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Implicit modeling of narrow vein type ore bodies based on Boolean combination constraints
Deyun Zhong1,2, Ju Zhang1,2, Liguan Wang1,2
1School of Resources and Safety Engineering, Central South University, Changsha, 410083, China.
This study introduces an automated method for modeling narrow vein ore bodies using Boolean constraints. The approach constructs implicit functions for geological surfaces, enabling precise digital representation of complex ore deposits.
Area of Science:
- Geological modeling
- Computational geology
- Mineral resource estimation
Background:
- Traditional ore body modeling often relies on direct interpolation, which can be insufficient for complex geological structures.
- Accurate modeling of narrow vein deposits is crucial for efficient mineral extraction and resource management.
Purpose of the Study:
- To develop an automated modeling method for narrow vein type ore bodies.
- To utilize Boolean combination constraints for constructing implicit functions of geological surfaces.
- To provide a flexible approach for incorporating geological constraints into the modeling process.
Main Methods:
- Constructing implicit functions for the hanging wall and foot wall surfaces separately.
- Combining these implicit functions using Boolean operations to define the complete ore body.
- Developing specific rules for vein thickness and boundary constraints to model complex surfaces.
Main Results:
- The proposed method successfully models narrow vein type ore bodies, such as gold and mineral sand deposits.
- Experimental results using real geological data validate the suitability of the approach.
- The method effectively handles ore bodies that are large in two dimensions and narrow in the third.
Conclusions:
- The automated Boolean constraint-based method offers an effective solution for modeling narrow vein ore bodies.
- This approach enhances the accuracy and efficiency of geological modeling for specific deposit types.
- The developed rules allow for detailed specification of geological features, improving model fidelity.
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