摘要
在当今信息时代,以地球科学数据为基础,数据挖掘技术及方法逐渐成为地球科学领域研究的重要方向。本文以粤西丰村–园垌层控型铅锌矿床发育区水系沉积物微量元素测量数据为研究对象,首先对原始数据进行清洗、变换和规约算法等数据处理,其次应用主成分分析法、聚类分析法及偏最小二乘降维(PLSR)算法挖掘地球化学元素组合异常信息。分析结果表明,基于等距对数比变换的PLSR建模,可以在不损失数据完整性的前提下,充分挖掘元素内部的相关性,并提取组合元素异常。PLSR法与主成分分析和聚类分析的结果对比分析显示,PLSR法提取出的元素组合与研究区铅锌矿床地球化学特征接近,可以充分挖掘地球化学数据的潜在信息,其分析结果与研究区地质条件耦合度高,可以为粤西地区层控型铅锌矿床下一步找矿提供可靠的数据支撑。
Nowadays,data mining technology and methods have gradually become an important research direction of geosciences.This paper carried out a case study on the trace element results of stream sediments related to stratabound lead-zinc mineralization in the western Guangdong province.After cleaning,transformation and reduction algorithm of the original data,including the log-ratio transformation,the partial least square reduction(PLSR)algorithm is applied to explore and dig out the geochemical element combination anomalies.The results showed that PLSR modeling based on log-ratio transformation can fully mine the correlation within elements and extract the anomalies of combined elements without losing data integrity.The element assemblages extracted by PLSR method are similar to the geochemical characteristics of the lead-zinc deposits in the study area.PLSR method can fully tap the potential information of geochemical data and the results are highly coupled with geological conditions,which provides strong data support for the stratabound lead-zinc prospecting and further exploration in the western Guangdong province.
作者
高乐
周永章
王琨
曾志强
卢宇彤
黄勇杰
GAO Le;ZHOU Yongzhang;WANG Kun;ZENG Zhiqiang;LU Yutong;HUANG Yongjie(Faculty of Intelligent Manufacturing,Wuyi University,Jiangmen 529000,Guangdong,China;School of Earth Sciences and Engineering,Sun Yat-sen University,Guangzhou 510275,Guangdong,China;Institute of Mineral Resources,Chinese Academy of Geological Sciences,Beijing 100037,China;National Supercomputer Center in Guangzhou,Guangzhou 510006,Guangdong,China)
出处
《大地构造与成矿学》
EI
CAS
CSCD
北大核心
2020年第2期258-266,共9页
Geotectonica et Metallogenia
基金
广东省自然科学基金项目(18zxxt52)
五邑大学青年科研基金项目(2019td10)
高层次人才项目(2018TP021)联合资助。