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Correcting laser scanning intensity recorded in a cave environment for high-resolution lithological mapping: A case study of the Gouffre Georges, France / Michaela Nováková in Remote sensing of environment, vol 280 (October 2022)
[article]
Titre : Correcting laser scanning intensity recorded in a cave environment for high-resolution lithological mapping: A case study of the Gouffre Georges, France Type de document : Article/Communication Auteurs : Michaela Nováková, Auteur ; Michal Gallay, Auteur ; Jozef Šupinský, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 113210 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] amélioration du contraste
[Termes IGN] Ariège (09)
[Termes IGN] cartographie géologique
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] filtrage du bruit
[Termes IGN] grotte
[Termes IGN] intensité lumineuse
[Termes IGN] lithologie
[Termes IGN] roche
[Termes IGN] semis de points
[Termes IGN] télémétrie laser terrestreRésumé : (auteur) Active remote sensing by laser scanning (LiDAR) has markedly improved the mapping of a cave environment with an unprecedented level of accuracy and spatial detail. However, the use of laser intensity simultaneously recorded during the scanning of caves remains unexplored despite it having promising potential for lithological mapping as it has been demonstrated by many applications in open-sky conditions. The appropriate use of laser intensity requires calibration and corrections for influencing factors, which are different in caves as opposed to the above-ground environments. Our study presents an efficient and complex workflow to correct the recorded intensity, which takes into consideration the acquisition geometry, micromorphology of the cave surface, and the specific atmospheric influence previously neglected in terrestrial laser scanning. The applicability of the approach is demonstrated on terrestrial LiDAR data acquired in the Gouffre Georges, a cave located in the northern Pyrenees in France. The cave is unique for its geology and lithology allowing for observation, with a spectacular continuity without any vegetal cover, of the contact between marble and lherzolite rocks and tectonic structures that characterize such contact. The overall accuracy of rock surface classification based on the corrected laser intensity was over 84%. The presence of water or a wet surface introduced bias of the intensity values towards lower values complicating the material discrimination. Such conditions have to be considered in applications of the recorded laser intensity in mapping underground spaces. The presented method allows for putting geological observations in an absolute spatial reference frame, which is often very difficult in a cave environment. Thus, laser scanning of the cave geometry assigned with the corrected laser intensity is an invaluable tool to unravel the complexity of such a lithological environment. Numéro de notice : A2022-775 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.113210 Date de publication en ligne : 10/08/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113210 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101807
in Remote sensing of environment > vol 280 (October 2022) . - n° 113210[article]Developing a GIS-based rough fuzzy set granulation model to handle spatial uncertainty for hydrocarbon structure classification, case study: Fars domain, Iran / Sahand Seraj in Geo-spatial Information Science, vol 25 n° 3 (October 2022)
[article]
Titre : Developing a GIS-based rough fuzzy set granulation model to handle spatial uncertainty for hydrocarbon structure classification, case study: Fars domain, Iran Type de document : Article/Communication Auteurs : Sahand Seraj, Auteur ; Mahmoud Reza Delavar, Auteur Année de publication : 2022 Article en page(s) : pp 399 - 41 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] cartographie géologique
[Termes IGN] classification floue
[Termes IGN] entropie de Shannon
[Termes IGN] forage
[Termes IGN] granulométrie (pétrologie)
[Termes IGN] hydrocarbure
[Termes IGN] incertitude géométrique
[Termes IGN] Iran
[Termes IGN] prospection minérale
[Termes IGN] sous ensemble flou
[Termes IGN] système d'information géographiqueRésumé : (auteur) It is well agreed that geologic risk occurs during hydrocarbon exploration because diverse uncertainties accompany the entire hydrocarbon system parameters such as the source rock, reservoir rock, trap and seal rock. In order to overcome such attributes with uncertainties, a number of soft computing methods are used. Information granules could be provided by the Rough Fuzzy Set Granulation (RFSG) with a thorough quality evaluation. This is capable of attribute reduction that has been claimed to be essential in investigating the hydrocarbon systems. This paper is an endeavor to recommend a Geospatial Information System (GIS)-based model with the aim of categorizing the hydrocarbon structures map consistent with the uncertainty range concepts of geologic risk in the rough fuzzy sets and granular computing. The model used the RFSG for the attribute reduction by a Decision Logic language (DL-language). The RFSG was employed in order to classify hydrocarbon structures according to geological risk and extract the fuzzy rules with a predefined range of uncertainty. In order to assess the precisions of the fuzzy decisions on the hydrocarbon structure classification, the fuzzy entropy and fuzzy cross-entropy are applied. The proposed RFSG model applied for 62 structures as the training data, average fuzzy entropy has been calculated as 0.85, whereas the average fuzzy cross-entropy has been calculated 0.18. As it can be discerned, just seven structures had cross-entropies greater than 0.1, while three structures were larger than 0.3. It is implied that the precision of the proposed model is about 89%. The results yielded two reductions for the condition attributes and 11 fuzzy rules being filtered by the granular computing values. Numéro de notice : A2022-724 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10095020.2021.2020600 Date de publication en ligne : 03/02/2022 En ligne : https://doi.org/10.1080/10095020.2021.2020600 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101667
in Geo-spatial Information Science > vol 25 n° 3 (October 2022) . - pp 399 - 41[article]Investigating the efficiency of deep learning methods in estimating GPS geodetic velocity / Omid Memarian Sorkhabi in Earth and space science, vol 9 n° 10 (October 2022)
[article]
Titre : Investigating the efficiency of deep learning methods in estimating GPS geodetic velocity Type de document : Article/Communication Auteurs : Omid Memarian Sorkhabi, Auteur ; Muhammed Milani, Auteur ; Seyed Mehdi Seyed Alizadeh, Auteur Année de publication : 2022 Article en page(s) : 8 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] apprentissage profond
[Termes IGN] champ de vitesse
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] géodynamique
[Termes IGN] point géodésique
[Termes IGN] positionnement par GPS
[Termes IGN] station GPS
[Termes IGN] tectoniqueRésumé : (auteur) Geodetic velocity (GV) has many applications in tectonic motion determination and geodynamic studies. Due to the high cost of global navigation satellite system stations, deep learning methods have been investigated to estimate GV. In this research, four methods of convolutional neural networks (CNNs), deep Boltzmann machines, deep belief net and recurrent neural networks have been applied. The GV of 42 global positioning system stations is entered the deep learning methods. The outputs of the four methods have successfully passed the normality test. The results show that the CNN method has a lower goodness of fit and root mean square error (RMSE). CNN can learn different dependencies and extract features. Numéro de notice : A2022-757 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1029/2021EA002202 Date de publication en ligne : 22/09/2022 En ligne : https://doi.org/10.1029/2021EA002202 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101763
in Earth and space science > vol 9 n° 10 (October 2022) . - 8 p.[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)
[article]
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 GNSSRésumé : (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
in Journal of geodesy > vol 96 n° 10 (October 2022) . - n° 71[article]Remote sensing and GIS based Soil Loss Estimation for Bhutan, using RUSLE model / Sangay Gyeltshen in Geocarto international, Vol 37 n° 21 ([01/10/2022])
[article]
Titre : Remote sensing and GIS based Soil Loss Estimation for Bhutan, using RUSLE model Type de document : Article/Communication Auteurs : Sangay Gyeltshen, Auteur ; Rabindra Adhikarib, Auteur ; Padam Bahadur Budha, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 6331 - 6350 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] Bhoutan
[Termes IGN] distribution spatiale
[Termes IGN] effondrement de terrain
[Termes IGN] érosion
[Termes IGN] modèle RUSLE
[Termes IGN] système d'information géographique
[Termes IGN] télédétection
[Termes IGN] utilisation du solRésumé : (auteur) The repository of soil by water at a national and basin scale was estimated using the RUSLE empirical model which is the first of its kind in Bhutan. The annual soil loss is estimated and categorized into five categories: very low (800 t/yr). Sakteng and Jaldakha basins contributed the highest soil loss rate of 0.04 and 0.039 t/ha/yr, while considering on landuse pattern, non-built-up and landslide category encountered the highest soil loss of 4.09 and 0.7 t/ha/yr among others. Similarly, Tsirang, Samtse and Haa contributed the major soil loss of 0.03, 0.0298 and 0.02 t/ha/yr, respectively. The research can be used as an authentic instrument enabling the soil conservationist and the policymakers to evaluate the adverse impacts, prioritize the conservation efforts and investigate further to narrow down the causes of soil erosion. Numéro de notice : A2022-718 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : https://doi.org/10.1080/10106049.2021.1936210 Date de publication en ligne : 22/06/2021 En ligne : https://doi.org/10.1080/10106049.2021.1936210 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101646
in Geocarto international > Vol 37 n° 21 [01/10/2022] . - pp 6331 - 6350[article]Réservation
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