Three-Dimensional Geological Modeling Method Based on Potential Vector Fields
Source abstract
Abstract
With the development of 3D geological modeling, implicit modeling methods have gradually gained popularity. However, existing potential field methods cannot directly represent unconformable geological interfaces. In response, an implicit modeling method based on a potential vector field was proposed, which generates geological surface models through the potential vector field method and generalized marching cubes algorithm, and visualizes the modeling results. An experiment was conducted on the study area of a certain mineral deposit, and a 3D geological surface model with consistency and no topological errors was established, demonstrating the effectiveness of the method for the surface modeling of unconformity geological interfaces.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Geological Modeling and Analysis · Image Processing and 3D Reconstruction · Hydrocarbon exploration and reservoir analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software, Qingdao 266580, China
Funding
National Natural Science Foundation of China · National Key Research and Development Program of China
References (35)
Show source-provided references
1. Inverse modelling in geology by interactive evolutionary computation10.1016/S0191-8141(03)00010-5 ↗
2. Geology-driven modeling: A new probabilistic approach for incorporating uncertain geological interpretations in 3D geological modeling10.1016/j.enggeo.2022.106833 ↗
3. Towards automatic and rapid 3D geological modelling of urban sedimentary strata from a large amount of borehole data using a parallel solution of implicit equations10.1007/s12145-023-01164-8 ↗
4. Explicit-implicit-integrated 3-D geological modelling approach: A case study of the Xianyan Demolition Volcano (Fujian, China)10.1016/j.tecto.2020.228648 ↗
5. Advancing geological modelling and geodata management: A web-based system with AI assessment in Singapore
6. Geological mapping goes 3-D in response to societal needs10.1130/GSATG86GW.1 ↗
7. A new 3D geological model and interpretation of structural evolution of the world-class Rio Tinto VMS deposit, Iberian Pyrite Belt (Spain)10.1016/j.oregeorev.2015.06.006 ↗
8. GSIS: A 3D geological multi-body modeling system from netty cross-sections with topology10.1016/j.cageo.2009.11.003 ↗
9. Section-constrained local geological interface dynamic updating method based on the HRBF surface10.1016/j.jsg.2017.11.017 ↗
10. A 3D geological model for the Ruiz-Tolima Volcanic Massif (Colombia): Assessment of geological uncertainty using a stochastic approach based on Bézier curve design10.1016/j.tecto.2016.09.011 ↗
11. Cowan, E.J., Beatson, R.K., Ross, H.J., Fright, W.R., McLennan, T.J., Evans, T.R., Carr, J.C., Lane, R.J., Bright, D.V., and Gillman, A.J. (2003, January 17–19). Practical implicit geological modelling. Proceedings of the 5th International Mining Geology Conference, Bendigo, VIC, Australia.
12. Vollgger, S.A., Cruden, A.R., and Cowan, J.E. (2013, January 12–15). 3D implicit geological modelling of a gold deposit from a structural geologist’s point of view. Proceedings of the 12th SGA Biennial Meeting—Mineral Deposit Research for a High-Tech World, Uppsala, Sweden.
13. Geological modelling from field data and geological knowledge: Part I. Modelling method coupling 3D potential-field interpolation and geological rules10.1016/j.pepi.2008.06.013 ↗
14. A review of 3D geological modeling technology and methods
15. Li, J., Liu, P., Wang, X., Cui, H., and Ma, Y. (2022). 3D geological implicit modeling method of regular voxel splitting based on layered interpolation data. Sci. Rep., 12.10.1038/s41598-022-17231-x ↗
16. Wang, J., Zhao, H., Bi, L., and Wang, L. (2018). Implicit 3D modeling of ore body from geological boreholes data using hermite radial basis functions. Minerals, 8.10.3390/min8100443 ↗
17. Implicit modeling of complex orebody with constraints of geological rules10.1016/S1003-6326(19)65145-9 ↗
18. Wilde, B.J., and Deutsch, C.V. (2012, January 11–15). Kriging and simulation in presence of stationary domains: Developments in boundary modeling. Proceedings of the Geostatistics Oslo 2012, Oslo, Norway.10.1007/978-94-007-4153-9_23 ↗
19. Geological modeling by an indicator kriging approach applied to a limestone deposit in Indiara city-Goiás10.1590/0370-44672016700113 ↗
20. Three-dimensional site characterization with borehole data—A case study of Suzhou area10.1016/j.enggeo.2017.12.019 ↗
21. Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization10.5194/se-9-385-2018 ↗
22. Hermite radial basis functions implicits10.1111/j.1467-8659.2010.01785.x ↗
23. Automatic and dynamic updating of three-dimensional ore body models from borehole and excavation data using the implicit function HRBF10.1016/j.oregeorev.2022.105018 ↗
24. Collaborative 3D geological modeling analysis based on multi-source data standard10.1016/j.enggeo.2018.10.001 ↗
25. Regional dome evolution and its control on ore-grade distribution: Insights from 3D implicit modelling of the Navachab gold deposit, Namibia10.1016/j.oregeorev.2015.02.020 ↗
26. Three-dimensional geological modeling and spatial analysis from geotechnical borehole data using an implicit surface and marching tetrahedra algorithm10.1016/j.enggeo.2021.106047 ↗
27. Lorensen, W.E., and Cline, H.E. (1998). Marching cubes: A high resolution 3D surface construction algorithm. Seminal Graphics: Pioneering Efforts that Shaped the Field, Association for Computing Machinery.10.1145/280811.281026 ↗
28. Exploiting triangulated surface extraction using tetrahedral decomposition10.1109/2945.485620 ↗
29. Comparison and Evaluation of Marching Cubes and Marching Tetrahedra
30. Nico, G. (2005, January 12–18). LU-GPU: Efficient algorithms for solving dense linear systems on graphics hardware. Proceedings of the SC’05: Proceedings of the 2005 ACM/IEEE Conference on Supercomputing, Seattle, WA, USA.
31. Volkov, V., and Demmel, J. (2025, January 14). LU, QR and Cholesky Factorizations Using Vector Capabilities of GPUs. Available online: https://bebop.cs.berkeley.edu/pubs/volkov2008-gpu-factorizations.pdf.
32. Agullo, E., Augonnet, C., Dongarra, J., Faverge, M., Ltaief, H., Thibault, S., and Tomov, S. (2011, January 16–20). QR Factorization on a Multicore Node Enhanced with Multiple GPU Accelerators. Proceedings of the 2011 IEEE International Parallel & Distributed Processing Symposium, Anchorage, AK, USA.10.1109/IPDPS.2011.90 ↗
33. Multiquadric equations of topography and other irregular surfaces10.1029/JB076i008p01905 ↗
34. Scattered data interpolation: Tests of some methods
35. Interpolation and approximation of 3-D and 4-D scattered data10.1016/0898-1221(87)90043-5 ↗