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Auteur Alain Demoulin |
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Impact of offsets on assessing the low-frequency stochastic properties of geodetic time series / Kevin Gobron in Journal of geodesy, vol 96 n° 7 (July 2022)
[article]
Titre : Impact of offsets on assessing the low-frequency stochastic properties of geodetic time series Type de document : Article/Communication Auteurs : Kevin Gobron, Auteur ; Paul Rebischung , Auteur ; Olivier de Viron, Auteur ; Alain Demoulin, Auteur ; Michel Van Camp, Auteur Année de publication : 2022 Projets : 3-projet - voir note / Article en page(s) : n° 46 Note générale : bibliographie
This study has been financially supported by the Direction Générale de l’Armement (DGA), the Nouvelle-Aquitaine region, and the Centre National des Etudes Spatiales (CNES) as an application of the geodesy missions. This research was also supported by the Brain LASUGEO project entitled “monitoring LAnd SUbsidence caused by Groundwater exploitation through gEOdetic measurements” funded by the Belgian Sciences Policy.Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] analyse de variance
[Termes IGN] bruit blanc
[Termes IGN] fréquence
[Termes IGN] méthode du maximum de vraisemblance (estimation)
[Termes IGN] modèle de Gauss-Markov
[Termes IGN] modèle stochastique
[Termes IGN] série temporelle
[Termes IGN] vitesseRésumé : (auteur) Understanding and modelling the properties of the stochastic variations in geodetic time series is crucial to obtain realistic uncertainties for deterministic parameters, e.g., long-term velocities, and helpful in characterizing non-modelled processes. With the increasing span of geodetic time series, it is expected that additional observations would help better understand the low-frequency properties of these stochastic variations. In the meantime, recent studies evidenced that the choice of the functional model for the time series biases the assessment of these low-frequency stochastic properties. In particular, frequent offsets in position time series can hinder the evaluation of the noise level at low frequencies and prevent the detection of possible random-walk-type variability. This study investigates the ability of the Maximum Likelihood Estimation (MLE) method to correctly retrieve low-frequency stochastic properties of geodetic time series in the presence of frequent offsets. We show that part of the influence of offsets reported by previous studies results from the MLE method estimation biases. These biases occur even when all offset epochs are correctly identified and accounted for in the trajectory model. They can cause a dramatic underestimation of deterministic parameter uncertainties. We show that one can avoid biases using the Restricted Maximum Likelihood Estimation (RMLE) method. Yet, even when using the RMLE method or equivalent, adding offsets to the trajectory model inevitably blurs the estimated low-frequency properties of geodetic time series by increasing low-frequency stochastic parameter uncertainties more than other stochastic parameters. Numéro de notice : A2022-519 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-022-01634-9 Date de publication en ligne : 29/06/2022 En ligne : https://doi.org/10.1007/s00190-022-01634-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101072
in Journal of geodesy > vol 96 n° 7 (July 2022) . - n° 46[article]Influence of aperiodic non-tidal atmospheric and oceanic loading deformations on the stochastic properties of global GNSS vertical land motion time series / Kevin Gobron in Journal of geophysical research : Solid Earth, vol 126 n° 9 (September 2021)
[article]
Titre : Influence of aperiodic non-tidal atmospheric and oceanic loading deformations on the stochastic properties of global GNSS vertical land motion time series Type de document : Article/Communication Auteurs : Kevin Gobron, Auteur ; Paul Rebischung , Auteur ; Michel Van Camp, Auteur ; Alain Demoulin, Auteur ; Olivier de Viron, Auteur Année de publication : 2021 Projets : 3-projet - voir note / Article en page(s) : n° e2021JB022370 Note générale : bibliographie
This study has been financially supported by the Direction Générale de l’Armement (DGA), the Nouvelle-Aquitaine region, and the Centre National des Etudes Spatiales (CNES) as an application of the geodesy missions. This research was also supported by the Brain LASUGEO project entitled ”monitoring LAnd SUbsidence caused by Groundwater exploitation through gEOdetic measurements” funded by the Belgian Sciences Policy. This is IPGP contribution number 4214.Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] coordonnées GNSS
[Termes IGN] déformation verticale de la croute terrestre
[Termes IGN] erreur systématique
[Termes IGN] résidu
[Termes IGN] série temporelle
[Termes IGN] station permanente
[Termes IGN] surcharge atmosphérique
[Termes IGN] surcharge océaniqueRésumé : (auteur) Monitoring vertical land motions (VLMs) at the level of 0.1 mm/yr remains one of the most challenging scientific applications of global navigation satellite systems (GNSS). Such small rates of change can result from climatic and tectonic phenomena, and their detection is important to many solid Earth-related studies, including the prediction of coastal sea-level change and the understanding of intraplate deformation. Reaching a level of precision allowing to detect such small signals requires a thorough understanding of the stochastic variability in GNSS VLM time series. This paper investigates how the aperiodic part of non-tidal atmospheric and oceanic loading (NTAOL) deformations influences the stochastic properties of VLM time series. Using the time series of over 10,000 stations, we describe the impact of correcting for NTAOL deformation on 5 complementary metrics, namely: the repeatability of position residuals, the power-spectrum of position residuals, the estimated time-correlation properties, the corresponding velocity uncertainties, and the spatial correlation of the residuals. We show that NTAOL deformations cause a latitude-dependent bias in white noise plus power-law model parameter estimates. This bias is significantly mitigated when correcting for NTAOL deformation, which reduces velocity uncertainties at high latitudes by 70%. Therefore, removing NTAOL deformation before the statistical analysis of VLM time series might help to detect subtle VLM signals in these areas. Our spatial correlation analysis also reveals a seasonality in the spatial correlation of the residuals, which is reduced after removing NTAOL deformation, confirming that NTAOL is a clear source of common-mode errors in GNSS VLM time series. Numéro de notice : A2021-783 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Autre URL associée : vers HAL Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1029/2021JB022370 Date de publication en ligne : 01/09/2021 En ligne : https://doi.org/10.1029/2021JB022370 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98954
in Journal of geophysical research : Solid Earth > vol 126 n° 9 (September 2021) . - n° e2021JB022370[article]