Using fractal model and factor analysis for FACA modeling and its application in deep mineral prediction

Area of Science:

• Geoscience
• Economic Geology
• Geochemistry

Background:

• Geochemical anomalies often indicate potential mineral deposits.
• Integrating geochemical data with spatial analysis is crucial for effective mineral exploration.
• Traditional methods may not fully capture complex elemental relationships in deep mineral prediction.

Purpose of the Study:

• To design and validate a novel Factor Analysis-Concentration Area (FACA) model for deep mineral prediction.
• To improve the accuracy of identifying geochemical anomalies in actual mining areas.
• To facilitate the discovery of blind ore bodies.

Main Methods:

• Developed a FACA model by combining Factor Analysis (FA) for elemental combination analysis and Concentration-Area (C-A) modeling for anomaly extraction.
• Applied FA to identify elemental combinations indicative of mineral resources.
• Utilized the C-A model to extract geochemical anomalies based on spatial distribution patterns.

Main Results:

• The FACA model was successfully applied to the Jiguanzui copper-gold mining area in China.
• Elements were classified into four combinations, and anomaly thresholds were established.
• The model accurately predicted the distribution of geochemical anomalies, aligning with known ore body distributions.

Conclusions:

• The FACA model effectively predicted a new blind ore body at a depth of 1120m-1150m.
• The model's consistency with theoretical distributions and actual ore bodies validates its efficacy.
• This predictive capability offers significant advancements for mineral exploration strategies.