Centre de documentation
scientifique

Dépouillements

Rapid mapping of seismic intensity assessment using ground motion data calculated from early aftershocks selected by GIS spatial analysis / Huaiqun 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 - 21 Titre : Rapid mapping of seismic intensity assessment using ground motion data calculated from early aftershocks selected by GIS spatial analysis Type de document : Article/Communication Auteurs : Huaiqun Zhao, Auteur ; Yijiao Jia, Auteur ; Wenkai Chen, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 1 - 21 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] cartographie thématique [Termes IGN] Chine [Termes IGN] distribution spatiale [Termes IGN] dommage [Termes IGN] régression [Termes IGN] sismologie [Termes IGN] zone sinistrée [Termes IGN] zone tampon Résumé : (auteur) Following a major earthquake, disaster information services must deliver accurate damage assessment results during the emergency ‘black box’ phase when data is scarce. Seismic intensity maps contain crucial information for determining the damage in the affected area. For earthquakes with Mw between 5.5 and 7, this study proposes using GIS analysis to mine aftershock events in early aftershock sequences that are closely related to the mainshock fault, and then using these events to generate seismic intensity assessment maps. Regression curves were first obtained using a nonparametric method (rLowess) to analyse the geographical coordinates of early aftershocks. Then, a buffer of 1 or 1.5 km radius was made for the curve, and the aftershocks in the buffer were used to calculate the predicted peak ground velocity (PGV) values over a specific km-grid range. Finally, rapid mapping of seismic intensity was assessed based on the intensity scale. This straightforward and repeatable method employs seismic station data obtained shortly after the mainshock. The assessed seismic intensity accurately reflects the location and extent of the hardest hit areas and can be cross-referenced with geophysical results to accurately assess the damage in the affected areas. Numéro de notice : A2023-035 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/POSITIONNEMENT Nature : Article DOI : 10.1080/19475705.2022.2160663 Date de publication en ligne : 02/01/2023 En ligne : https://doi.org/10.1080/19475705.2022.2160663 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102304 [article]

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]

Machine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami / Riantini Virtriana in Geomatics, Natural Hazards and Risk, vol 14 n° 1 (2023)  

Public [article]  inGeomatics, Natural Hazards and Risk > vol 14 n° 1 (2023) . - pp 28 - 51 Titre : Machine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami Type de document : Article/Communication Auteurs : Riantini Virtriana, Auteur ; Agung Budi Harto, Auteur ; Fiza Wira Atmaja, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 28 - 51 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] base de données d'images [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] dommage matériel [Termes IGN] données Copernicus [Termes IGN] image Sentinel-MSI [Termes IGN] image Worldview [Termes IGN] Indonésie [Termes IGN] modèle numérique de surface [Termes IGN] segmentation d'image [Termes IGN] tsunami Résumé : (auteur) In Indonesia, tsunamis are frequent events. In 2000–2016, there were 44 tsunami events in Indonesia, with financial losses reaching 43.38 trillion. In 2018, a tsunami occurred in the Sunda Strait due to the eruption of the Anak Krakatau Volcano, which caused many fatalities and much building damage. This study aimed to detect the building damage in the Labuan District, Banten Province. Machine learning methods were used to detect building damage using random forest with object-based techniques. No previous research has combined selected predictors into scenarios; hence, the novelty of this study is combining various random forest predictors to identify the extent of building damage using 14 predictor scenarios. In addition, field surveys were conducted two years and nine months after the tsunami to observe the changes and efforts made. The results of the random forest classification were validated and compared with three datasets, namely xBD, Copernicus, and field survey data. The results of this study can help classify the level of building damage using satellite imagery to improve mitigation in tsunami-prone areas. Numéro de notice : A2023-037 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/19475705.2022.2147455 Date de publication en ligne : 07/12/2022 En ligne : https://doi.org/10.1080/19475705.2022.2147455 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102307 [article]

Sediment yield estimation in GIS environment using RUSLE and SDR model in Southern Ethiopia / Dawit Kanito in Geomatics, Natural Hazards and Risk, vol 14 n° 1 (2023)  

Public [article]  inGeomatics, Natural Hazards and Risk > vol 14 n° 1 (2023) . - n° 2167614 Titre : Sediment yield estimation in GIS environment using RUSLE and SDR model in Southern Ethiopia Type de document : Article/Communication Auteurs : Dawit Kanito, Auteur ; Dawit Bedadi, Auteur ; Samuel Feyissa, Auteur Année de publication : 2023 Article en page(s) : n° 2167614 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse de sensibilité [Termes IGN] bassin hydrographique [Termes IGN] Ethiopie [Termes IGN] image Landsat [Termes IGN] modèle RUSLE [Termes IGN] précipitation [Termes IGN] sédiment [Termes IGN] système d'information géographique Résumé : (auteur) Soil erosion and sediment yields are the current limitations and future threats to agriculture, water resources and hydropower projects particularly in developing countries. Estimating the extent and comprehending the spatial distribution of hotspot area is crucial to implement evidence-based soil and water conservation (SWC) measures with limited resources. The study used RUSLE and SDR models in ArcGIS 10.8 environment. The RUSLE model was found to be highly sensitive to C factor followed by LS factor. The result indicated that the annual soil loss varies from 0 to 359.99 t ha−1 yr−1 with 22.31 t ha−1 yr−1 as a mean annual. Besides, the estimated sediment yield ranged from 0 to 42.5 t ha−1 yr−1 with a mean value of 12.02 t ha−1 yr−1. The finding revealed that the central west (SW_5) and northeast (SW_4) parts of the watershed yield higher sediment. The result also signified that about 52.9% of the eroded materials including soil and nutrients are transferred to the outlet. The outcome of our finding undoubtedly aids in the identification of hotspot areas for the adoption of appropriate SWC measures. Hence, adopting RUSLE and SDR for Gununo watershed and another watershed having similar biophysical and environmental factors is suggested. Numéro de notice : A2023-155 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475705.2023.2167614 Date de publication en ligne : 26/01/2023 En ligne : https://doi.org/10.1080/19475705.2023.2167614 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102841 [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]

Comparative analysis of estimation of slope-length gradient (LS) factor for entire Afghanistan / Ahmad Ansari in Geomatics, Natural Hazards and Risk, vol 14 n° 1 (2023)  

Public [article]  inGeomatics, Natural Hazards and Risk > vol 14 n° 1 (2023) . - n° 2200890 Titre : Comparative analysis of estimation of slope-length gradient (LS) factor for entire Afghanistan Type de document : Article/Communication Auteurs : Ahmad Ansari, Auteur ; Gökmen Tayfur, Auteur Année de publication : 2023 Article en page(s) : n° 2200890 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] Afghanistan [Termes IGN] bassin hydrographique [Termes IGN] érosion [Termes IGN] gradient de pente [Termes IGN] modèle numérique de surface [Termes IGN] modèle RUSLE [Termes IGN] système d'information géographique Résumé : (auteur) Slope length gradient (LS) is one of the crucial factors in the Universal Soil Loss Equations (USLE, RUSLE). This study aimed at estimating the slope-length and slope-steepness (LS) factor for the entire watersheds of Afghanistan by using three different methods, namely; (1) LS-TOOLMFD (Method 1); (2) The Method of Equations (Method 2); and (3) The approach of Moore and Burch (Method 3). The first method uses the digital elevation model (DEM) in the ASCII format, and the other two methods use the DEM in the spatial domain. The results show that the LS-factor of the study area ranges from 0.01 to 44.31, with a mean of 5.24 and standard deviation of 6.95, according to Method 1; 0.03 to 163.49, with a mean of 9.6 and standard deviation of 13.58, according to Method 2; and 0 to 3985, with a mean of 7.16 and standard deviation of 29.7, according to Method 3. The study reveals that Methods 1 and 2 are more appropriate than Method 3 because Method 3 yields high LS-factor values close to or at streamlines located near mountainous regions. The highest LS values are found to be in the northeast, north, and central regions of Afghanistan, which is consistent with the high mountains and deep valley geomorphology, indicating that these regions are particularly vulnerable to soil erosion by rainfall-runoff processes. The sediment delivery ratio (SDR) for the Upper-Helmand River Basin (Upper-HRB) is also estimated by the RUSLE, employing the LS factors produced by the three methods. The results revealed that the average annual soil loss is found to be, respectively, 9.3, 18.2, and 11.1 (ton/ha/year) by using the three methods, corresponding to SDR of 23.5%, 12.1%, and 19.9%. Numéro de notice : A2023-193 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475705.2023.2200890 Date de publication en ligne : 18/04/2023 En ligne : https://doi.org/10.1080/19475705.2023.2200890 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103074 [article]