Using fractal model and factor analysis for FACA modeling and its application in deep mineral prediction - Feilong Qin; Hongjin Zhu; Yu Feng; ShiCheng Yu (PLoS ONE, 2026)
Objective This paper designs a FACA model for deep mineral prediction in actual mining areas. Method The spatial distribution of geochemical anomalies was consistent with the concentration--area (C-A), this paper established a C–A model for geochemical anomaly extraction. Based on mineral resources formed by multiple element combinations, factor analysis (FA) was used to obtain the different combinations and comprehensive information of elements. On this basis, a new FACA model was designed for mineral prediction using the FA and C–A. Results The proposed FACA model was applied to mineral prediction in the Jiguanzui copper-gold mining area in China. The elements in the study area were divided into four combinations. The thresholds of single element and element combination anomalies were identified. Through diagnostic testing, the abnormal distributions of geochemical elements were consistent with their theoretical distributions, and the comprehensive abnormal distribution area of elements was consistent with the distribution of the actual ore bodies, demonstrating that the designed FACA algorithm of this paper was reasonable. Conclusions A new blind ore body in the study area is predicted by using FACA model, positioned at a depth ranging from approximately 1120m to 1150m below ground, between drill holes ZK02618 and KZK23. These findings hold significant implications for mineral exploration efforts.