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Seismic deformation in the Adriatic Sea region / B. Orecchio in Journal of geodynamics, vol 155 (March 2023)  

Public [article]  inJournal of geodynamics > vol 155 (March 2023) . - n°101956 Titre : Seismic deformation in the Adriatic Sea region Type de document : Article/Communication Auteurs : B. Orecchio, Auteur ; D. Presti, Auteur ; S. Scolaro, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n°101956 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] Adriatique, mer [Termes IGN] déformation de la croute terrestre [Termes IGN] faille géologique [Termes IGN] forme d'onde [Termes IGN] histogramme [Termes IGN] inversion [Termes IGN] sismologie [Termes IGN] surveillance géologique [Termes IGN] tectonique des plaques Résumé : (auteur) We present an overall analysis of the recent seismic activity occurred in the Adriatic Sea region, a strongly debated sector of the Mediterranean area, where several authors have proposed different models of plate configuration and kinematics. In the past, seismic investigations of this marine area have been strongly hampered by non-optimal network geometries, but data quality increase and recent methodological improvements lay the groundwork to attempt more accurate analyses including proper evaluations of result reliability. On these grounds, we investigated the seismic activity of the last decades by means of new hypocenter locations, waveform inversion focal mechanisms and seismogenic stress fields. We used the Bayloc non-linear probabilistic algorithm to compute hypocenter locations for the most relevant seismic sequences by carefully evaluating location quality and seismolineaments reliability. We also provided an updated database of waveform inversion focal mechanisms including original solutions estimated by applying the waveform inversion method Cut And Paste and data available from official catalogs. Then, focal mechanism solutions have been used to estimate seismogenic stress fields through different inversion algorithms. Seismic results indicate a relevant degree of fragmentation and different patterns of deformation in the Central Adriatic region. In particular, our analyses depicted two NW-SE oriented, adjacent volumes: (i) a pure compressive domain with NNE-trending axis of maximum compression characterizes the northeastern volume where the seismic activity occurs on W-to-NW oriented seismic sources; (ii) a transpressive domain with NW-trending axis of maximum compression characterizes the southwestern sector where thrust faulting preferentially occurs on ENE-to-NE oriented planes and strike-slip faulting on E-W ones. Joint evaluation of seismic findings of the present study and kinematic models proposed in the literature indicates just in the Central Adriatic region the presence of a broad deformation zone, accommodating a still evolving fragmentation of the Adriatic domain in two blocks rotating in opposite directions. On these grounds, the obtained results not only furnish new seismological evidence supporting the "two-blocks model" proposed by previous authors, but they also provide additional constraints, useful for better understanding and modeling the seismotectonic processes occurring in the Adriatic region. Numéro de notice : A2023-051 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1016/j.jog.2022.101956 Date de publication en ligne : 30/11/2022 En ligne : https://doi.org/10.1016/j.jog.2022.101956 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102379 [article]

Wavelet-like denoising of GNSS data through machine learning. Application to the time series of the Campi Flegrei volcanic area (Southern Italy) / Rolando Carbonari in Geomatics, Natural Hazards and Risk, vol 14 n° 1 (2023)  

Public [article]  inGeomatics, Natural Hazards and Risk > vol 14 n° 1 (2023) . - n° 2187271 Titre : Wavelet-like denoising of GNSS data through machine learning. Application to the time series of the Campi Flegrei volcanic area (Southern Italy) Type de document : Article/Communication Auteurs : Rolando Carbonari, Auteur ; Umberto Riccardi, Auteur ; Prospero De Martino, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 2187271 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] caldeira [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] déformation de la croute terrestre [Termes IGN] données GNSS [Termes IGN] filtrage du bruit [Termes IGN] Naples [Termes IGN] relief volcanique [Termes IGN] risque naturel [Termes IGN] série temporelle [Termes IGN] surveillance géologique [Termes IGN] transformation en ondelettes Résumé : (auteur) The great potential of the Global Navigation Satellite System (GNSS) in monitoring ground deformation is widely recognized. As with other geophysical data, GNSS time series can be significantly noisy, hiding elusive ground deformation signals. Several denoising techniques have been proposed to improve the signal-to-noise ratio over the years. One of the most effective denoising techniques has been proved to be multi-resolution decomposition through the discrete wavelet transform. However, wavelet analysis requires long data sets to be effective, as well as long computation times, that hinder its use as a real or near real-time monitoring tool. We propose training by a Convolutional Neural Network (CNN) to perform the equivalent of wavelet analysis to overcome these limitations. Once trained, the CNN model provides answers within seconds, making it feasible as a real-time data analysis tool. Our Machine Learning algorithm is tested on daily GNSS time series collected in the Campi Flegrei caldera (Southern Italy), which is a highly volcanic risk area. Without significant gaps, the retrieved RMSE and R2 values vary in the ranges 0.65–0.98 and 0.06–0.52 cm, respectively. These results are encouraging, as they hint at the possibility of applying this methodology in more effective real-time monitoring solutions for active volcanoes. Numéro de notice : A2023-180 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1080/19475705.2023.2187271 Date de publication en ligne : 10/03/2023 En ligne : https://doi.org/10.1080/19475705.2023.2187271 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102949 [article]

Potential and limitation of PlanetScope images for 2-D and 3-D Earth surface monitoring with example of applications to glaciers and earthquakes / Saif Aati in IEEE Transactions on geoscience and remote sensing, vol 60 n° 10 (October 2022)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 60 n° 10 (October 2022) . - n° 4512919 Titre : Potential and limitation of PlanetScope images for 2-D and 3-D Earth surface monitoring with example of applications to glaciers and earthquakes Type de document : Article/Communication Auteurs : Saif Aati , Auteur ; Jean-Philippe Avouac, Auteur ; Ewelina Rupnik , Auteur ; Marc Pierrot-Deseilligny , Auteur Année de publication : 2022 Article en page(s) : n° 4512919 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de scène 3D [Termes IGN] artefact [Termes IGN] image PlanetScope [Termes IGN] modèle de déformation des images [Termes IGN] modèle par fonctions rationnelles [Termes IGN] séisme [Termes IGN] surveillance géologique Résumé : (auteur) The Planet PlanetScope (PS) CubeSat constellation acquires high-resolution optical images that cover the entire surface of the Earth daily, enabling an unprecedented capability to monitor the Earth’s surface changes. However, our analysis reveals artifacts of the geometry of PS images related to the imaging system and processing issues, limiting the usability of these data for various Earth science applications, including the monitoring of glaciers, dune motion, or the measurement of ground deformation due to earthquakes and landslides. Here, we analyze these artifacts and propose ways to remediate them. We use two examples to evaluate the data and assess the performance of our proposed approaches. The first is the ground deformation caused by the 2019 Ridgecrest earthquake sequence, California, USA, and the second is the 2018–2019 surge of the Shisper glacier in the Karakorum. Using an image correlation technique, we show that PS images exhibit several geometric artifacts, such as scene-to-scene misregistration, inconsistence geolocation accuracy between spectral bands, and topographic artifacts. Altogether, these artifacts make a quantitative analysis of ground displacement difficult and inaccurate. We present a method that remediates most of these geometric artifacts. In addition, we propose a framework for selecting the most appropriate images and a procedure for refining the rational function model (RFM) of unrectified images to monitor surface displacements and topography changes in 3-D. These tools should enhance the use of PS images for Earth science applications. Numéro de notice : A2022-951 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3215821 Date de publication en ligne : 19/10/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3215821 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103278 [article]

Detection and characterization of slow-moving landslides in the 2017 Jiuzhaigou earthquake area by combining satellite SAR observations and airborne Lidar DSM / Jiehua Cai in Engineering Geology, vol 305 (August 2022)  

Public [article]  inEngineering Geology > vol 305 (August 2022) . - n° 106730 Titre : Detection and characterization of slow-moving landslides in the 2017 Jiuzhaigou earthquake area by combining satellite SAR observations and airborne Lidar DSM Type de document : Article/Communication Auteurs : Jiehua Cai, Auteur ; Lu Zhang, Auteur ; Jie Dong, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 106730 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] cartographie des risques [Termes IGN] déformation de surface [Termes IGN] données lidar [Termes IGN] données multisources [Termes IGN] effondrement de terrain [Termes IGN] géomorphologie [Termes IGN] image ALOS-PALSAR [Termes IGN] image optique [Termes IGN] image Sentinel-SAR [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] MNS lidar [Termes IGN] MNS SRTM [Termes IGN] séisme [Termes IGN] Setchouan (Chine) [Termes IGN] surveillance géologique Résumé : (auteur) On 8th August 2017, a catastrophic Ms. 7.0 earthquake with a focal depth of 20 km struck the Jiuzhaigou County in Sichuan Province, China. It exerted a strong influence on the slope stability within the surrounding areas and triggered numerous secondary geohazards including rockfalls and other co-seismic landslides, which incurred drastic surface changes, and thus can be easily identified from cloud-free high-resolution optical imagery. Most of such landslides became stabilized shortly after the earthquake while others moving very slowly for years. In contrast, some slopes were destabilized without significant surface change into slow-moving landslides, which may pose long-term potential threats to people's life and property. Therefore, it is crucial to accurately identify these slow-moving landslides and regularly monitor their post-seismic activity. In this study, we employed the synthetic aperture radar interferometry (InSAR) techniques to detect and monitor slow-moving landslides after the earthquake in the Jiuzhaigou area, and analyzed the impacts of the earthquake on these landslides through integration of multi-source data (InSAR, Lidar, optical image, and field survey). As a result, 16 slow-moving landslides were detected by InSAR in the Jiuzhaigou area, including several historical landslides. The results of time-series InSAR analyses enabled identification of three kinds of landslide evolution modes affected by the earthquake, i.e. acceleration of deformation of pre-existing landslides, reactivation of dormant landslide, and remobilization of earthquake-triggered landslide. Each mode is supported by detailed analyses of multi-source data. The results demonstrated that satellite InSAR combined with high-resolution Lidar and optical data can provide a cost-effective approach of post-earthquake geohazards detection and monitoring. Numéro de notice : A2022-469 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.enggeo.2022.106730 Date de publication en ligne : 28/05/2022 En ligne : https://doi.org/10.1016/j.enggeo.2022.106730 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100811 [article]

Cliff change detection using siamese KPCONV deep network on 3D point clouds / Iris de Gelis in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-3-2022 (2022 edition)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-3-2022 (2022 edition) . - pp 649 - 656 Titre : Cliff change detection using siamese KPCONV deep network on 3D point clouds Type de document : Article/Communication Auteurs : Iris de Gelis, Auteur ; Zoé Bessin, Auteur ; Pauline Letortu, Auteur ; Marion Jaud, Auteur ; C. Delacourt, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 649 - 656 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] détection de changement [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] érosion côtière [Termes IGN] falaise [Termes IGN] semis de points [Termes IGN] surveillance géologique Mots-clés libres : KPConv = Kernel Point Convolution Résumé : (auteur) Mainly depending on their lithology, coastal cliffs are prone to changes due to erosion. This erosion could increase due to climate change leading to potential threats for coastal users, assets, or infrastructure. Thus, it is important to be able to understand and characterize cliff face changes at fine scale. Usually, monitoring is conducted thanks to distance computation and manual analysis of each cliff face over 3D point clouds to be able to study 3D dynamics of cliffs. This is time consuming and inclined to each one judgment in particular when dealing with 3D point clouds data. Indeed, 3D point clouds characteristics (sparsity, impossibility of working on a classical top view representation, volume of data, …) make their processing harder than 2D images. Last decades, an increase of performance of machine learning methods for earth observation purposes has been performed. To the best of our knowledge, deep learning has never been used for 3D change detection and categorization in coastal cliffs. Lately, Siamese KPConv brings successful results for change detection and categorization into 3D point clouds in urban area. Although the case study is different by its more random characteristics and its complex geometry, we demonstrate here that this method also allows to extract and categorize changes on coastal cliff face. Results over the study area of Petit Ailly cliffs in Varengeville-sur-Mer (France) are very promising qualitatively as well as quantitatively: erosion is retrieved with an intersection over union score of 83.86 %. Numéro de notice : A2022-444 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5194/isprs-annals-V-3-2022-649-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-3-2022-649-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100779 [article]

Multi-method monitoring of rockfall activity along the classic route up Mont Blanc (4809 m a.s.l.) to encourage adaptation by mountaineers / Jacques Mourey in Natural Hazards and Earth System Sciences, vol 22 n° 2 (February 2022)  

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Modélisation du lien entre éruptions et glissements de flancs au Piton de la Fournaise / Quentin Dumont (2022)  

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Three-dimensional simulations of rockfalls in Ischia, Southern Italy, and preliminary susceptibility zonation / Massimiliano Alvioli in Geomatics, Natural Hazards and Risk, vol 13 (2022)  

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Validation of the accuracy of geodetic automated measurement system based on GNSS platform for continuous monitoring of surface movements in post-mining areas / Violetta Sokoła-Szewioła in Reports on geodesy and geoinformatics, vol 112 n° 1 (December 2021)  

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Persistent scatterer interferometry for Pettimudi (India) landslide monitoring using Sentinel-1A images / Hari Shankar in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 11 (November 2021)  

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Investigation of the landslides in Beylikdüzü-Esenyurt districts of Istanbul from InSAR and GNSS observations / Caglar Bayik in Natural Hazards, vol 109 n° 1 (October 2021)  

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Investigating the application of artificial intelligence for earthquake prediction in Terengganu / Suzlyana Marhain in Natural Hazards, vol 108 n° 1 (August 2021)  

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Applying planetary mapping methods to submarine environments: onshore-offshore geomorphology of Christiana-Santorini-Kolumbo Volcanic Group, Greece / Alexandra E. Huff in Journal of maps, vol 17 n° 3 (July 2021)  

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Dynamique contrastée de la compaction d’un ferralsol après une défriche mécanisée alternative en Guyane française / Xavier Guerrini in Bois et forêts des tropiques, n° 348 ([01/07/2021])  

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Integration of an InSAR and ANN for sinkhole susceptibility mapping: A case study from Kirikkale-Delice (Turkey) / Hakan Nefeslioglu in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)  

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