胶东半岛(中国东部)滕家金矿床白云母短波红外光谱数据的机器学习:金矿床的勘探指标

IF 3.2 2区 地球科学 Q1 GEOLOGY
Jiayao Hao , Liuan Duan , Yu Zhang , Hongtao Zhao , Yongjun Shao , Yuncheng Guo , Xu Wang , Shuling Song
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引用次数: 0

摘要

原生金矿床是全球黄金资源的主要来源,在深部具有巨大的成矿潜力,因此需要采取新的战略来寻找深部隐伏矿体。滕家金矿床是一个新发现的隐伏蚀变岩型块状金矿床(50 吨 @ 3.89 克/吨),位于中国东部著名的富金胶东半岛昭平成矿带内。该矿床的特点是普遍存在与金矿化相关的植蚀作用,是矿床地球化学勘探的理想目标。滕家的矿化和蚀变分为三个不同的阶段,由矿物组合和纹理关系划分:K长石-石英(Ⅰ)阶段、石英-钠长石-原生金-硫化物(Ⅱ)阶段、石英-方解石-方铅矿-闪锌矿(Ⅲ)阶段。通过对短波红外光谱(SWIR)进行系统分析,并结合岩相观察,发现在滕家Ⅱ期中存在大量白色云母(蒙脱石、褐铁矿、伊利石、霰石和辉绿岩)。Al-OH吸收特征波长(Pos2200)和伊利石结晶度(IC)值在矿石沉积附近出现了明显的波长变长(2204 nm)和值变高(1.4)的现象,这可能是成矿流体与壁岩之间强烈的水-岩相互作用的结果。正交偏最小二乘法(OPLS-DA)的判别分析表明,与水、-OH 和 Al-OH 相对应的吸收波长可有效区分矿石和壁岩样品。此外,使用随机森林算法(RF)进行的分析表明,滕家白云母的光谱数据可以可靠地对矿体进行分类,准确率达到 83.2%。因此,研究结果表明,白云母独特的西南红外光谱特征为探测隐蔽的滕家金矿化提供了宝贵的工具。本研究提出了一种将机器学习技术与西南红外光谱分析相结合的新方法,用于隐伏金矿床的识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Machine learning on white mica short-wave infrared (SWIR) spectral data in the Tengjia Au deposit, Jiaodong peninsula (Eastern China): A prospecting indicator for lode gold deposits

Machine learning on white mica short-wave infrared (SWIR) spectral data in the Tengjia Au deposit, Jiaodong peninsula (Eastern China): A prospecting indicator for lode gold deposits

Lode gold deposits are the primary source of global gold resources and possess significant mineralization potential at depth, necessitating new strategies to locate deep concealed orebodies. The Tengjia Au deposit, a newly-discovered concealed altered rock-type lode gold deposit (50 t @ 3.89 g/t), is located within the Zhaoping metallogenic belt of the illustrious gold-rich Jiaodong peninsula in Eastern China. It is distinguished by pervasive phyllic alteration associated with gold mineralization, making it an ideal target for mineral geochemical exploration in lode gold deposits. The mineralization and alteration at Tengjia unfold across three distinct stages, delineated by mineral assemblages and textural relationships: K-feldspar-quartz (I), quartz-sericite-native gold-sulfide (Ⅱ), quartz-calcite-galena-sphalerite (Ⅲ) stages.

Systematic analysis of short wavelength infrared (SWIR) spectra, coupled with petrographic observation, has unveiled an abundance of white micas (montmorillonite, muscovite, illite, paragonite, and phengite) within Stage Ⅱ at Tengjia. The Al-OH absorption feature wavelengths (Pos2200), as well as illite crystallinity (IC) values, exhibit a discernible shift towards longer wavelengths (>2204 nm) and higher values (>1.4) in the vicinity of ore deposition, which likely resulted from intense water–rock interaction between ore-forming fluid and wall rocks. Discriminant analysis of the orthogonal partial least squares method (OPLS-DA) shows that the absorption wavelengths corresponding to Water, –OH, and Al-OH effectively differentiate between ore and wall-rock samples. Additionally, analysis using the random forest algorithm (RF) demonstrates that spectral data from Tengjia white micas can reliably classify orebodies, achieving an accuracy of 83.2 %. Hence, the findings suggest that the unique SWIR spectral features of white micas offer a valuable tool for detecting the concealed Tengjia gold mineralization. This study proposes a novel approach that integrates machine learning technology with SWIR analysis for the identification of concealed lode gold deposits.

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来源期刊
Ore Geology Reviews
Ore Geology Reviews 地学-地质学
CiteScore
6.50
自引率
27.30%
发文量
546
审稿时长
22.9 weeks
期刊介绍: Ore Geology Reviews aims to familiarize all earth scientists with recent advances in a number of interconnected disciplines related to the study of, and search for, ore deposits. The reviews range from brief to longer contributions, but the journal preferentially publishes manuscripts that fill the niche between the commonly shorter journal articles and the comprehensive book coverages, and thus has a special appeal to many authors and readers.
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