Quartz Ti/Ge-P discrimination diagram: A machine learning based approach for deposit classification

Abstract

石英的微量元素记录了石英生长的物理化学条件。通过微量元素对石英原岩进行分类的研究历史已久，经典工作是在以微量元素为坐标轴的图解上绘制各类型石英的分布范围，以区分石英类型。经典图解包括Rusk （2012）提出用于区分三种矿床类型石英的Al-Ti二元图解，和Schrön et al.（1988）提出的用于判别不同岩浆岩类型石英的Ti-Al-Ge三元图解。越来越多的研究表明，上述图解不能满足对更多石英类型进行分类的需求，同时也出现与部分已知产状类型的石英微量元素判别相矛盾的情况。随着石英原位微区测试方法的成熟，高精度石英微量元素数据逐渐丰富为系统开展机器学习提供了大数据基础，为石英微量元素研究提供了新的角度和可能性。本研究运用机器学习分类方法对石英微量元素进行精确数学分析，提出Ti/Ge-P图解为石英成因研究提出新的地球化学指标。本文同时测试了六种经典机器学习分类算法，提高Ti/Ge-P图解在石英成因分类研究上的精度。此Ti/Ge-P图解适用于多种矿床研究，包括但不局限于斑岩型矿床、矽卡岩型矿床、浅成低温热液型矿床、卡林型矿床以及造山型矿床中的石英。这项工作是大数据技术与机器学习技术在地球化学研究中的积极探索。;Trace elements in quartz record quartz physical and chemical growth processes, which so far has been an important tool to investigate geological settings associated with quartz-forming environments. The classic method is to draw the distribution range of various types of quartz on a diagram with trace elements as the coordinate axis. Classical diagrams include the Al-Ti binary diagram of Rusk (2012) distinguishing three-type ore deposits, as well as the Ti-Al-Ge ternary diagram of Schrön et al. (1988) distinguishing quartz from various magmatic rocks. However, these studies cannot fit well with some known types of quartz trace element data, and they in addition cannot support scholars to classify more quartz types. With the maturity of micro-area testing technology, quartz trace element data is gradually enriched. To address it, here we use programming methods to exhaustively combine several elements and their ratios in the published quartz trace element data. Then we design an algorithm to select the best axis, which turns out to be Ti/Ge-Al diagram, for distinguishing quartz from different deposit types. On the basis of the Ti/Ge and P axes, six machine learning classification algorithms were trained and compared, and the best classification algorithm was selected to predict the decision boundary. The Ti/Ge-P diagram is therefore proposed to be used to identify quartz in porphyry deposits, skarn deposits, epithermal deposits, Carlin deposits, and orogenic deposits. Our research is a great active exploration of data technology and machine learning technology in hydrothermal geochemistry of mineral deposits.