Advertisement

A methodology for 3D geological mapping and implementation

  • Bo Cai
  • Jianhui Zhao
  • Xiangyu Yu
Article
  • 15 Downloads

Abstract

Using 3D visualization models to exhibit geological structure has become a trend in geological studies. Compared to 2D geological mapping, 3D geological mapping is dependent on more geological sampling information. Geophysical methods (e.g., gravity, seismic, and electric) thus become the major tools in 3D geological mapping. In traditional works, 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 3D geological mapping. It first constructs visualization models from different geophysical data grids and subsequently integrates these models for interpretation and finally converts to a 3D geological model. Based on this methodology, we implement the corresponding system which can accomplish the above process automatically. As an example, we gave a detail description for constructing the 3D lithological model by the methodology mentioned above with the geological survey data acquired in the western Jungger, Xinjiang of China. The demonstration show us that the methodology can effectively solve the matter of 3D geological modeling in case of enriched in geophysical data but in lack of sufficient geological sampling information.

Keywords

3D geological mapping Geophysical data interpretation Data visualization Knowledge extraction 

Notes

References

  1. 1.
    Brown CJ, Collier JS (2008) Mapping benthic habitat in regions of gradational substrata: an automated approach utilizing geophysical, geological, and biological relationships. Estuar Coast Shelf Sci 78(1):203–214CrossRefGoogle Scholar
  2. 2.
    Chalke T, Mcgaughey J, Perron G (2012) 3D software technology for structural interpretation and modeling. Structural Geology and Resources:16–20Google Scholar
  3. 3.
    Dantas EL, Silva AM, Almeida L (2003) Old geophysical data applied to modern geological mapping problems: a case-study in the Serid Belt, Ne Brazil. Braz J Geol 33(2):65–72Google Scholar
  4. 4.
    Gong J, Cheng P, Wang Y (2004) Three-dimensional modeling and application in geological exploration engineering. Comput Geosci 30(4):391–404CrossRefGoogle Scholar
  5. 5.
    Huang W, Ding L (2010) Identification of fuzzy inference system based on information granulation. KSII Trans Internet Inf Syst 4(4):575–594Google Scholar
  6. 6.
    Jaques AL, Wellman P, Whitaker A, Wyborn D (1997) High-resolution geophysics in modern geological mapping. AGSO J Aust Geol Geophys 17:159–174Google Scholar
  7. 7.
    Jessell M (2001) Three-dimensional geological modeling of potential-field data. Comput Geosci 27(4):455–465CrossRefGoogle Scholar
  8. 8.
    Lim J (2005) Reservoir properties determination using fuzzy logic and neural networks. J Pet Sci Eng 49(3):182–192CrossRefGoogle Scholar
  9. 9.
    Malehmir A, Hans T, Ari T (2008) The Paleoproterozoic Kristine-berg mining area, northern Sweden: results from integrated 3D geophysical and geologic modeling, and implications for targeting ore deposits. Geophysics 74(1):B9–B22CrossRefGoogle Scholar
  10. 10.
    Martelet G, Calcagno P, Gumiaux C (2004) Integrated 3D geophysical and geological modeling of the Hercynian suture zone in the Champtoceaux area (South Brittany, France). Tectonophysics 382(1):117–128CrossRefGoogle Scholar
  11. 11.
    McGaughey J, Milkereit B (2007) Geological models, rock properties, and the 3D inversion of geophysical data. In: Proceedings of exploration 7:473–483Google Scholar
  12. 12.
    Ning F, Wei J (2009) 3D visualization of stratum with faults based on VTK. 2009 International Conference on Computational Intelligence and Software Engineering, p 1–3Google Scholar
  13. 13.
    Russell HAJ, Thorleifsonm LH, Berg RC (2013) Overview-3D geological mapping: developing more widespread adoption by geological survey organizations. 3D Mapping Workshop OFR 13-02 7:7–14Google Scholar
  14. 14.
    Trampert J, van der Hilst RD (2005) Towards a quantitative interpretation of global seismic tomography. Geophys Monogr Ser 160:47–64Google Scholar
  15. 15.
    Wu Q, Xu H, Zou X (2005) An effective method for 3D geological modeling with multi-source data integration. Comput Geosci 31(1):35–43CrossRefGoogle Scholar
  16. 16.
    Yao YY (2001) Information granulation and rough set approximation. Int J Intell Syst 16(1):87–104MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Yu X, Xu Y (2013) Building a 3D visualization system for the geological survey. IEEE International Conference on Computer Science and Automation Engineering, p 1238-1240Google Scholar
  18. 18.
    Yu X, Xu Y (2014) Building a visual mapping system from the geophysical data to the geological property. The 6th International Conference on Environmental and Engineering Geophysics, p 224-228Google Scholar
  19. 19.
    Yu X, Xu Y (2015) A methodology of deep 3D geological modeling based on the geophysical data: earth science. J China Univ Geosci 40(3):419–424Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Information CenterWuhan UniversityWuhanChina
  2. 2.School of ComputerWuhan UniversityWuhanChina
  3. 3.Hubei Subsurface Multi-scale Imaging Key Laboratory, Institute of Geophysics and GeomaticsChina University of GeosciencesWuhanChina

Personalised recommendations