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Improving accuracy of local geoid model using machine learning approaches and residuals of GPS/levelling geoid height / Mosbeh R. Kaloop in Survey review, vol 54 n° 387 (November 2022)  

Public [article]  inSurvey review > vol 54 n° 387 (November 2022) . - pp 505 - 518 Titre : Improving accuracy of local geoid model using machine learning approaches and residuals of GPS/levelling geoid height Type de document : Article/Communication Auteurs : Mosbeh R. Kaloop, Auteur ; Samui Pijush, Auteur ; Mostafa Rabah, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 505 - 518 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] apprentissage automatique [Termes IGN] géoïde gravimétrique [Termes IGN] géoïde local [Termes IGN] Koweit [Termes IGN] MNS SRTM [Termes IGN] modèle de géopotentiel [Termes IGN] nivellement avec assistance GPS [Termes IGN] processus gaussien [Termes IGN] régression [Termes IGN] régression multivariée par spline adaptative [Termes IGN] résidu Résumé : (auteur) This study aims to use GPS/Levelling data and machine learning techniques (MLs) to model a high precision local geoid for Kuwait. To improve the accuracy of a local geoid the global geopotential model and local terrain effect should be incorporated. The geoid model was improved based on the modelling of geoid residuals using three MLs. Minimax Probability Machine Regression (MPMR), Gaussian Process Regression (GPR), and Multivariate Adaptive Regression Splines (MARS) MLs were developed for modelling the calculated geoid residuals. The results show that the accuracy of the three MLs was improved compared to previous studies, and the accuracy of the GPR model was better than the other models. The standard deviations of Kuwait geoid undulation determined by GPS/Levelling, gravimetric, and developed GPR models were 1.377, 1.375, 1.375 m, respectively. Thus, the developed GPR model has successfully predicted an accurate geoid height of Kuwait with maximum variation approaches ±0.02 m. Numéro de notice : A2022-829 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2021.1970918 Date de publication en ligne : 27/08/2021 En ligne : https://doi.org/10.1080/00396265.2021.1970918 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102013 [article]

The RTM harmonic correction revisited / R. Klees in Journal of geodesy, vol 96 n° 6 (June 2022)  

Public [article]  inJournal of geodesy > vol 96 n° 6 (June 2022) . - n° 39 Titre : The RTM harmonic correction revisited Type de document : Article/Communication Auteurs : R. Klees, Auteur ; Kurt Seitz, Auteur ; D.C. Slobbe, Auteur Année de publication : 2022 Article en page(s) : n° 39 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] analyse harmonique [Termes IGN] anomalie de pesanteur [Termes IGN] Auvergne [Termes IGN] correction des altitudes [Termes IGN] géoïde local [Termes IGN] harmonique sphérique [Termes IGN] hauteur ellipsoïdale [Termes IGN] méthode des moindres carrés [Termes IGN] modèle de géopotentiel local [Termes IGN] modèle numérique de terrain [Termes IGN] Norvège [Termes IGN] quasi-géoïde [Termes IGN] résidu Résumé : (auteur) In this paper, we derive improved expressions for the harmonic correction to gravity and, for the first time, expressions for the harmonic correction to potential and height anomaly. They need to be applied at stations buried inside the masses to transform internal values into harmonically downward continued values, which are then input to local quasi-geoid modelling using least-squares collocation or least-squares techniques in combination with the remove-compute-restore approach. Harmonic corrections to potential and height anomaly were assumed to be negligible so far resulting in yet unknown quasi-geoid model errors. The improved expressions for the harmonic correction to gravity, and the new expressions for the harmonic correction to potential and height anomaly are used to quantify the approximation errors of the commonly used harmonic correction to gravity and to quantify the magnitude of the harmonic correction to potential and height anomaly. This is done for two test areas with different topographic regimes. One comprises parts of Norway and the North Atlantic where the presence of deep, long, and narrow fjords suggest extreme values for the harmonic correction to potential and height anomaly and corresponding large errors of the commonly used approximation of the harmonic correction to gravity. The other one is located in the Auvergne test area with a moderate topography comprising both flat and hilly areas and therefore may be representative for many areas around the world. For both test areas, two RTM surfaces with different smoothness are computed simulating the use of a medium-resolution and an ultra-high-resolution reference gravity field, respectively. We show that the errors of the commonly used harmonic correction to gravity may be as large as the harmonic correction itself and attain peak values in areas of strong topographic variations of about 100 mGal. Moreover, we show that this correction may introduce long-wavelength biases in the computed quasi-geoid model. Furthermore, we show that the harmonic correction to height anomaly can attain values on the order of a decimetre at some points. Overall, however, the harmonic correction to height anomaly needs to be applied only in areas of strong topographic variations. In flat or hilly areas, it is mostly smaller than one centimetre. Finally, we show that the harmonic corrections increase with increasing smoothness of the RTM surface, which suggests to use a RTM surface with a spatial resolution comparable to the finest scales which can be resolved by the data rather than depending on the resolution of the global geopotential model used to reduce the data. Numéro de notice : A2022-414 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s00190-022-01625-w Date de publication en ligne : 30/05/2022 En ligne : https://doi.org/10.1007/s00190-022-01625-w Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100769 [article]

Proceedings of the 1st Conference of the European Association on Quality Control of Bridges and Structures : EUROSTRUCT 2021. An automated machine learning-based approach for structural novelty detection based on SHM / Nicolas Manzini (2022)  

Public Titre de série : Proceedings of the 1st Conference of the European Association on Quality Control of Bridges and Structures : EUROSTRUCT 2021 Titre : An automated machine learning-based approach for structural novelty detection based on SHM Type de document : Article/Communication Auteurs : Nicolas Manzini, Auteur ; Ndeye Mar, Auteur ; Franziska Schmidt, Auteur ; Jean-François Bercher, Auteur ; André Orcesi, Auteur ; Pierre Marchand, Auteur ; Julien Gazeaux , Auteur ; Christian Thom , Auteur Editeur : Springer Nature Année de publication : 2022 Collection : Lecture Notes in Civil Engineering num. 200 Projets : 2-Pas d'info accessible - article non ouvert / Importance : pp 1180 - 1189 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] apprentissage automatique [Termes IGN] détection d'anomalie [Termes IGN] ouvrage d'art [Termes IGN] pont [Termes IGN] régression multiple [Termes IGN] réseau de capteurs [Termes IGN] résidu [Termes IGN] surveillance d'ouvrage Résumé : (auteur) One major goal of structural health monitoring (SHM) is to detect, and possibly locate, quantify or predict damage on structures. Without detailed knowledge of structural mechanical behavior, data analysis is a complex task and operational monitoring is often limited to the use of more or less arbitrary thresholds. Data-driven techniques, which rely on a statistical analysis of data, have encountered a growing interest over the past two decades. In parallel, SHM is now increasingly considered for several types of structures with the development of low-cost sensors and IoT. In this context, this paper proposes an approach based on multiple automated machine learning-based models for novelty detection and location in monitoring data. This study focuses on the monitoring of large structures with multiple sensors. For each sensor, multiple regression models (based on neural networks) are generated using the same training set, with various input data: internal temperature, environmental conditions, or data from other sensors deployed on the structure. Anomalies are then identified in the dataset based on residuals between model outputs and in situ data. For a given sensor, residuals of all models are then compiled to produce an anomaly indicator. This paper presents some of the results obtained on data acquired from the monitoring of a large concrete bridge. Some anomalies are simulated and added to the dataset to demonstrate the detection performance of the proposed approach. Numéro de notice : C2022-001 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1007/978-3-030-91877-4_134 Date de publication en ligne : 12/12/2021 En ligne : https://doi.org/10.1007/978-3-030-91877-4_134 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99378

Accuracy estimation of site coordinates derived from GNSS-observations by non-classical error theory of measurements / Petro Dvulit in Geodesy and Geodynamics, vol 12 n° 5 (September 2021)  

Public [article]  inGeodesy and Geodynamics > vol 12 n° 5 (September 2021) . - pp 347 - 355 Titre : Accuracy estimation of site coordinates derived from GNSS-observations by non-classical error theory of measurements Type de document : Article/Communication Auteurs : Petro Dvulit, Auteur ; Stepan Savchuk, Auteur ; Iryna Sosonka, Auteur Année de publication : 2021 Article en page(s) : pp 347 - 355 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux [Termes IGN] coordonnées GNSS [Termes IGN] Europe (géographie politique) [Termes IGN] résidu [Termes IGN] série temporelle [Termes IGN] station permanente [Termes IGN] théorie de la mesure [Termes IGN] théorie des erreurs Résumé : (auteur) The velocities of tectonic plates derived from GNSS time series are regularly used as input data for geophysical models. However, as shown by numerous researches, the coordinates time series contain residual errors of a systematic nature, which can significantly affect the reliability of the obtained velocity estimates. This research shows that using non-classical error theory of measurement (NETM) for processing GNSS time series allows detecting the presence of weak, not removed from GNSS processing, sources of systematic errors. Based on the coordinate time series of selected permanent GNSS stations in Europe, we checked the empirical distributions of errors by the NETM on G. Jeffries' recommendations and on the principles of the theory of hypothesis tests according to Pearson's criterion. It is established that the obtained coordinates time series of GNSS-stations only partially confirm the hypothesis of their conformity to the normal Gaussian distribution law, and this may be the main reason for their unrepresentative classification. In the future, it is necessary to identify and take into account the causes of residual errors that distort the real distribution of the results of the GNSS time series. Numéro de notice : A2021-946 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1016/j.geog.2021.07.005 Date de publication en ligne : 13/09/2021 En ligne : https://doi.org/10.1016/j.geog.2021.07.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99761 [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)  

Public [article]  inJournal of geophysical research : Solid Earth > vol 126 n° 9 (September 2021) . - n° e2021JB022370 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 océanique Résumé : (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 [article]

Are there detectable common aperiodic displacements at ITRF co-location sites? / Maylis Teyssendier de la Serve (2021)  

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Sea level acceleration under the magnifier / Huseyin Baki Iz in Journal of geodetic science, vol 11 n° 1 (January 2021)  

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An advanced residual error model for tropospheric delay estimation / Szabolcs Rózsa in GPS solutions, Vol 24 n° 4 (October 2020)  

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Comparative analysis of different atmospheric surface pressure models and their impacts on daily ITRF2014 GNSS residual time series / Zhao Li in Journal of geodesy, vol 94 n°4 (April 2020)  

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Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network / Chunping Qiu in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)  

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Sensitivity of GPS tropospheric estimates to mesoscale convective systems in West Africa / Samuel Nahmani in Atmospheric chemistry and physics, vol 19 n° 14 (July 2019)  

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Seasonal pattern in time series of variances of GPS residual errors Anova estimates / Darko Anđić in Geodetski vestnik, vol 63 n° 2 (June - August 2019)  

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An improved robust Kalman filtering strategy for GNSS kinematic positioning considering small cycle slips / Wanke Liu in Advances in space research, vol 63 n° 9 (1 May 2019)  

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Assessing the latest performance of Galileo-only PPP and the contribution of Galileo to Multi-GNSS PPP / Fengyu Xiu in Advances in space research, vol 63 n° 9 (1 May 2019)  

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Automatic building extraction from high-resolution aerial images and LiDAR data using gated residual refinement network / Jianfeng Huang in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

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