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Empirical comparison between stochastic and deterministic modifiers over the French Auvergne geoid computation test-bed / Ropesh Goyal in Survey review, vol 54 n° 382 (January 2022)  

Public [article]  inSurvey review > vol 54 n° 382 (January 2022) . - pp 57 - 69 Titre : Empirical comparison between stochastic and deterministic modifiers over the French Auvergne geoid computation test-bed Type de document : Article/Communication Auteurs : Ropesh Goyal, Auteur ; Jonas Ågren, Auteur ; Will E. Featherstone, Auteur Année de publication : 2022 Article en page(s) : pp 57 - 69 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] analyse comparative [Termes IGN] Auvergne [Termes IGN] géoïde local [Termes IGN] harmonique sphérique [Termes IGN] méthode déterministe [Termes IGN] NGF-IGN69 [Termes IGN] nivellement par GPS [Termes IGN] processus stochastique [Termes IGN] quasi-géoïde Résumé : (auteur) Since 2006, several different groups have computed geoid and/or quasigeoid (quasi/geoid) models for the Auvergne test area in central France using various approaches. In this contribution, we compute and compare quasigeoid models for Auvergne using Curtin University of Technology’s and the Swedish Royal Institute of Technology’s approaches. These approaches differ in many ways, such as their treatment of the input data, choice of type of spherical harmonic model (combined or satellite-only), form and sequence of correction terms applied, and different modified Stokes’s kernels (deterministic or stochastic). We have also compared our results with most of the previously reported studies over Auvergne in order to seek any improvements with respect to time [exceptions are when different subsets of data have been used]. All studies considered here compare the computed quasigeoid models with the same 75 GPS-levelling heights over Auvergne. The standard deviation for almost all of the computations (without any fitting) is of the order of 30–40 mm, so there is not yet any clear indication whether any approach is necessarily better than any other nor improving over time. We also recommend more standardisation on the presentation of quasi/geoid comparisons with GPS-levelling data so that results from different approaches over the same areas can be compared more objectively. Numéro de notice : A2022-111 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2021.1871821 En ligne : https://doi.org/10.1080/00396265.2021.1871821 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99628 [article]

Evaluation of global geopotential models: a case study for India / Ropesh Goyal in Survey review, vol 51 n° 368 (September 2019)  

Public [article]  inSurvey review > vol 51 n° 368 (September 2019) . - pp 402 - 412 Titre : Evaluation of global geopotential models: a case study for India Type de document : Article/Communication Auteurs : Ropesh Goyal, Auteur ; Onkar Dikshit, Auteur ; Nagarajan Balasubramania, Auteur Année de publication : 2019 Article en page(s) : pp 402 - 412 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] analyse multicritère [Termes IGN] Inde [Termes IGN] Matlab [Termes IGN] modèle de géopotentiel [Termes IGN] modèle de géopotentiel local [Termes IGN] réseau altimétrique national Résumé : (Auteur) This paper aims to identify most suitable global geopotential model (GGM) for India, by comparing 15 GGMs developed through 1996 to 2017. The GGM derived geoid undulation values are compared with the geometrical undulation values obtained from GNSS/levelling data on Indian vertical datum. A correction term is added to the computed GGM derived geoid undulation value after fitting three-, four-, five- and seven-parameter models to account for bias and tilt between local geometric Indian vertical datum and global gravimetric vertical datum. The results indicate that EGM2008 model is the best GGM available for India with root-mean-square error (RMSE) of 0.28 m, without model fitting. However, after considering the systematic errors in the two datums with seven-parameter model, GECO GGM has shown significantly better results with RMSE of 0.19 m for India. Numéro de notice : A2019-366 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2018.1468537 Date de publication en ligne : 11/05/2018 En ligne : https://doi.org/10.1080/00396265.2018.1468537 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93472 [article]