A detection method of urban underground geological anomalies in the United Kingdom based on feature fusion
Engineering geological conditions include the nature of rock and soil, geological structure, landform, hydrogeological conditions, and adverse geological processes. Among them, faults, fissures, folds, karst, and lithology changes seriously affect the safety and construction cost of mountain tunnels...
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| Veröffentlicht in: | Earth sciences research journal 2022-09, Vol.26 (3), p.255-262 |
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| description | Engineering geological conditions include the nature of rock and soil, geological structure, landform, hydrogeological conditions, and adverse geological processes. Among them, faults, fissures, folds, karst, and lithology changes seriously affect the safety and construction cost of mountain tunnels, hydraulic tunnels, and other projects. For this reason, a new method based on feature fusion is proposed to detect the geological anomalies in London and Sheffield. It established a 3D raster data model oriented to attribute information modeling and visualization of urban underground space to obtain geological data. Based on this acquired data, authors adopted the feature-level fusion extraction method based on the multi-attribute geological abnormal body to extract, fuse, fill and surface the multi-attribute data of underground space geological data. Smooth processing can realize the detection of abnormal geological bodies in underground space. It has been proved that this method can be used in geological data display, feature extraction, feature fusion, and abnormal physical examination. |
| doi_str_mv | 10.15446/esrj.v26n3.103605 |
| format | Article |
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Among them, faults, fissures, folds, karst, and lithology changes seriously affect the safety and construction cost of mountain tunnels, hydraulic tunnels, and other projects. For this reason, a new method based on feature fusion is proposed to detect the geological anomalies in London and Sheffield. It established a 3D raster data model oriented to attribute information modeling and visualization of urban underground space to obtain geological data. Based on this acquired data, authors adopted the feature-level fusion extraction method based on the multi-attribute geological abnormal body to extract, fuse, fill and surface the multi-attribute data of underground space geological data. Smooth processing can realize the detection of abnormal geological bodies in underground space. It has been proved that this method can be used in geological data display, feature extraction, feature fusion, and abnormal physical examination.</description><identifier>ISSN: 1794-6190</identifier><identifier>EISSN: 2339-3459</identifier><identifier>DOI: 10.15446/esrj.v26n3.103605</identifier><language>eng</language><publisher>Bogata: Universidad Nacional de Colombia, Departamento de Geociencias</publisher><subject>Analysis ; Anomalies ; Construction costs ; Data acquisition ; Engineering geology ; Fault detection ; Feature extraction ; Geological data ; Geological processes ; Geological structures ; Geology ; GEOSCIENCES, MULTIDISCIPLINARY ; Hydrogeology ; Karst ; Landforms ; Lithofacies ; Lithology ; Methods ; Mountain tunnels ; Soil structure ; Tunnel construction ; Tunnels ; Underground structures ; Visualization (Computers)</subject><ispartof>Earth sciences research journal, 2022-09, Vol.26 (3), p.255-262</ispartof><rights>COPYRIGHT 2022 Universidad Nacional de Colombia, Departamento de Geociencias</rights><rights>2022. This work is published under https://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>This work is licensed under a Creative Commons Attribution 4.0 International License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c262t-2892773c1c3f641c61f0296f0e4c0c02449059d5d84372b4aa697f42caff72733</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids></links><search><creatorcontrib>Liu, Xuemei</creatorcontrib><title>A detection method of urban underground geological anomalies in the United Kingdom based on feature fusion</title><title>Earth sciences research journal</title><addtitle>Earth Sci. Res. J</addtitle><description>Engineering geological conditions include the nature of rock and soil, geological structure, landform, hydrogeological conditions, and adverse geological processes. Among them, faults, fissures, folds, karst, and lithology changes seriously affect the safety and construction cost of mountain tunnels, hydraulic tunnels, and other projects. For this reason, a new method based on feature fusion is proposed to detect the geological anomalies in London and Sheffield. It established a 3D raster data model oriented to attribute information modeling and visualization of urban underground space to obtain geological data. Based on this acquired data, authors adopted the feature-level fusion extraction method based on the multi-attribute geological abnormal body to extract, fuse, fill and surface the multi-attribute data of underground space geological data. Smooth processing can realize the detection of abnormal geological bodies in underground space. It has been proved that this method can be used in geological data display, feature extraction, feature fusion, and abnormal physical examination.</description><subject>Analysis</subject><subject>Anomalies</subject><subject>Construction costs</subject><subject>Data acquisition</subject><subject>Engineering geology</subject><subject>Fault detection</subject><subject>Feature extraction</subject><subject>Geological data</subject><subject>Geological processes</subject><subject>Geological structures</subject><subject>Geology</subject><subject>GEOSCIENCES, MULTIDISCIPLINARY</subject><subject>Hydrogeology</subject><subject>Karst</subject><subject>Landforms</subject><subject>Lithofacies</subject><subject>Lithology</subject><subject>Methods</subject><subject>Mountain tunnels</subject><subject>Soil structure</subject><subject>Tunnel construction</subject><subject>Tunnels</subject><subject>Underground structures</subject><subject>Visualization 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fusion</title><author>Liu, Xuemei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c262t-2892773c1c3f641c61f0296f0e4c0c02449059d5d84372b4aa697f42caff72733</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Analysis</topic><topic>Anomalies</topic><topic>Construction costs</topic><topic>Data acquisition</topic><topic>Engineering geology</topic><topic>Fault detection</topic><topic>Feature extraction</topic><topic>Geological data</topic><topic>Geological processes</topic><topic>Geological structures</topic><topic>Geology</topic><topic>GEOSCIENCES, MULTIDISCIPLINARY</topic><topic>Hydrogeology</topic><topic>Karst</topic><topic>Landforms</topic><topic>Lithofacies</topic><topic>Lithology</topic><topic>Methods</topic><topic>Mountain tunnels</topic><topic>Soil structure</topic><topic>Tunnel construction</topic><topic>Tunnels</topic><topic>Underground 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| subjects | Analysis Anomalies Construction costs Data acquisition Engineering geology Fault detection Feature extraction Geological data Geological processes Geological structures Geology GEOSCIENCES, MULTIDISCIPLINARY Hydrogeology Karst Landforms Lithofacies Lithology Methods Mountain tunnels Soil structure Tunnel construction Tunnels Underground structures Visualization (Computers) |
| title | A detection method of urban underground geological anomalies in the United Kingdom based on feature fusion |
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