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Deep mass redistribution prior to the 2010 Mw 8.8 Maule (Chile) Earthquake revealed by GRACE satellite gravity / Marie Bouih in Earth and planetary science letters, vol 584 (15 April 2022)  

Public [article]  inEarth and planetary science letters > vol 584 (15 April 2022) . - n° 117465 Titre : Deep mass redistribution prior to the 2010 Mw 8.8 Maule (Chile) Earthquake revealed by GRACE satellite gravity Type de document : Article/Communication Auteurs : Marie Bouih , Auteur ; Isabelle Panet , Auteur ; Dominique Remy, Auteur ; Laurent Longuevergne, Auteur ; Sylvain Bonvalot, Auteur Année de publication : 2022 Projets : Université de Paris / Clerici, Christine Article en page(s) : n° 117465 Note générale : bibliographie This study contributes to the IdEx Université de Paris ANR-18-IDEX-0001. Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] champ de pesanteur terrestre [Termes IGN] Chili [Termes IGN] déformation de la croute terrestre [Termes IGN] données GRACE [Termes IGN] gradient de gravitation [Termes IGN] jeu de données [Termes IGN] levé gravimétrique [Termes IGN] prévention des risques [Termes IGN] risque naturel [Termes IGN] séisme [Termes IGN] signal [Termes IGN] subduction [Termes IGN] tectonique des plaques Résumé : (auteur) Subduction zones megathrust faults constitute a considerable hazard as they produce most of the world's largest earthquakes. However, the role in megathrust earthquake generation exerted by deeper subduction processes remains poorly understood. Here, we analyze the 2003 – 2014 space-time variations of the Earth's gravity gradients derived from three datasets of GRACE geoid models over a large region surrounding the rupture zone of the Mw 8.8 Maule earthquake. In all these datasets, our analysis reveals a large-amplitude gravity gradient signal, progressively increasing in the three months before the earthquake, North of the epicentral area. We show that such signals are equivalent to a water storage decrease over 2 months and cannot be explained by hydrological sources nor artefacts, but rather find origin from mass redistributions within the solid Earth on the continental side of the subduction zone. These gravity gradient variations could be explained by an extensional deformation of the slab around 150-km depth along the Nazca Plate subduction direction, associated with large-scale fluid release. Furthermore, the lateral migration of the gravity signal towards the surface from a low coupling segment around North to the high coupling one in the South suggests that the Mw 8.8 earthquake may have originated from the propagation up to the trench of this deeper slab deformation. Our results highlight the importance of observations of the Earth's time-varying gravity field from satellites in order to probe slow mass redistributions in-depth major plate boundaries and provide new information on dynamic processes in the subduction system, essential to better understand the seismic cycle as a whole. Numéro de notice : A2022-280 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.epsl.2022.117465 En ligne : https://doi.org/10.1016/j.epsl.2022.117465 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100288 [article]

Detection of damaged buildings after an earthquake with convolutional neural networks in conjunction with image segmentation / Ramazan Unlu in The Visual Computer, vol 38 n° 2 (February 2022)  

Public [article]  inThe Visual Computer > vol 38 n° 2 (February 2022) . - pp 685 - 694 Titre : Detection of damaged buildings after an earthquake with convolutional neural networks in conjunction with image segmentation Type de document : Article/Communication Auteurs : Ramazan Unlu, Auteur ; Recep Kiriş, Auteur Année de publication : 2022 Article en page(s) : pp 685 - 694 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] bâtiment [Termes IGN] classification par nuées dynamiques [Termes IGN] détection de changement [Termes IGN] dommage matériel [Termes IGN] données étiquetées d'entrainement [Termes IGN] réseau neuronal convolutif [Termes IGN] segmentation d'image [Termes IGN] séisme Résumé : (auteur) Detecting damaged buildings after an earthquake as quickly as possible is important for emergency teams to reach these buildings and save the lives of many people. Today, damaged buildings after the earthquake are carried out by the survivors contacting the authorities or using some air vehicles such as helicopters. In this study, AI-based systems were tested to detect damaged or destroyed buildings by integrating into street camera systems after unexpected disasters. For this purpose, we have used VGG-16, VGG-19, and NASNet convolutional neural network models which are often used for image recognition problems in the literature to detect damaged buildings. In order to effectively implement these models, we have first segmented all the images with the K-means clustering algorithm. Thereafter, for the first phase of this study, segmented images labeled “damaged buildings” and “normal” were classified and the VGG-19 model was the most successful model with a 90% accuracy in the test set. Besides, as the second phase of the study, we have created a multiclass classification problem by labeling segmented images as “damaged buildings,” “less damaged buildings,” and “normal.” The same three architectures are used to achieve the most accurate classification results on the test set. VGG-19 and VGG-16, and NASNet have achieved considerable success in the test set with about 70%, 67%, and 62% accuracy, respectively. Numéro de notice : A2022-145 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s00371-020-02043-9 Date de publication en ligne : 03/01/2022 En ligne : https://doi.org/10.1007/s00371-020-02043-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100039 [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)  

Public [article]  inNatural Hazards and Earth System Sciences > vol 22 n° 2 (February 2022) . - pp 445 - 460 Titre : Multi-method monitoring of rockfall activity along the classic route up Mont Blanc (4809 m a.s.l.) to encourage adaptation by mountaineers Type de document : Article/Communication Auteurs : Jacques Mourey, Auteur ; Pascal Lacroix, Auteur ; Pierre-Allain Duvillard, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 445 - 460 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] capteur actif [Termes IGN] capteur non-imageur [Termes IGN] carte thématique [Termes IGN] détection de changement [Termes IGN] éboulement [Termes IGN] modèle numérique de terrain [Termes IGN] Mont-Blanc, massif du [Termes IGN] onde sismique [Termes IGN] pergélisol [Termes IGN] prévention des risques [Termes IGN] risque naturel [Termes IGN] saison [Termes IGN] sismologie [Termes IGN] surveillance géologique [Termes IGN] température de l'air Résumé : (auteur) There are on average 35 fatal mountaineering accidents per summer in France. On average, since 1990, 3.7 of them have occurred every summer in the Grand Couloir du Goûter, on the classic route up Mont Blanc (4809 m a.s.l.). Rockfall is one of the main factors that explain this high accident rate and contribute to making it one of the most accident-prone areas in the Alps for mountaineers. In this particular context, the objective of this study is to document the rockfall activity and its triggering factors in the Grand Couloir du Goûter in order to disseminate the results to mountaineers and favour their adaptation to the local rockfall hazard. Using a multi-method monitoring system (five seismic sensors, an automatic digital camera, three rock subsurface temperature sensors, a traffic sensor, a high-resolution topographical survey, two weather stations and a rain gauge), we acquired a continuous database on rockfalls during a period of 68 d in 2019 and some of their potential triggering factors (precipitation, ground and air temperatures, snow cover, frequentation by climbers). At the seasonal scale, our results confirm previous studies showing that rockfalls are most frequent during the snowmelt period in permafrost-affected rockwalls. Furthermore, the unprecedented time precision and completeness of our rockfall database at high elevation thanks to seismic sensors allowed us to investigate the factors triggering rockfalls. We found a clear correlation between rockfall frequency and air temperature, with a 2 h delay between peak air temperature and peak rockfall activity. A small number of rockfalls seem to be triggered by mountaineers. Our data set shows that climbers are not aware of the variations in rockfall frequency and/or cannot/will not adapt their behaviour to this hazard. These results should help to define an adaptation strategy for climbers. Therefore, we disseminated our results within the mountaineering community thanks to the full integration of our results into the management of the route by local actors. Knowledge built during this experiment has already been used for the definition and implementation of management measures for the attendance in summer 2020. Numéro de notice : A2022-181 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article DOI : 10.5194/nhess-22-445-2022 En ligne : https://doi.org/10.5194/nhess-22-445-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99859 [article]

Three-Dimensional point cloud analysis for building seismic damage information / Fan Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 2 (February 2022)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 2 (February 2022) . - pp 103 - 111 Titre : Three-Dimensional point cloud analysis for building seismic damage information Type de document : Article/Communication Auteurs : Fan Yang, Auteur ; Zhiwei Fan, Auteur ; Chao Wen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 103 - 111 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse comparative [Termes IGN] analyse de groupement [Termes IGN] analyse en composantes principales [Termes IGN] densité des points [Termes IGN] détection du bâti [Termes IGN] dommage matériel [Termes IGN] données localisées 3D [Termes IGN] extraction de données [Termes IGN] filtrage de points [Termes IGN] mur [Termes IGN] séisme [Termes IGN] semis de points Résumé : (Auteur) Postearthquake building damage assessment requires professional judgment; however, there are factors such as high workload and human error. Making use of Terrestrial Laser Scanning data, this paper presents a method for seismic damage information extraction. This new method is based on principal component analysis calculating the local surface curvature of each point in the point cloud. Then use the nearest point angle algorithm, combined with the data features of the actual measured value to identify point cloud seismic information, and filter the points that tend to the plane by setting the threshold value. Based on the statistical analysis of the normal vector, the raw point cloud data are deplanarized to obtain the preliminary results of seismic damage information. The density clustering algorithm is used to denoise the initially extracted seismic damage information. Ultimately, we can obtain the distribution patterns and characteristics of cracks in the walls of the building. The extraction result of the seismic damage information point cloud data is compared with the photos collected at the site, showing that the algorithm steps successfully identify the crack and shed wall skin information recorded in the site photos (identification rate: 95%). Point cloud distribution maps of cracked and shed siding areas determine quantitative information on seismic damage, providing a higher level of performance and detail than direct contact measurements. Numéro de notice : A2022-065 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00019R3 Date de publication en ligne : 01/02/2022 En ligne : https://doi.org/10.14358/PERS.21-00019R3 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99727 [article]

Automatic extraction of damaged houses by earthquake based on improved YOLOv5: A case study in Yangbi / Yafei Jing in Remote sensing, vol 14 n° 2 (January-2 2022)  

Public [article]  inRemote sensing > vol 14 n° 2 (January-2 2022) . - n° 382 Titre : Automatic extraction of damaged houses by earthquake based on improved YOLOv5: A case study in Yangbi Type de document : Article/Communication Auteurs : Yafei Jing, Auteur ; Yuhuan Ren, Auteur ; Yalan Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 382 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] détection d'objet [Termes IGN] détection de cible [Termes IGN] détection du bâti [Termes IGN] dommage matériel [Termes IGN] extraction automatique [Termes IGN] image captée par drone [Termes IGN] orthoimage [Termes IGN] séisme [Termes IGN] Yunnan (Chine) Résumé : (auteur) Efficiently and automatically acquiring information on earthquake damage through remote sensing has posed great challenges because the classical methods of detecting houses damaged by destructive earthquakes are often both time consuming and low in accuracy. A series of deep-learning-based techniques have been developed and recent studies have demonstrated their high intelligence for automatic target extraction for natural and remote sensing images. For the detection of small artificial targets, current studies show that You Only Look Once (YOLO) has a good performance in aerial and Unmanned Aerial Vehicle (UAV) images. However, less work has been conducted on the extraction of damaged houses. In this study, we propose a YOLOv5s-ViT-BiFPN-based neural network for the detection of rural houses. Specifically, to enhance the feature information of damaged houses from the global information of the feature map, we introduce the Vision Transformer into the feature extraction network. Furthermore, regarding the scale differences for damaged houses in UAV images due to the changes in flying height, we apply the Bi-Directional Feature Pyramid Network (BiFPN) for multi-scale feature fusion to aggregate features with different resolutions and test the model. We took the 2021 Yangbi earthquake with a surface wave magnitude (Ms) of 6.4 in Yunan, China, as an example; the results show that the proposed model presents a better performance, with the average precision (AP) being increased by 9.31% and 1.23% compared to YOLOv3 and YOLOv5s, respectively, and a detection speed of 80 FPS, which is 2.96 times faster than YOLOv3. In addition, the transferability test for five other areas showed that the average accuracy was 91.23% and the total processing time was 4 min, while 100 min were needed for professional visual interpreters. The experimental results demonstrate that the YOLOv5s-ViT-BiFPN model can automatically detect damaged rural houses due to destructive earthquakes in UAV images with a good performance in terms of accuracy and timeliness, as well as being robust and transferable. Numéro de notice : A2022-104 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14020382 Date de publication en ligne : 14/01/2022 En ligne : https://doi.org/10.3390/rs14020382 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99577 [article]

Co-seismic ionospheric disturbances following the 2016 West Sumatra and 2018 Palu earthquakes from GPS and GLONASS measurements / Mokhamad Nur Cahyadi in Remote sensing, vol 14 n° 2 (January-2 2022)  

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Géophysique / Jacques Dubois (2022) 

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A rapid assessment method for earthquake-induced landslide casualties based on GIS and logistic regression model / Yuqian Dai in Geomatics, Natural Hazards and Risk, vol 13 n° 1 (2022)  

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Disaster intensity-based selection of training samples for remote sensing building damage classification / Luis Moya in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)  

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Joint inversion of ground gravity data and satellite gravity gradients between Nepal and Bhutan: New insights on structural and seismic segmentation of the Himalayan arc / Rodolphe Cattin in Physics and chemistry of the Earth (A/B/C), vol 123 (October 2021)  

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Estimation of surface deformation due to Pasni earthquake using RADAR interferometry / Muhammad Ali in Geocarto international, vol 36 n° 14 ([01/08/2021])  

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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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Three-dimensional reconstruction of seismo-traveling ionospheric disturbances after March 11, 2011, Japan Tohoku earthquake / Changzhi Zhai in Journal of geodesy, vol 95 n° 7 (July 2021)  

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Anomalous variations of air temperature prior to earthquakes / Irfan Mahmood in Geocarto international, vol 36 n° 12 ([01/07/2021])  

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Orogenic collapse and stress adjustments revealed by an intense seismic swarm following the 2015 Gorkha earthquake in Nepal / Lok Bijaya Adhikari in Frontiers in Earth Science, vol 9 (2021)  

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