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Discrete element analysis of deformation features of slope controlled by karst fissures under the mining effect: a case study of Pusa landslide, China / Qian Zhao in Geomatics, Natural Hazards and Risk, vol 14 n° 1 (2023)  

Public [article]  inGeomatics, Natural Hazards and Risk > vol 14 n° 1 (2023) . - pp 1 - 32 Titre : Discrete element analysis of deformation features of slope controlled by karst fissures under the mining effect: a case study of Pusa landslide, China Type de document : Article/Communication Auteurs : Qian Zhao, Auteur ; Zhongping Yang, Auteur ; Yuanwen Jiang, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 1 - 32 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] Chine [Termes IGN] effondrement de terrain [Termes IGN] faille géologique [Termes IGN] géomorphologie locale [Termes IGN] karst [Termes IGN] pente [Termes IGN] prospection minérale Résumé : (auteur) Karst landforms are widely distributed in the southwestern mountain areas of China, and the continuous underground mining activities lead to frequent occurrence of catastrophic collapses and landslides. Revealing the relationship between the development characteristics of the controlling karst fissures and the slope deformation process is crucial to understand the collapse and landslide phenomena. The Pusa landslide is selected as the geological prototype of discrete element analysis, and the universal distinct element code (UDEC) is applied to simulate the overall deformation response of the mountain containing extensive karst fissure during the mining process. The results show that under the action of mining, the roof above the goaf bends and subsides, and the middle of the roof even breaks and collapses. The separation fractures effectively block the upward transmission of the collapse state of the rock stratum. The bottom of the karst fissure is susceptible to cracking first in the process of coal seam mining due to stress concentration, and the area of severe deformation in the slope coincides with the mining pressurization area. The morphology of the karst fissure controls and determines the deformation characteristics of the rock mass at the slope top, and only the karst fissure located within the mining influence range is the object to be considered in the slope stability analysis. The limit karst fracture depth, about 1/3 of the slope height, is the limit value to determine whether the rock mass at the slope top is toppled or slipped. The relationship between the karst fissure and the free surface gradually changes from the directional or co-directional to the reverse, the motion state of the rock mass at the slope top changes from slipping to toppling, and the role of karst fissure changes from a potential slip surface to the cracking boundary. Although the deformation damage of the reverse structural slope is not very serious, the influence of the karst fissure on the stability of the slope still cannot be ignored. This study aims to provide basic theoretical support for the subsequent research on the failure mechanism of karst mountains under the combined action of multi-structural planes. Numéro de notice : A2023-036 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475705.2022.2158376 Date de publication en ligne : 29/12/2023 En ligne : https://doi.org/10.1080/19475705.2022.2158376 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102305 [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 Projets : SLES-S5 / Nocquet, Jean-Mathieu 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]

Decadal surface changes and displacements in Switzerland / Valentin Tertius Bickel in Journal of Geovisualization and Spatial Analysis, vol 6 n° 2 (December 2022)  

Public [article]  inJournal of Geovisualization and Spatial Analysis > vol 6 n° 2 (December 2022) . - n° 24 Titre : Decadal surface changes and displacements in Switzerland Type de document : Article/Communication Auteurs : Valentin Tertius Bickel, Auteur ; Andrea Manconi, Auteur Année de publication : 2022 Article en page(s) : n° 24 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] corrélation d'images [Termes IGN] détection de changement [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] données multitemporelles [Termes IGN] effondrement de terrain [Termes IGN] géomorphologie locale [Termes IGN] glacier [Termes IGN] Liechtenstein [Termes IGN] modèle numérique de terrain [Termes IGN] stéréophotogrammétrie [Termes IGN] Suisse Résumé : (auteur) Multi-temporal, high-resolution, and homogeneous geospatial datasets acquired by space- and/or airborne sensors provide unprecedented opportunities for the characterization and monitoring of surface changes on very large spatial scales. Here, we demonstrate how an off-the-shelf, open-source image correlation algorithm can be combined with SwissALTI3D LiDAR-derived elevation data from different tracking periods to create country-scale surface displacement and vertical change maps of Switzerland, including Liechtenstein, with minimal computational effort. The results show that glacier displacement and ablation make up the most significant fraction of the detected surface changes in the last two decades. In addition, we identify numerous landslides and other geomorphic features, as well as manmade changes such as construction sites and landfills. All produced maps and data products are available online, free of charge. Numéro de notice : A2022-832 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s41651-022-00119-9 Date de publication en ligne : 01/08/2022 En ligne : https://doi.org/10.1007/s41651-022-00119-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102019 [article]

Graph neural networks with constraints of environmental consistency for landslide susceptibility evaluation / Haowei Zeng in International journal of geographical information science IJGIS, vol 36 n° 11 (November 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 11 (November 2022) Titre : Graph neural networks with constraints of environmental consistency for landslide susceptibility evaluation Type de document : Article/Communication Auteurs : Haowei Zeng, Auteur ; Qing Zhu, Auteur ; Yulin Ding, Auteur ; et al., Auteur Année de publication : 2022 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] aléa [Termes IGN] cartographie des risques [Termes IGN] cohérence des données [Termes IGN] effondrement de terrain [Termes IGN] prédiction [Termes IGN] programmation par contraintes [Termes IGN] réseau neuronal de graphes [Termes IGN] vulnérabilité Résumé : (auteur) In complex and heterogeneous geoenvironments, landslides exhibit varying features in different environments, and data in landslide inventories are imbalanced. Existing data-driven landslide susceptibility evaluation (LSE) methods overlook environmental heterogeneity and cannot reliably predict regions with few samples. Alternatively, global random negative sampling strategies may produce imbalanced positive and negative samples in some environments, contributing to inaccurate predictions. This article proposes a graph neural network (GNN) constrained by environmental consistency (GNN-EC) to overcome these problems. The GNN-EC consists of graphs with nodes, and edges. A graph represents the environmental relationships in the study area. Nodes are geographic units delineated from terrain polygon approximation. Edges capture the relationships between node-pairs. Additionally, the weights of edges reflect the similarity between two node environments. A GNN aggregates node information in the graph for LSE. Our experiment showed that the proposed method outperformed the common machine learning methods: increasing prediction accuracy by approximately 7, 5–6 and 3–4% compared to the artificial neural network (ANN), the support vector machine (SVM) and the random forest (RF), respectively. Moreover, our method can maintain high prediction accuracy, even with a small training set. Numéro de notice : A2022-626 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2103819 Date de publication en ligne : 28/07/2022 En ligne : https://doi.org/10.1080/13658816.2022.2103819 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101396 [article]

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Machine learning and landslide studies: recent advances and applications / Faraz S. Tehrani in Natural Hazards, vol 114 n° 2 (November 2022)  

Public [article]  inNatural Hazards > vol 114 n° 2 (November 2022) . - pp 1197 - 1245 Titre : Machine learning and landslide studies: recent advances and applications Type de document : Article/Communication Auteurs : Faraz S. Tehrani, Auteur ; Michele Calvello, Auteur ; Zongqiang Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1197 - 1245 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse d'image orientée objet [Termes IGN] analyse spatiale [Termes IGN] apprentissage automatique [Termes IGN] cartographie des risques [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] effondrement de terrain [Termes IGN] image Sentinel-MSI [Termes IGN] réseau neuronal artificiel [Termes IGN] simulation spatiale Résumé : (auteur) Upon the introduction of machine learning (ML) and its variants, in the form that we know today, to the landslide community, many studies have been carried out to explore the usefulness of ML in landslide research and to look at some classic landslide problems from an ML point of view. ML techniques, including deep learning methods, are becoming popular to model complex landslide problems and are starting to demonstrate promising predictive performance compared to conventional methods. Almost all the studies published in the literature in recent years belong to one of the following three broad categories: landslide detection and mapping, landslide spatial forecasting in the form of susceptibility mapping, and landslide temporal forecasting. In this paper, we present a brief overview of ML techniques, provide a general summary of the landslide studies conducted, in recent years, in the three above-mentioned categories, and make an attempt to critically evaluate the use of ML methods to model landslide processes. The paper also provides suggestions for future use of these powerful data-driven techniques in landslide studies. Numéro de notice : A2022-841 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1007/s11069-022-05423-7 Date de publication en ligne : 20/06/2022 En ligne : https://doi.org/10.1007/s11069-022-05423-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102051 [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])  

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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)  

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PS-InSAR based validated landslide susceptibility modelling: a case study of Ghizer valley, Northern Pakistan / Sajid Hussain in Geocarto international, vol 37 n° 13 ([15/07/2022])  

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Assessing and mapping landslide susceptibility using different machine learning methods / Osman Orhan in Geocarto international, vol 37 n° 10 ([01/06/2022])  

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Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping / Emrehan Kutlug Sahin in Geocarto international, vol 37 n° 9 ([15/05/2022])  

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Landslide susceptibility assessment considering spatial agglomeration and dispersion characteristics: A case study of Bijie City in Guizhou Province, China / Kezhen Yao in ISPRS International journal of geo-information, vol 11 n° 5 (May 2022)  

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Volunteered geographic information mobile application for participatory landslide inventory mapping / Raden Muhammad Anshori in Computers & geosciences, vol 161 (April 2022)  

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Analyse haute résolution de la morphologie des paysages et des processus à partir de LiDAR aéroporté répété et simulation hydraulique / Thomas Bernard (2022)  

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Combining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: A case study of Wanzhou section of the Three Gorges Reservoir, China / Huijuan Zhang in Computers & geosciences, vol 158 (January 2022)  

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A GIS-based landslide susceptibility mapping and variable importance analysis using artificial intelligent training-based methods / Pengxiang Zhao in Remote sensing, vol 14 n° 1 (January-1 2022)  

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