Analysis of Seasonal Signal in GPS Short-Baseline Time Series
Proper modeling of seasonal signals and their quantitative analysis are of interest in geoscience applications, which are based on position time series of permanent GPS stations. Seasonal signals in GPS short-baseline ( 5 m) or type of the monument. For comparison, we also processed an approximately...
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| Veröffentlicht in: | Pure and applied geophysics 2018-10, Vol.175 (10), p.3485-3509 |
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| description | Proper modeling of seasonal signals and their quantitative analysis are of interest in geoscience applications, which are based on position time series of permanent GPS stations. Seasonal signals in GPS short-baseline ( 5 m) or type of the monument. For comparison, we also processed an approximately zero baseline with a distance of 0.4 mm in the horizontal direction are observed in five short-baselines, and the amplitudes exceed 1 mm in four of them. These horizontal seasonal signals are likely related to the propagation of daily/sub-daily TEM displacement or other signals related to the site environment. Mismodeling of the tropospheric delay may also introduce spurious seasonal signals with annual amplitudes of ~ 5 and ~ 2 mm, respectively, for two short-baselines with elevation differences grea |
| doi_str_mv | 10.1007/s00024-018-1871-4 |
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Seasonal signals in GPS short-baseline (< 2 km) time series, if they exist, are mainly related to site-specific effects, such as thermal expansion of the monument (TEM). However, only part of the seasonal signal can be explained by known factors due to the limited data span, the GPS processing strategy and/or the adoption of an imperfect TEM model. In this paper, to better understand the seasonal signal in GPS short-baseline time series, we adopted and processed six different short-baselines with data span that varies from 2 to 14 years and baseline length that varies from 6 to 1100 m. To avoid seasonal signals that are overwhelmed by noise, each of the station pairs is chosen with significant differences in their height (> 5 m) or type of the monument. For comparison, we also processed an approximately zero baseline with a distance of < 1 m and identical monuments. The daily solutions show that there are apparent annual signals with annual amplitude of ~ 1 mm (maximum amplitude of 1.86 ± 0.17 mm) on almost all of the components, which are consistent with the results from previous studies. Semi-annual signal with a maximum amplitude of 0.97 ± 0.25 mm is also present. The analysis of time-correlated noise indicates that instead of flicker (FL) or random walk (RW) noise, band-pass-filtered (BP) noise is valid for approximately 40% of the baseline components, and another 20% of the components can be best modeled by a combination of the first-order Gauss–Markov (FOGM) process plus white noise (WN). The TEM displacements are then modeled by considering the monument height of the building structure beneath the GPS antenna. The median contributions of TEM to the annual amplitude in the vertical direction are 84% and 46% with and without additional parts of the monument, respectively. Obvious annual signals with amplitude > 0.4 mm in the horizontal direction are observed in five short-baselines, and the amplitudes exceed 1 mm in four of them. These horizontal seasonal signals are likely related to the propagation of daily/sub-daily TEM displacement or other signals related to the site environment. Mismodeling of the tropospheric delay may also introduce spurious seasonal signals with annual amplitudes of ~ 5 and ~ 2 mm, respectively, for two short-baselines with elevation differences greater than 100 m. The results suggest that the monument height of the additional part of a typical GPS station should be considered when estimating the TEM displacement and that the tropospheric delay should be modeled cautiously, especially with station pairs with apparent elevation differences. The scheme adopted in this paper is expected to explicate more seasonal signals in GPS coordinate time series, particularly in the vertical direction.</description><identifier>ISSN: 0033-4553</identifier><identifier>EISSN: 1420-9136</identifier><identifier>DOI: 10.1007/s00024-018-1871-4</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Amplitude ; Amplitudes ; Components ; Correlation analysis ; Data processing ; Delay ; Direction ; Displacement ; Earth and Environmental Science ; Earth Sciences ; Elevation ; Flicker ; Gaussian process ; Geophysics/Geodesy ; Global positioning systems ; GPS ; Height ; Horizontal orientation ; Markov chains ; Modelling ; Noise ; Quantitative analysis ; Random walk ; Satellite navigation systems ; Signal processing ; Solutions ; Thermal expansion ; Time correlation functions ; Time series ; Troposphere ; White noise</subject><ispartof>Pure and applied geophysics, 2018-10, Vol.175 (10), p.3485-3509</ispartof><rights>Springer International Publishing AG, part of Springer Nature 2018</rights><rights>Pure and Applied Geophysics is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a339t-535bc9bf4c3b01a4e275c4e68b377a0812e1dce9df48ab6b51de7d571c59b86e3</citedby><cites>FETCH-LOGICAL-a339t-535bc9bf4c3b01a4e275c4e68b377a0812e1dce9df48ab6b51de7d571c59b86e3</cites><orcidid>0000-0002-3267-9682</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00024-018-1871-4$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00024-018-1871-4$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Wang, Kaihua</creatorcontrib><creatorcontrib>Jiang, Weiping</creatorcontrib><creatorcontrib>Chen, Hua</creatorcontrib><creatorcontrib>An, Xiangdong</creatorcontrib><creatorcontrib>Zhou, Xiaohui</creatorcontrib><creatorcontrib>Yuan, Peng</creatorcontrib><creatorcontrib>Chen, Qusen</creatorcontrib><title>Analysis of Seasonal Signal in GPS Short-Baseline Time Series</title><title>Pure and applied geophysics</title><addtitle>Pure Appl. Geophys</addtitle><description>Proper modeling of seasonal signals and their quantitative analysis are of interest in geoscience applications, which are based on position time series of permanent GPS stations. Seasonal signals in GPS short-baseline (< 2 km) time series, if they exist, are mainly related to site-specific effects, such as thermal expansion of the monument (TEM). However, only part of the seasonal signal can be explained by known factors due to the limited data span, the GPS processing strategy and/or the adoption of an imperfect TEM model. In this paper, to better understand the seasonal signal in GPS short-baseline time series, we adopted and processed six different short-baselines with data span that varies from 2 to 14 years and baseline length that varies from 6 to 1100 m. To avoid seasonal signals that are overwhelmed by noise, each of the station pairs is chosen with significant differences in their height (> 5 m) or type of the monument. For comparison, we also processed an approximately zero baseline with a distance of < 1 m and identical monuments. The daily solutions show that there are apparent annual signals with annual amplitude of ~ 1 mm (maximum amplitude of 1.86 ± 0.17 mm) on almost all of the components, which are consistent with the results from previous studies. Semi-annual signal with a maximum amplitude of 0.97 ± 0.25 mm is also present. The analysis of time-correlated noise indicates that instead of flicker (FL) or random walk (RW) noise, band-pass-filtered (BP) noise is valid for approximately 40% of the baseline components, and another 20% of the components can be best modeled by a combination of the first-order Gauss–Markov (FOGM) process plus white noise (WN). The TEM displacements are then modeled by considering the monument height of the building structure beneath the GPS antenna. The median contributions of TEM to the annual amplitude in the vertical direction are 84% and 46% with and without additional parts of the monument, respectively. Obvious annual signals with amplitude > 0.4 mm in the horizontal direction are observed in five short-baselines, and the amplitudes exceed 1 mm in four of them. These horizontal seasonal signals are likely related to the propagation of daily/sub-daily TEM displacement or other signals related to the site environment. Mismodeling of the tropospheric delay may also introduce spurious seasonal signals with annual amplitudes of ~ 5 and ~ 2 mm, respectively, for two short-baselines with elevation differences greater than 100 m. The results suggest that the monument height of the additional part of a typical GPS station should be considered when estimating the TEM displacement and that the tropospheric delay should be modeled cautiously, especially with station pairs with apparent elevation differences. The scheme adopted in this paper is expected to explicate more seasonal signals in GPS coordinate time series, particularly in the vertical direction.</description><subject>Amplitude</subject><subject>Amplitudes</subject><subject>Components</subject><subject>Correlation analysis</subject><subject>Data processing</subject><subject>Delay</subject><subject>Direction</subject><subject>Displacement</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Elevation</subject><subject>Flicker</subject><subject>Gaussian process</subject><subject>Geophysics/Geodesy</subject><subject>Global positioning systems</subject><subject>GPS</subject><subject>Height</subject><subject>Horizontal orientation</subject><subject>Markov chains</subject><subject>Modelling</subject><subject>Noise</subject><subject>Quantitative analysis</subject><subject>Random walk</subject><subject>Satellite navigation systems</subject><subject>Signal processing</subject><subject>Solutions</subject><subject>Thermal expansion</subject><subject>Time correlation functions</subject><subject>Time series</subject><subject>Troposphere</subject><subject>White noise</subject><issn>0033-4553</issn><issn>1420-9136</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kE1Lw0AURQdRsFZ_gLuA69H35iOTWbioRatQUEhdD5Pkpaa0SZ1JF_33pkRw5ery4NzL4zB2i3CPAOYhAoBQHDDjmBnk6oxNUAngFmV6ziYAUnKltbxkVzFuANAYbSfscdb67TE2MenqJCcfu-FO8mZ9iqZNFh95kn91oedPPtK2aSlZNTsa0NBQvGYXtd9GuvnNKft8eV7NX_nyffE2ny25l9L2XEtdlLaoVSkLQK9IGF0qSrNCGuMhQ0FYlWSrWmW-SAuNFZlKGyy1LbKU5JTdjbv70H0fKPZu0x3C8GJ0AoQVFjWagcKRKkMXY6Da7UOz8-HoENzJkhstucGSO1lyauiIsRMHtl1T-Fv-v_QDNDxogw</recordid><startdate>20181001</startdate><enddate>20181001</enddate><creator>Wang, Kaihua</creator><creator>Jiang, Weiping</creator><creator>Chen, Hua</creator><creator>An, Xiangdong</creator><creator>Zhou, Xiaohui</creator><creator>Yuan, Peng</creator><creator>Chen, Qusen</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TG</scope><scope>7UA</scope><scope>7XB</scope><scope>88I</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>H8D</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>L7M</scope><scope>M2P</scope><scope>P5Z</scope><scope>P62</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0002-3267-9682</orcidid></search><sort><creationdate>20181001</creationdate><title>Analysis of Seasonal Signal in GPS Short-Baseline Time Series</title><author>Wang, Kaihua ; Jiang, Weiping ; Chen, Hua ; An, Xiangdong ; Zhou, Xiaohui ; Yuan, Peng ; Chen, Qusen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a339t-535bc9bf4c3b01a4e275c4e68b377a0812e1dce9df48ab6b51de7d571c59b86e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Amplitude</topic><topic>Amplitudes</topic><topic>Components</topic><topic>Correlation analysis</topic><topic>Data processing</topic><topic>Delay</topic><topic>Direction</topic><topic>Displacement</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Elevation</topic><topic>Flicker</topic><topic>Gaussian process</topic><topic>Geophysics/Geodesy</topic><topic>Global positioning systems</topic><topic>GPS</topic><topic>Height</topic><topic>Horizontal orientation</topic><topic>Markov chains</topic><topic>Modelling</topic><topic>Noise</topic><topic>Quantitative analysis</topic><topic>Random walk</topic><topic>Satellite navigation systems</topic><topic>Signal processing</topic><topic>Solutions</topic><topic>Thermal expansion</topic><topic>Time correlation functions</topic><topic>Time series</topic><topic>Troposphere</topic><topic>White noise</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Kaihua</creatorcontrib><creatorcontrib>Jiang, Weiping</creatorcontrib><creatorcontrib>Chen, Hua</creatorcontrib><creatorcontrib>An, Xiangdong</creatorcontrib><creatorcontrib>Zhou, Xiaohui</creatorcontrib><creatorcontrib>Yuan, Peng</creatorcontrib><creatorcontrib>Chen, Qusen</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ProQuest Central Student</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><jtitle>Pure and applied geophysics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Kaihua</au><au>Jiang, Weiping</au><au>Chen, Hua</au><au>An, Xiangdong</au><au>Zhou, Xiaohui</au><au>Yuan, Peng</au><au>Chen, Qusen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Analysis of Seasonal Signal in GPS Short-Baseline Time Series</atitle><jtitle>Pure and applied geophysics</jtitle><stitle>Pure Appl. Geophys</stitle><date>2018-10-01</date><risdate>2018</risdate><volume>175</volume><issue>10</issue><spage>3485</spage><epage>3509</epage><pages>3485-3509</pages><issn>0033-4553</issn><eissn>1420-9136</eissn><abstract>Proper modeling of seasonal signals and their quantitative analysis are of interest in geoscience applications, which are based on position time series of permanent GPS stations. Seasonal signals in GPS short-baseline (< 2 km) time series, if they exist, are mainly related to site-specific effects, such as thermal expansion of the monument (TEM). However, only part of the seasonal signal can be explained by known factors due to the limited data span, the GPS processing strategy and/or the adoption of an imperfect TEM model. In this paper, to better understand the seasonal signal in GPS short-baseline time series, we adopted and processed six different short-baselines with data span that varies from 2 to 14 years and baseline length that varies from 6 to 1100 m. To avoid seasonal signals that are overwhelmed by noise, each of the station pairs is chosen with significant differences in their height (> 5 m) or type of the monument. For comparison, we also processed an approximately zero baseline with a distance of < 1 m and identical monuments. The daily solutions show that there are apparent annual signals with annual amplitude of ~ 1 mm (maximum amplitude of 1.86 ± 0.17 mm) on almost all of the components, which are consistent with the results from previous studies. Semi-annual signal with a maximum amplitude of 0.97 ± 0.25 mm is also present. The analysis of time-correlated noise indicates that instead of flicker (FL) or random walk (RW) noise, band-pass-filtered (BP) noise is valid for approximately 40% of the baseline components, and another 20% of the components can be best modeled by a combination of the first-order Gauss–Markov (FOGM) process plus white noise (WN). The TEM displacements are then modeled by considering the monument height of the building structure beneath the GPS antenna. The median contributions of TEM to the annual amplitude in the vertical direction are 84% and 46% with and without additional parts of the monument, respectively. Obvious annual signals with amplitude > 0.4 mm in the horizontal direction are observed in five short-baselines, and the amplitudes exceed 1 mm in four of them. These horizontal seasonal signals are likely related to the propagation of daily/sub-daily TEM displacement or other signals related to the site environment. Mismodeling of the tropospheric delay may also introduce spurious seasonal signals with annual amplitudes of ~ 5 and ~ 2 mm, respectively, for two short-baselines with elevation differences greater than 100 m. The results suggest that the monument height of the additional part of a typical GPS station should be considered when estimating the TEM displacement and that the tropospheric delay should be modeled cautiously, especially with station pairs with apparent elevation differences. The scheme adopted in this paper is expected to explicate more seasonal signals in GPS coordinate time series, particularly in the vertical direction.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1007/s00024-018-1871-4</doi><tpages>25</tpages><orcidid>https://orcid.org/0000-0002-3267-9682</orcidid></addata></record> |
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| subjects | Amplitude Amplitudes Components Correlation analysis Data processing Delay Direction Displacement Earth and Environmental Science Earth Sciences Elevation Flicker Gaussian process Geophysics/Geodesy Global positioning systems GPS Height Horizontal orientation Markov chains Modelling Noise Quantitative analysis Random walk Satellite navigation systems Signal processing Solutions Thermal expansion Time correlation functions Time series Troposphere White noise |
| title | Analysis of Seasonal Signal in GPS Short-Baseline Time Series |
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