer to −2. This has a significant impact on the estimated linear rate since flattening of the power spectral density roughly halves the uncertainty of the estimated tectonic rate while random walk doubles it. Our noise model selection is based on the highest log-likelihood value, and the Akaike and Bayesian Information Criteria to reduce the probability of over selecting noise models with many parameters. Finally, the noise in position time series also depends on the stability of the monument on which the GPS antenna is installed. We corroborate previous results that deep-drilled brace monuments produce smaller uncertainties than concrete piers. However, if at each site the optimal noise model is used, the differences become smaller due to the fact that many concrete piers are located in tectonic/seismic quiet areas. Thus, for the predicted performance of a new GPS network, not only the type of monument but also the noise properties of the region need to be taken into account.
format	article
author	Xiaoxing He Machiel Simon Bos Jean-Philippe Montillet Rui Fernandes Tim Melbourne Weiping Jiang Wudong Li
author_facet	Xiaoxing He Machiel Simon Bos Jean-Philippe Montillet Rui Fernandes Tim Melbourne Weiping Jiang Wudong Li
author_sort	Xiaoxing He
title	Spatial Variations of Stochastic Noise Properties in GPS Time Series
title_short	Spatial Variations of Stochastic Noise Properties in GPS Time Series
title_full	Spatial Variations of Stochastic Noise Properties in GPS Time Series
title_fullStr	Spatial Variations of Stochastic Noise Properties in GPS Time Series
title_full_unstemmed	Spatial Variations of Stochastic Noise Properties in GPS Time Series
title_sort	spatial variations of stochastic noise properties in gps time series
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/6e5c7490c08643f18879a717772d7827
work_keys_str_mv	AT xiaoxinghe spatialvariationsofstochasticnoisepropertiesingpstimeseries AT machielsimonbos spatialvariationsofstochasticnoisepropertiesingpstimeseries AT jeanphilippemontillet spatialvariationsofstochasticnoisepropertiesingpstimeseries AT ruifernandes spatialvariationsofstochasticnoisepropertiesingpstimeseries AT timmelbourne spatialvariationsofstochasticnoisepropertiesingpstimeseries AT weipingjiang spatialvariationsofstochasticnoisepropertiesingpstimeseries AT wudongli spatialvariationsofstochasticnoisepropertiesingpstimeseries
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Spatial Variations of Stochastic Noise Properties in GPS Time Series

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