Germanium enrichment in sphalerite with acicular and euhedral textures: an example from the Zhulingou carbonate-hosted Zn(-Ge) deposit, South China

Due to the combined influences such as ore-forming temperature, fluid and metal sources, sphalerite tends to incorporate diverse contents of trace elements during the formation of different types of Lead-zinc (Pb-Zn) deposits. Therefore, trace elements in sphalerite have long been utilized to distinguish Pb-Zn deposit types. However, previous discriminant diagrams usually contain two or three dimensions, which are limited to revealing the complicated interrelations between trace elements of sphalerite and the types of Pb-Zn deposits. In this study, we aim to prove that the sphalerite trace elements can be used to classify the Pb-Zn deposit types and extract key factors from sphalerite trace elements that can discriminate Pb-Zn deposit types using machine learning algorithms. A dataset of nearly 3600 sphalerite spot analyses from 95 Pb-Zn deposits worldwide determined by LA-ICP-MS was compiled from peer-reviewed publications, containing 12 elements (Mn, Fe, Co, Cu, Ga, Ge, Ag, Cd, In, Sn, Sb, and Pb) from 5 types, including Sedimentary Exhalative (SEDEX), Mississippi Valley Type (MVT), Volcanic Massive Sulfide (VMS), skarn, and epithermal deposits. Random Forests (RF) is applied to the data processing and the results show that trace elements of sphalerite can successfully discriminate different types of Pb-Zn deposits except for VMS deposits, most of which are falsely distinguished as skarn and epithermal types. To further discriminate VMS deposits, future studies could focus on enlarging the capacity of VMS deposits in datasets and applying other geological factors along with sphalerite trace elements when constructing the classification model. RF’s feature importance and permutation feature importance were adopted to evaluate the element significance for classification. Besides, a visualized tool, t-distributed stochastic neighbor embedding (t-SNE), was used to verify the results of both classification and evaluation. The results presented here show that Mn, Co, and Ge display significant impacts on classification of Pb-Zn deposits and In, Ga, Sn, Cd, and Fe also have relatively important effects compared to the rest elements, confirming that Pb-Zn deposits discrimination is mainly controlled by multi-elements in sphalerite. Our study hence shows that machine learning algorithm can provide new insights into conventional geochemical analyses, inspiring future research on constructing classification models of mineral deposits using mineral geochemistry data. 

Discrimination of Pb-Zn deposit types using sphalerite geochemistry: New insights from machine learning algorithm

Critical scientific issues of super-enrichment of dispersed metals

Numerical modeling of formation of the Maoping Pb-Zn deposit within the Sichuan-Yunnan-Guizhou Metallogenic Province, Southwestern China: Implications for the spatial distribution of concealed Pb mineralization and its controlling factors

LA-ICP-MS analyses of trace elements in zoned sphalerite: A study from the Maoping carbonate-hosted Pb-Zn(-Ge) deposit, southwest China

扬子板块周缘铅锌多金属成矿带内分布着数以百计的沉积岩容矿型铅锌矿床，它们不仅是我国主要的铅锌矿产地，同时也是重要的稀散元素（Ge、Ga等）生产基地。本次研究采用LA-ICPMS技术分别测定了扬子板块西南缘的会泽铅锌矿床、金沙厂铅锌矿床、大梁子铅锌矿床，扬子板块北缘的马元铅锌矿床以及扬子板块东南缘的凤凰茶田锌（铅）汞矿床中闪锌矿的微量元素组成，以揭示闪锌矿中微量元素（稀散元素）的富集规律和赋存状态，并为矿床成因类型的厘定及稀散元素矿产资源综合利用提供更多依据。LA-ICPMS微量元素测定结果显示闪锌矿中不同微量元素（稀散元素）分布不均匀，但这些矿床中闪锌矿总体以富集稀散元素Ge、Ga、Cd，贫In、Se、Tl、Te为特征，其Fe、Mn含量要明显低于与岩浆热液有关的高温闪锌矿，指示了扬子板块周缘铅锌矿床可能形成于中-低温成矿流体，而与岩浆热液无直接的成因联系，此外这些矿床中闪锌矿富Ge贫In的特征与其他的密西西比河谷型铅锌矿床（MVT）一致。同时，本次研究综合分析了闪锌矿中不同微量元素（稀散元素）之间的相关关系，并与闪锌矿微量元素LA-ICPMS时间分辨率特征相结合，研究表明：这些铅锌矿床中稀散元素Ge可能主要通过3Zn2+↔Ge4++2（Cu+，Ag+）和2Zn2+↔Ge4++□（晶体空位）的替代方式进入闪锌矿，Ga在闪锌矿中富集机理主要为2Zn2+↔（Cu，Ag）++（Ga，As，Sb）3+。此外，为进一步揭示不同成因类型铅锌矿床中稀散元素的富集规律，本文还系统对比了全球范围内不同类型铅锌矿床闪锌矿的稀散元素（均为LA-ICPMS数据）组成特征，并初步探讨了造成不同成因闪锌矿中稀散元素（Ge、Ga和In）差异性富集的主要控制因素，研究表明：（1）Ge在中低温盆地卤水成矿系统（MVT和SEDEX矿床）和岩浆-火山热液成矿系统（浅成脉状铅锌矿床和VMS矿床）形成的闪锌矿中均可能富集成矿，但中低温浅成脉状矿床中Ge的富集程度要明显高于高温脉状矿床，指示了成矿温度是控制闪锌矿中Ge富集的一个重要因素。（2）铅锌矿床闪锌矿中In主要为岩浆来源，In倾向于在成矿温度较高的岩浆及火山热液成因铅锌矿床中富集成矿，而壳源的MVT和SEDEX型铅锌矿床中闪锌矿均贫In。可见除形成温度外，成矿物质来源是决定闪锌矿是否富In的关键因素。（3）除矽卡岩型铅锌矿床外，其他不同成因类型、不同形成温度的铅锌矿床中闪锌矿均可能富Ga。矽卡岩型铅锌矿床闪锌矿具有明显的贫Ga、Ge的特征，这可能是由于矽卡岩化过程中稀散元素Ga、Ge大量进入早期矽卡岩矿物，进而导致了成矿流体以及随后形成的闪锌矿中Ga、Ge的贫化。综上所述，闪锌矿中稀散元素富集与否和富集程度受成矿物质来源、成矿流体性质以及流体演化过程等多因素的综合控制。（4）扬子板块周缘铅锌矿床闪锌矿的微量元素（稀散元素）组成特征指示了它们形成于中低温成矿环境，稀散元素的富集规律与其它MVT型铅锌矿床类似。

扬子板块周缘mvt型铅锌矿床闪锌矿微量元素组成特征与指示意义:la-icpms研究

锗作为一种稀散元素，是现代信息产业最重要的金属之一，然而大多数锗资源是在综合评价煤矿床、铅锌矿床以及铁矿床的过程中发现的，缺乏专门的找锗方法。为了进一步加强锗矿找矿方法的研究，本文以川滇黔接壤处作为研究区，通过采用探索性分析方法（EDA）和基于分形理论的浓度-面积（C-A）方法以及证据权模型，分析研究区内低密度水系沉积物测量数据Zn、Ge元素的数字特征和分布规律。结果表明：（1）Ge元素分布由单个总体构成，基本符合正态分布，数值变化小（变异系数=0.13），基本围绕地壳丰度值（1.6×10-6）波动，且矿床的分布与地球化学异常之间没有明显的相关关系，因此采用以Ge找Ge的思路是行不通的，而这可能是由于Ge在表生氧化状态下的多亲和性和高度分散性造成的。（2）Zn元素分布明显不符合正态分布，且有多个峰值，数据离散程度大（变异系数=1.14），可能至少由两个以上总体构成，且在空间上矿床的分布与地球化学异常之间有良好的相关关系，大多数矿床位于相对高值区。另外鉴于在原生状态下，Ge元素往往以类质同象的形式赋存在闪锌矿中，因此在区域上采用以Zn找Ge的思路是可行的。（3）采用累计概率法、直方图法、箱式图法、原生晕法以及基于多重分形的C-A方法，确定的异常下限分别为146.5×10-6、392.7×10-6、153.9×10-6、87.5×10-6和124×10-6。通过采用证据权方法所提供的空间相关性统计量（t）进行检验，发现异常下限应当落在120×10-6~130×10-6的区间内。因此基于分形理论的C-A方法是最合理的，这可能是其不仅考虑了频数特征，还考虑了空间几何特征的原因。（4）在圈定地球化学块体的基础上，结合热液矿床受构造控制，且往往具有'丛聚效应'以及'鹤立鸡群'的特点，圈定黑区-赤普、大湾子-大桥边、茂租-乐红、毛坪、天宝山-小石房、猫猫厂-白蜡厂、大梁子、会泽、青山-杉树林、猴子厂-顶头山以及富乐作为寻找锗矿的潜力区，建议进一步加强这11个矿田级远景区锗资源的找矿工作。

Role of regional geochemical survey for Ge mineral prediction in Chuan-Dian-Qian Pb-Zn (Ge) metallogenic region

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扬子板块周缘mvt型铅锌矿床闪锌矿微量元素组成特征与指示意义:la-icpms研究

Role of regional geochemical survey for Ge mineral prediction in Chuan-Dian-Qian Pb-Zn (Ge) metallogenic region