Mineral resource prediction model construction method based on complex geological background
By constructing a difference comparison and interference coupling field model between the three-dimensional ideal geological background field and the actual geological background field, and combining it with a knowledge graph-driven interference factor system, the adaptability and accuracy problems of mineral resource prediction under complex geological backgrounds are solved, and efficient mineral resource prediction is achieved.
Patent Information
- Application Number
- CN202511207066.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-27
- Publication Date
- 2025-10-03
- Estimated Expiration
- 2045-08-27
AI Technical Summary
Existing technologies are difficult to effectively express dynamic evolution in mineral resource prediction under complex geological backgrounds, have poor adaptability and limited interpretability, and traditional models lack scalability and generalization capabilities, making it difficult to meet the needs of refined prediction of deep and concealed mineral deposits.
A dual difference comparison between the three-dimensional ideal geological background field without mineralization interference and the actual geological background field is constructed. Combined with the theoretical response model of the interference coupling field, mineralization-related anomalies are identified through a voxel-level spatial coupling algorithm. A knowledge graph-driven interference factor system is used for intelligent matching and data integration to form a parameterized and spatialized mineral resource prediction model.
It realizes cross-validation of mineral resource prediction under complex geological background, improves the reliability and intelligence level of prediction results, adapts to the prediction needs of different geological backgrounds, overcomes the multi-solution problem of traditional methods, and significantly improves the flexibility and accuracy of the mineral resource prediction system.