Preprints
https://doi.org/10.5194/gmd-2018-305
https://doi.org/10.5194/gmd-2018-305

Submitted as: development and technical paper 04 Feb 2019

Submitted as: development and technical paper | 04 Feb 2019

Review status: this preprint was under review for the journal GMD but the revision was not accepted.

Semantic Description and Complete Computer Characterization of Structural Geological Models

Xianglin Zhan1, Jiandong Liang2, Cai Lu1, and Guangmin Hu2 Xianglin Zhan et al.
  • 1School of Information and Communication Engineering, University of Electronic and Science Technology of China, Chengdu, 611731, China
  • 2School of Resources and Environment, University of Electronic and Science Technology of China, Chengdu, 611731, China

Abstract. A structural geological model is an important basis for the understanding of subsurface structures and exploration of mineral resources, especially petroleum reservoirs. In the field of geological modelling, the lack of a well-defined semantic level description and corresponding computer characterization method hinders its application. In this paper, we propose the semantic descriptions for structural geological models in order to facilitate computer based processing of geological semantics. A multi-level heterogeneous network is proposed to characterize the semantic description for this purpose. The semantic description of a structural geological model gives a complete description of structural units (called semantic entities) of structural models. Basic semantic entities include points, lines, interfaces, bodies, formations and advanced semantic entities include stratified structures/massive structures, planar structures, linear structures. Semantic relations represent the logical relationships among these semantic entities. The multi-level heterogeneous network contains complete information of structural geological models for both geometry and geology. Hence, it has a one-to-one correspondence with a structural geological model. In particular, we propose a bottom-up and top-down integrating structural modelling method based on semantic descriptions. This approach aims to address defects of the existing structural modelling methods that can only carry out bottom-up modelling. Because the addition of semantic information, it improves the adaptability of structural modelling to complex structures and enhances modelling efficiency.

Xianglin Zhan et al.

 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Xianglin Zhan et al.

Data sets

Data sets of Xinjiang model and Chongqin model X. Zhan https://doi.org/10.5281/zenodo.2481084

Model code and software

Code for semantic description extraction, structural modeling with semantic descriptionS and calculating points on intersection lines X. Zhan https://doi.org/10.5281/zenodo.2481084

Xianglin Zhan et al.

Viewed

Total article views: 1,800 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,552 211 37 1,800 44 40
  • HTML: 1,552
  • PDF: 211
  • XML: 37
  • Total: 1,800
  • BibTeX: 44
  • EndNote: 40
Views and downloads (calculated since 04 Feb 2019)
Cumulative views and downloads (calculated since 04 Feb 2019)

Viewed (geographical distribution)

Total article views: 1,369 (including HTML, PDF, and XML) Thereof 1,246 with geography defined and 123 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 26 Oct 2021
Download
Short summary
We proposed the semantic descriptions for structural geological models in order to facilitate computer based processing of geological semantics. The semantic description is a complete representation of the structural model. And we use the multi-level heterogeneous network to be the computer characterization of the semantic description. Semantic descriptions can also be used to constrain structure modeling which forms a top-down modeling process. We validated the effectiveness with actual data.