锆石微量元素对成矿岩体的判别——来自大数据思维的应用

华南钦杭结合带燕山期岩浆活动异常活跃,且具有较明显的成矿专属性。近年来微区测试技术日益成熟,积累了大量锆石微量数据。通过全体数据挖掘的思维方法,对前人发表的数据进行了进一步数据挖掘,利用锆石稀土元素对岩体成矿潜力进行判别,探讨有效的找矿地球化学标志。利用Python语言编程,对采用的13种稀土元素及元素比值进行穷举式组合,获得了4095个二元图解及121485个三元图解,并设计筛选算法,自动筛选出能有效区分锆石母岩成矿类型的图解。结果表明,锆石稀土元素含量及比值图解对不同成矿类型岩体的区分程度各异:与Ce、Eu有关的地球化学指标可以较清晰地对斑岩铜矿和钨锡(锡)矿床进行判别,这可能与岩体的氧逸度和含水量有关。此外,还挖掘出一些新的元素组合图解,如Dy/Lu-Er/Lu、Gd/Dy-Er/Yb等,可以有效区分岩体成矿类型,其隐含的地球化学机制尚待进一步解释。地球化学数据挖掘结果可以作为找矿标志使用,为华南燕山期岩浆-热液矿床研究及找矿勘查提供了科学依据,也是大数据技术在矿床学方面应用研究的积极探索。

The discrimination between ore-forming and barren granites based on zircon REE compositions: Insights from big data mining

Yanshanian magmatism is well developed and has obvious metallogenic specificity in Qinzhou-Hangzhou Bays of South China. With the development of in-situ zircon analysis technology, a huge number of zircon composition data has been accumulated in recent years. On the basis of collecting data published by previous researchers, the authors determined the ore-forming potential of rock masses by using zircon REE compositions through big data thinking method, and explored effective geochemical indicators for ore prospecting. Python language was used to program arbitrary combination of elements. A total of 4095 binary diagrams and 121485 ternary diagrams were obtained, and diagrams that could effectively distinguish zircon parent rock metallogenic types were automatically screened out. The results show that different types of ore-forming rocks have different degrees of differentiation. Geochemical indices related to Ce and Eu can be well distinguished, which may result from the oxygen fugacity and water content of magma. Additionally, it is observed that some new element association diagrams (i.e., Dy/Lu-Er/Lu, Gd/Dy-Er/Yb) can distinguish ore-forming types of rock bodies effectively, but the underlying geochemical mechanism has not been fully understood. In brief, the results of geochemical data mining in this paper can be used as the prospecting indicators, which can provide scientific basis for the study and prospecting of Yanshanian hydrothermal deposits in South China, and can also be used to actively explore the application of big data technology in mineralogy.