投稿时间：2021-12-17
修订日期：2022-03-01
网络发布日期：2022-07-07
中文摘要:大数据技术在地学领域的应用越来越广泛，大数据思维为地学研究开辟了新的思路。从数据出发，以数据驱动模式去分析地质问题，在元素分布特征分析、矿床地化异常识别等方面较传统地学分析方法有着明显的优势。文章基于便携式X荧光分析仪（pXRF）测试的原位、无损、快捷、多元素分析等优点，对宣城矿集区内的荞麦山铜多金属矿床岩芯进行高密度的全数据原位采集，使用主成分分析（PCA）、多元逐步线性回归等方法进行量化分析。2种方法均表明，荞麦山铜多金属矿床中元素Mg、Ca、Mn、Co、As、Se、Ag、Hg、U和成矿元素Cu、Fe、S、W存在正相关关系；钻孔矿化和蚀变特征、铜硫矿石、钨矿石、石英砂岩均表现出不同的元素组合，特别是主成分综合得分（PCA）和第一主成分（PC1）元素对钻孔成矿区具有较好的指示效果；逐步多元线性回归分析进一步量化了元素富集规律，对成矿元素的拟合能够较好地与钻孔信息形成对应。因此，pXRF高密度的原位测量能够快速获取全面、准确的元素数据，分析结果能够直观反映荞麦山铜多金属矿床各元素的深度空间分布情况及量化相关关系，对深部地球化学特征的恢复和找矿提供帮助。
Abstract:Big data technology has been widely used in the geoscience and big data thinking has opened up a new way of thinking for geological research. Based on data, it is possible to obtain some results better than traditional geological analysis methods when analyzing geological problems with data-driven model. Based on the advantages of in-situ, nondestructive, fast and multi-element analysis of portable X-ray fluorescence analyzer (pXRF), this paper collects high-density in-situ geochemical data from the drill cores of the Qiaomaishan Cu polymetallic deposit in the Xuancheng ore district, Anhui Province, China, and makes quantitative analysis by using principal component analysis (PCA) and multiple stepwise linear regression. Both methods can show that there is a positive correlation between elements Mg, Ca, Mn, Co, As, Se, Ag, Hg, U and ore-forming elements Cu, Fe, S, W in the Qiaomaishan Cu deposit. The characteristics of mineralization and alteration, copper sulfur ore, tungsten ore and quartz sandstone all show different element combinations, especially the comprehensive score of principal component (PCA) and the first principal component (PC1) elements have a good indication or vectoring to the ore zones. Stepwise multiple linear regression analysis further quantifies the law of element enrichment, and the fitting of metallogenic elements can better correspond to the borehole information. Therefore, the high-density insitu measurement of pXRF can quickly obtain comprehensive and accurate element data, and the analysis results can directly reflect the depth and spatial distribution and quantitative correlation of various elements in the Qiaomaishan Cu deposit, which can provide help for the restoration of deep geochemical characteristics and ore prospecting.
keywords:geology portable XRF(pXRF) data-driven principal component analysis(PCA) stepwise regression Qiaomaishan Cu polymetallic deposit spatial distribution of elements
文章编号：
中图分类号：
文献标志码：
基金项目:本文得到国家自然科学基金资助项目（编号：41872247、41820104007、42072321）的资助
引用文本：
张明明,龙瑾潇,周国玉,焦峻嵚,方红东.2022.基于pXRF的荞麦山铜多金属矿床元素富集规律研究[J].矿床地质,41(3):643~658ZHANG MingMing,LONG JinXiao,ZHOU GuoYu,JIAO JunQin,FANG HongDong.2022.A pXRF study on element enrichment law of Qiaomaishan Cu polymetallic deposit, Anhui, China[J].Mineral Deposits41(3):643~658
图/表
