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Establishing a high-precision real-time ZTD model of China with GPS and ERA5 historical data and its application in PPP / Pengfei Xia in GPS solutions, vol 27 n° 1 (January 2023)  

Public [article]  inGPS solutions > vol 27 n° 1 (January 2023) . - n° 2 Titre : Establishing a high-precision real-time ZTD model of China with GPS and ERA5 historical data and its application in PPP Type de document : Article/Communication Auteurs : Pengfei Xia, Auteur ; Mengxiang Tong, Auteur ; Shirong Ye, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 2 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] Chine [Termes IGN] correction troposphérique [Termes IGN] données météorologiques [Termes IGN] grille [Termes IGN] positionnement ponctuel précis [Termes IGN] retard troposphérique zénithal [Termes IGN] série de Fourier [Termes IGN] série temporelle [Termes IGN] station GNSS [Termes IGN] temps de convergence [Termes IGN] temps réel [Termes IGN] variation diurne Résumé : (auteur) A high-precision real-time troposphere model is constructed by combining ground-based GNSS observation data and the latest European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5). First, the zenith tropospheric delay (ZTD) is extracted in real time with high accuracy by combining the data of more than 500 GNSS stations in the Crustal Movement Observation Network of China (CMONOC) and national reference station network (NRSN); second, a grid model of the elevation normalization model (ENM) in China using ERA5 data is constructed, which takes into account the annual, semiannual and daily cycles. The ZTD estimated by GNSS stations at different heights based on precise point positioning (PPP) is normalized to a uniform height based on ENM; in addition, the optimal smoothing factors of the Gauss distance weighting function in different seasons are determined based on ERA5, which contributes to improved accuracy of ZTD interpolated from GNSS-derived ZTD to ZTD at grid points; finally, a real-time 1° × 1°ZTD grid model of China is created; the broadcast interval is extended to 6 min from few seconds. The new ZTD model has been evaluated using the data of 15 GNSS stations in China in 2020. The test results show that the new ZTD model deviates from the reference value with a mean value better than − 0.09 cm and RMSE, better than 1.44 cm compared with the ZTD estimated by post-processing GNSS, while the mean value of the deviation is -0.13 cm, and the RMSE is approximately 3.11 cm compared with radiosonde-derived ZTD. The new ZTD grid model can be used to enhance GNSS/PPP. Two weeks of GNSS observations, one week in winter and another in summer, were randomly collected for PPP processing. The statistical results show the convergence time in the vertical directions is shortened by 37.4% and 38.6% at the 95% and 68% confidence levels after ZTD constraints are applied to the float PPP solution, respectively. Numéro de notice : A2023-004 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-022-01338-9 Date de publication en ligne : 07/10/2022 En ligne : https://doi.org/10.1007/s10291-022-01338-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101874 [article]

Bayesian inference on the initiation phase of the 2014 Iquique, Chile, earthquake / Cédric Twardzik in Earth and planetary science letters, vol 600 (15 December 2022)  

Public [article]  inEarth and planetary science letters > vol 600 (15 December 2022) . - n° 117835 Titre : Bayesian inference on the initiation phase of the 2014 Iquique, Chile, earthquake Type de document : Article/Communication Auteurs : Cédric Twardzik, Auteur ; Zacharie Duputel, Auteur ; Romain Jolivet, Auteur ; Emilie Klein, Auteur ; Paul Rebischung , Auteur Année de publication : 2022 Article en page(s) : n° 117835 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] Chili [Termes IGN] coordonnées GNSS [Termes IGN] effondrement de terrain [Termes IGN] inférence [Termes IGN] matrice de covariance [Termes IGN] séisme [Termes IGN] série temporelle [Termes IGN] sismologie Résumé : (auteur) We investigate the initiation phase of the 2014 Mw8.1 Iquique earthquake in northern Chile. In particular, we focus on the month preceding the mainshock, a time period known to exhibit an intensification of the seismic and aseismic activity in the region. The goal is to estimate the time-evolution and partitioning of seismic and aseismic slip during the preparatory phase of the mainshock. To do so, we develop a Bayesian inversion scheme to infer the spatio-temporal evolution of pre-slip from position time-series along with the corresponding uncertainty. To extract the aseismic component to the pre-seismic motion, we correct geodetic observations from the displacement induced by foreshocks. We find that aseismic slip accounts for ∼80 percents of the slip budget. That aseismic slip takes the form of a slow-slip events occurring between 20 to 5 days before the future mainshock. This time-evolution is not consistent with self-accelerating fault slip, a model that is often invoked to explain earthquake nucleation. Instead, the slow-slip event seems to have interacted with the foreshock sequence such that the foreshocks contributed to the arrest of aseismic slip. In addition, we observe some evidence of late self-accelerating slip, but associated with large uncertainties, making it difficult to assess its reliability from our observations alone. Numéro de notice : A2022-698 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.epsl.2022.117835 Date de publication en ligne : 26/10/2022 En ligne : https://doi.org/10.1016/j.epsl.2022.117835 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102117 [article]

Machine learning models applied to a GNSS sensor network for automated bridge anomaly detection / Nicolas Manzini in Journal of structural engineering, Vol 148 n° 11 (November 2022)  

Public [article]  inJournal of structural engineering > Vol 148 n° 11 (November 2022) . - n° 3469 Titre : Machine learning models applied to a GNSS sensor network for automated bridge anomaly detection Type de document : Article/Communication Auteurs : Nicolas Manzini, Auteur ; André Orcesi, Auteur ; Christian Thom , Auteur ; Marc-Antoine Brossault, Auteur ; Serge Botton , Auteur ; Miguel Ortiz, Auteur ; John Dumoulin, Auteur Année de publication : 2022 Projets : 2-Pas d'info accessible - article non ouvert / Article en page(s) : n° 3469 Note générale : bibliographie EN ATTENTE DU DOCUMENT Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Topographie [Termes IGN] apprentissage automatique [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] détection d'anomalie [Termes IGN] ouvrage d'art [Termes IGN] pont [Termes IGN] régression [Termes IGN] série temporelle [Termes IGN] surveillance d'ouvrage [Termes IGN] topométrie de précision Résumé : (auteur) Structural health monitoring (SHM) based on global navigation satellite systems (GNSS) is an interesting solution to provide absolute positions at different locations of a structure in a global reference frame. In particular, low-cost GNSS stations for large-scale bridge monitoring have gained increasing attention these last years because recent experiments showed the ability to achieve a subcentimeter accuracy for continuous monitoring with adequate combinations of antennas and receivers. Technical solutions now allow displacement monitoring of long bridges with a cost-effective deployment of GNSS sensing networks. In particular, the redundancy of observations within the GNSS network with various levels of correlations between the GNSS time series makes such monitoring solution a good candidate for anomaly detection based on machine learning models, using several predictive models for each sensor (based on environmental conditions, or other sensors as input data). This strategy is investigated in this paper based on GNSS time series, and an anomaly indicator is proposed to detect and locate anomalous structural behavior. The proposed concepts are applied to a cable-stayed bridge for illustration, and the comparison between multiple tools highlights recurrent neural networks (RNN) as an effective regression tool. Coupling this tool with the proposed anomaly detection strategy enables one to identify and localize both real and simulated anomalies in the considered data set. Numéro de notice : A2022-672 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1061/(ASCE)ST.1943-541X.0003469 En ligne : https://doi.org/10.1061/(ASCE)ST.1943-541X.0003469 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101615 [article]

A determination of the motion based on GNSS observations between 2000 and 2021 using the IGS points in the polar regions / Atinç Pirti in Geodesy and cartography, vol 48 n° 3 (October 2022)  

Public [article]  inGeodesy and cartography > vol 48 n° 3 (October 2022) . - pp 177 - 184 Titre : A determination of the motion based on GNSS observations between 2000 and 2021 using the IGS points in the polar regions Type de document : Article/Communication Auteurs : Atinç Pirti, Auteur Année de publication : 2022 Article en page(s) : pp 177 - 184 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse diachronique [Termes IGN] Antarctique [Termes IGN] Arctique [Termes IGN] données GNSS [Termes IGN] international GPS service for geodynamics [Termes IGN] International Terrestrial Reference Frame [Termes IGN] mouvement du pôle [Termes IGN] réseau géodésique [Termes IGN] série temporelle [Vedettes matières IGN] Traitement de données GNSS Résumé : (auteur) People are fascinated today more than ever by the polar regions of the Earth. One reason for this is that wide expanses of the Arctic and Antarctic have not been explored and are therefore still viewed as frontier regions. Another is that they both have very diverse histories with regard to their origins and ice formation. Their numerous aspects still pose many puzzles for science today. The regions of the Earth designated as polar are those areas located between the North or South Pole and the Arctic or Antarctic Circles, respectively. The northern polar region, called the Arctic, encompasses the Arctic Ocean and a portion of some surrounding land masses. The southern polar region, called the Antarctic, contains the continent of Antarctica and areas of the surrounding Southern Ocean. In this paper three tests (2000, 2010 and 2021) of continuous GNSS data recorded by 8 permanent International GPS Service (IGS) stations in both Polar Regions have been processed by using CSRS-PPP Software for geodetic networks. The results also show that all GNSS provide good visibility with low elevation angles, whereas with high elevation angles, which might be needed due to natural barriers, the GLONASS and other satellites provides the highest number of visible satellites. Consequently, the mean motion of the study area was found approximately 7–15 cm for horizontal components (X–Y) and 6 cm for vertical components (Ellipsoidal Height) on the eight IGS points in the both poles. Numéro de notice : A2022-761 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.3846/gac.2022.14848 Date de publication en ligne : 02/09/2022 En ligne : https://doi.org/10.3846/gac.2022.14848 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101777 [article]

Modelling and prediction of GNSS time series using GBDT, LSTM and SVM machine learning approaches / Wenzong Gao in Journal of geodesy, vol 96 n° 10 (October 2022)  

Public [article]  inJournal of geodesy > vol 96 n° 10 (October 2022) . - n° 71 Titre : Modelling and prediction of GNSS time series using GBDT, LSTM and SVM machine learning approaches Type de document : Article/Communication Auteurs : Wenzong Gao, Auteur ; Zhao Li, Auteur ; Qusen Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 71 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] déformation de la croute terrestre [Termes IGN] données GNSS [Termes IGN] Extreme Gradient Machine [Termes IGN] modèle de simulation [Termes IGN] série temporelle [Vedettes matières IGN] Traitement de données GNSS Résumé : (auteur) Global navigation satellite system (GNSS) site coordinate time series provides essential data for geodynamic and geophysical studies, realisation of a regional or global geodetic reference frames, and crustal deformation research. The coordinate time series has been conventionally modelled by least squares (LS) fitting with harmonic functions, alongside many other analysis methods. As a key limitation, the traditional modelling approaches simply use the functions of time variable, despite good knowledge of various underlying physical mechanisms responsible for the site displacements. This paper examines the use of machine learning (ML) models to reflect the effects or residential effects of physical variables related to Sun and the Moon ephemerides, polar motion, temperature, atmospheric pressure, and hydrology on the site displacements. To form the ML problem, these variables are constructed as the input vector of each ML training sample, while the vertical displacement of a GNSS site is regarded as the output value. In the evaluation experiments, three ML approaches, namely the gradient boosting decision tree (GBDT) approach, long short-term memory (LSTM) approach, and support vector machine (SVM) approach, are introduced and evaluated with the time series datasets collected from 9 GNSS sites over the period of 13 years. The results indicate that all three approaches achieve similar fitting precision in the range of 3–5 mm in the vertical displacement component, which is an improvement in over 30% with respect to the traditional LS fitting precision in the range of 4–7 mm. The prediction of the vertical time series with the three ML approaches shows the precision in the range of 4–7 mm over the future 24- month period. The results also indicate the relative importance of different physical features causing the displacements of each site. Overall, ML approaches demonstrate better performance and effectiveness in modelling and prediction of GNSS time series, thus impacting maintenance of geodetic reference frames, geodynamics, geophysics, and crustal deformation analysis. Numéro de notice : A2022-737 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-022-01662-5 Date de publication en ligne : 27/09/2022 En ligne : https://doi.org/10.1007/s00190-022-01662-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101709 [article]

Monitoring spatiotemporal soil moisture changes in the subsurface of forest sites using electrical resistivity tomography (ERT) / Julian Fäth in Journal of Forestry Research, vol 33 n° 5 (October 2022)  

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Increasing and widespread vulnerability of intact tropical rainforests to repeated droughts / Shengli Tao in Proceedings of the National Academy of Sciences of the United States of America PNAS, vol 119 n° 37 (2022)  

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Adaptive block modeling of time dependent variations of datum reference points in a tectonically active area / Chun-Yun Chou in Survey review, vol 54 n° 386 (September 2022)  

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Automated detection of discontinuities in EUREF permanent GNSS network stations due to earthquake events / Sergio Baselga in Survey review, vol 54 n° 386 (September 2022)  

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Benchmarking laser scanning and terrestrial photogrammetry to extract forest inventory parameters in a complex temperate forest / Daniel Kükenbrink in International journal of applied Earth observation and geoinformation, vol 113 (September 2022)  

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Impact assessment of the seasonal hydrological loading on geodetic movement and seismicity in Nepal Himalaya using GRACE and GNSS measurements / Devendra Shashikant Nagale in Geodesy and Geodynamics, vol 13 n° 5 (September 2022)  

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Mapping annual urban evolution process (2001–2018) at 250 m: A normalized multi-objective deep learning regression / Haoyu Wang in Remote sensing of environment, vol 278 (September 2022)  

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Towards a global seasonal and permanent reference water product from Sentinel-1/2 data for improved flood mapping / Sandro Martinis in Remote sensing of environment, vol 278 (September 2022)  

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Exploring tree growth allometry using two-date terrestrial laser scanning / Tuomas Yrttimaa in Forest ecology and management, vol 518 (August-15 2022)  

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Mapping land-use intensity of grasslands in Germany with machine learning and Sentinel-2 time series / Maximilian Lange in Remote sensing of environment, vol 277 (August 2022)  

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