Primary Controlling Factors of Apatite Trace Element Composition and Implications for Exploration in Orogenic Gold Deposits

Author:

Cao Genshen12,Chen Huayong123ORCID,Zhang Yu4ORCID,Sun Weipin12,Zhao Junfeng12ORCID,Zhao Hongtao4,Wang Hao12

Affiliation:

1. Key Laboratory of Mineralogy and Metallogeny Guangzhou Institute of Geochemistry Chinese Academy of Sciences Guangzhou China

2. College of Earth and Planetary Science University of Chinese Academy of Science Beijing China

3. Guangdong Provincial Key Laboratory of Mineral Physics and Materials Guangzhou China

4. Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring School of Geoscience and Info‐Physics Central South University Changsha China

Abstract

AbstractSignificant and readily accessible orogenic gold deposits have been previously recognized, exploited, and progressively depleted. Innovative approaches are required to discover new and deeply buried deposits. Recently, trace element variations in apatite have been used to distinguish fertile and barren environments as reliable mineral exploration tools. In this study, machine learning models using Random Forest and Deep Neutral Network are utilized to assess the fertility of quartz veins and altered zones in the orogenic gold systems. The two models have been trained using trace element data of apatite, and the performance of both models yield good classification accuracy (∼90% on average) with low false positive rates. Feature importance analysis (Gini decrease and hidden layer weights) suggest that Pb, As, U, Sr, Eu, Mn, and Fe are the important parameters. Arsenic, U, Eu, Mn, and Fe are redox‐sensitive elements, with their concentrations responding to changes in fluid redox conditions. Strontium primarily originates from the breakdown of plagioclase, which is more likely to occur under oxidizing fluid conditions. Therefore, we infer that the main controlling factor of the models is the redox conditions. The two distinct models consistently highlight the most significant contribution of Pb to this differentiation, even though Pb is not a redox‐sensitive element and can only substitute for Ca2+ in apatite as Pb2+. We infer that the high contribution of Pb may be attributed to the potential transportation of Au in the form of a Pb‐(Bi)‐Au melt, and the Pb content in apatite is influenced by the Pb content in the melt, fluid oxygen, and sulfur fugacity. We also propose a novel discriminant plot using Linear Discriminant Analysis (LDA) to assess the mineralization potential in quartz veins and alteration zones based on apatite trace element data. The machine learning and LDA results suggest that apatite trace elements could be used effectively in the future orogenic gold deposit exploration.

Publisher

American Geophysical Union (AGU)

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