Abstract:

Carlin-type gold deposits are an important reservoir of gold. Due to the gradual depletion of shallow surface gold resources, there is an urgent need for new prospecting methods to explore deep and hidden areas. The advent of the big data era has opened up new prospecting ideas. Association rule algorithm one of the most commonly used mining algorithms and can be used to effectively mine the inherent correlation between data items in large data sets. In this study, association rule mining is used to analyze the correlation between trace elements and gold mineralization in major Carlin-type gold deposits in the Yunnan-Guizhou-Guangxi “Golden Triangle” region. Combined with element migration and enrichment patterns, elemental anomaly combinations are extracted, and quantitative prospecting indicators are established. The elemental anomaly combinations are divided into elements with strong positive correlation and significantly enriched (As, Sb, Hg, Tl, Ag, W, Rb), indicating sulfidation and clayification (Rb); elements with strong positive correlation and slightly enriched (Zr, Th, Ta, Nb, Hf) or with strong negative correlation and strongly depleted (Li, Sr), indicating decarbonation; elements with strong positive correlation and slightly enriched (Sn, Zn, Ni, V, Co, Cu), likely reflecting their low contents in ore-forming fluids; and elements with weak correlation and not enriched (Cd, Pb, Ba, Bi, U, Mo)—these elements show no significant correlation with gold mineralization. The elemental anomaly combinations obtained by big data approach is consistent with previous understanding of the genesis of Au deposits, i.e., Au is mainly formed under decarbonation and sulfidation processes accompanied by significant clayification, in which sulfidation is the main genetic mechanism. Through association rule mining, quantitative prospecting indicators are established: For sulfidation related elements (As, Hg, Sb, Tl, W, Ag, Rb), when the number of medium-high content elements in samples ≥1, 2, 3, 4, or 5, the corresponding Au contents ≥4.5×10^-9, 47.0×10^-9, 150×10^-9, 500×10^-9, or 1000×10^-9; when the number of high-content elements ≥1, 2, or 3, the corresponding Au contents ≥150×10^-9, 500×10^-9, or 1000×10^-9; during prospecting, both indicators should be used to ensure efficient delineation of ore bodies, without outcrops. For decarbonation related elements (Zr, Th, Ta, Nb, Hf), decarbonation is indicated when elemental content anomaly occurs at any two of the elements in samples. The method developed in this study for establishing quantitative prospecting indicators based on association rule algorithms should provide new ideas for other types of mineral deposits.

Key words: geological big data, association rule algorithm, Carlin-type gold deposit, element enrichment law, factors of control, prospecting indicators