Big Data-Driven 3D Visualization Analysis System for Promoting Regional-Scale Digital Geological Exploration
As geological exploration technology advances, geoscience relies on digitization and intelligence to address challenges such as data fragmentation, multi-source heterogeneity, and visual analysis. This study develops a big data-driven 3D visual analysis system for regional-scale applications. The sy...
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| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/7/4003 |
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| author | Yiping Tian Jiongqi Wu Genshen Chen Gang Liu Xialin Zhang |
| author_facet | Yiping Tian Jiongqi Wu Genshen Chen Gang Liu Xialin Zhang |
| author_sort | Yiping Tian |
| collection | DOAJ |
| description | As geological exploration technology advances, geoscience relies on digitization and intelligence to address challenges such as data fragmentation, multi-source heterogeneity, and visual analysis. This study develops a big data-driven 3D visual analysis system for regional-scale applications. The system integrates three core technological components: (1) a heterogeneous cloud resource scheduling method employing an optimized CMMN algorithm with unified cloud API standardization to enhance task distribution efficiency; (2) a block model-based dynamic data aggregation approach utilizing semantic unification and attribute mapping for multi-source geological data integration; (3) a GPU-accelerated rendering framework implementing occlusion culling and batch processing to optimize 3D visualization performance. Experimental validation shows the improved CMMN algorithm reduces cloud task completion time by 2.37% while increasing resource utilization by 0.652% compared with conventional methods. The dynamic data model integrates 12 geological data types across eight categories through semantic mapping. Rendering optimizations achieve a 93.7% memory reduction and 60.6% faster visualization compared with baseline approaches. This system provides robust decision support and reliable tools for the digital transformation of geoscience work. |
| format | Article |
| id | doaj-art-237904bd0c31402299f83008f9d03aa4 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-237904bd0c31402299f83008f9d03aa42025-08-20T03:06:20ZengMDPI AGApplied Sciences2076-34172025-04-01157400310.3390/app15074003Big Data-Driven 3D Visualization Analysis System for Promoting Regional-Scale Digital Geological ExplorationYiping Tian0Jiongqi Wu1Genshen Chen2Gang Liu3Xialin Zhang4School of Computer Science, China University of Geosciences, Wuhan 430074, ChinaSchool of Computer Science, China University of Geosciences, Wuhan 430074, ChinaSchool of Computer Science, China University of Geosciences, Wuhan 430074, ChinaSchool of Computer Science, China University of Geosciences, Wuhan 430074, ChinaSchool of Computer Science, China University of Geosciences, Wuhan 430074, ChinaAs geological exploration technology advances, geoscience relies on digitization and intelligence to address challenges such as data fragmentation, multi-source heterogeneity, and visual analysis. This study develops a big data-driven 3D visual analysis system for regional-scale applications. The system integrates three core technological components: (1) a heterogeneous cloud resource scheduling method employing an optimized CMMN algorithm with unified cloud API standardization to enhance task distribution efficiency; (2) a block model-based dynamic data aggregation approach utilizing semantic unification and attribute mapping for multi-source geological data integration; (3) a GPU-accelerated rendering framework implementing occlusion culling and batch processing to optimize 3D visualization performance. Experimental validation shows the improved CMMN algorithm reduces cloud task completion time by 2.37% while increasing resource utilization by 0.652% compared with conventional methods. The dynamic data model integrates 12 geological data types across eight categories through semantic mapping. Rendering optimizations achieve a 93.7% memory reduction and 60.6% faster visualization compared with baseline approaches. This system provides robust decision support and reliable tools for the digital transformation of geoscience work.https://www.mdpi.com/2076-3417/15/7/4003geological big datadigital exploration3D visualizationvisual analyticsGIS |
| spellingShingle | Yiping Tian Jiongqi Wu Genshen Chen Gang Liu Xialin Zhang Big Data-Driven 3D Visualization Analysis System for Promoting Regional-Scale Digital Geological Exploration Applied Sciences geological big data digital exploration 3D visualization visual analytics GIS |
| title | Big Data-Driven 3D Visualization Analysis System for Promoting Regional-Scale Digital Geological Exploration |
| title_full | Big Data-Driven 3D Visualization Analysis System for Promoting Regional-Scale Digital Geological Exploration |
| title_fullStr | Big Data-Driven 3D Visualization Analysis System for Promoting Regional-Scale Digital Geological Exploration |
| title_full_unstemmed | Big Data-Driven 3D Visualization Analysis System for Promoting Regional-Scale Digital Geological Exploration |
| title_short | Big Data-Driven 3D Visualization Analysis System for Promoting Regional-Scale Digital Geological Exploration |
| title_sort | big data driven 3d visualization analysis system for promoting regional scale digital geological exploration |
| topic | geological big data digital exploration 3D visualization visual analytics GIS |
| url | https://www.mdpi.com/2076-3417/15/7/4003 |
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