{"title":"A Methodology for Automatically 3D Geological Modeling Based on Geophysical Data Grids","authors":"Xiangyu Yu, Yixian Xu","doi":"10.1109/ICICTA.2015.19","DOIUrl":null,"url":null,"abstract":"Using 3D visualization models to exhibit geological structure has become a trend in geological studies. Compared to 2D geological mapping, 3D geological modeling is dependent on more geological sampling information. In many cases, however, the geological sampling information is difficult to acquire by drilling (especially for deep subsurface information). Geophysical methods (e.g., Gravity, seismic, and electric) have become the major tools in geological modeling. Because the geophysical data are recorded in a data grid, people must extract the geological information from various data grids acquired through different geophysical methods and subsequently integrate the information to manually construct a 3D geological model. This approach usually causes inconvenience and inefficiencies in practice. Therefore, we propose a methodology of automatically 3D geological modeling based on geophysical data grids. The method first constructs visualization models from different geophysical data grids and subsequently integrates these models for interpretation using mapping rules learned from physical properties of rock samples measured in a laboratory and finally converts the interpreted visualization model to a 3D geological model. With the application in the practical work, the result demonstrates that the methodology can effectively solve problems of 3D geological modeling in the case of enriched geophysical data lacking sufficient geological sampling information.","PeriodicalId":231694,"journal":{"name":"2015 8th International Conference on Intelligent Computation Technology and Automation (ICICTA)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 8th International Conference on Intelligent Computation Technology and Automation (ICICTA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICTA.2015.19","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Using 3D visualization models to exhibit geological structure has become a trend in geological studies. Compared to 2D geological mapping, 3D geological modeling is dependent on more geological sampling information. In many cases, however, the geological sampling information is difficult to acquire by drilling (especially for deep subsurface information). Geophysical methods (e.g., Gravity, seismic, and electric) have become the major tools in geological modeling. Because the geophysical data are recorded in a data grid, people must extract the geological information from various data grids acquired through different geophysical methods and subsequently integrate the information to manually construct a 3D geological model. This approach usually causes inconvenience and inefficiencies in practice. Therefore, we propose a methodology of automatically 3D geological modeling based on geophysical data grids. The method first constructs visualization models from different geophysical data grids and subsequently integrates these models for interpretation using mapping rules learned from physical properties of rock samples measured in a laboratory and finally converts the interpreted visualization model to a 3D geological model. With the application in the practical work, the result demonstrates that the methodology can effectively solve problems of 3D geological modeling in the case of enriched geophysical data lacking sufficient geological sampling information.