西北太平洋采薇海山富钴结壳矿物学和地球化学特征

富钴结壳是一种富含Mn、Co、Ni和稀土元素（镧系元素加钇,简称REY）等元素的海底矿产资源。本文研究的富钴结壳样品是“科学”号在2018年HOBAB5航次于西北太平洋采薇海山的山顶边缘上通过电视抓斗获得的。利用扫描电镜、X射线衍射仪（XRD）、电感耦合等离子体发射光谱仪（ICP-OES）和电感耦合等离子体质谱仪（ICP-MS）分析了富钴结壳的显微构造、矿物学特征和地球化学特征,并探讨了其成因类型和形成机制。富钴结壳的结构从内到外可分为土黄色的疏松层（C8-5）、黑色铁锰致密层（C8-2、C8-3和C8-4）和发育葡萄状球体的粗糙表面（C8-1）。土黄色疏松层孔隙度较高,主要组成矿物为水羟锰矿、石英、钙长石、钠长石、钙十字沸石和钡镁锰矿,Mn的含量较低,Al的含量较高。黑色的铁锰致密层孔隙度较低,呈柱状构造,主要组成矿物为水羟锰矿、石英、钙长石和钠长石,Al含量有所下降,Mn含量升高, 说明陆源物质的供应逐渐变少。在富钴结壳的生长后期,其主要显微构造由柱状构造向斑杂构造转变,二者的过渡区域为铁锰氧化物与富Si碎屑物质组成的层状构造。富钴结壳各层位的Mn/Fe比值为1.16～1.85,且各层位Ce呈正异常,Y呈负异常,以上特征表明富钴结壳为水成成因型,其金属元素来源于氧化性海水,未受到热液活动的影响。依据富钴结壳的年代学数据,可知从渐新世末期到上新世中期,富钴结壳的生长过程一直受控于太平洋深层水。Co/(Fe+Mn)和Co/(Ni+Cu)的不断升高表明富钴结壳一直在氧化性较高的海水环境中生长。相较于其他大洋和海区,采薇海山富钴结壳具有高含量的Co、Ni和REY,具有极高的经济价值和开采价值。     

九州-帕劳海脊南段铁锰结壳物质组成特征及成因机制

铁锰结壳（又称富钴结壳、多金属结壳）富含Co、Cu、Mn、Ni、Ti、V、REE、Y和Zn等人类日常生活和高新技术产业亟需的关键金属,是一种重要的战略性矿产资源。本文对九州-帕劳海脊南段水深1900～2600 m处获得的9个站位铁锰结壳样品进行了矿物学和地球化学研究,分析了铁锰结壳的矿物组成、主微量元素和稀土元素含量,并进一步探讨了铁锰结壳的成因类型。结果表明,研究区铁锰结壳的矿物组成以水羟锰矿为主,同时含有大量非晶态铁氧/氢氧化物;Mn、Fe、Co、Ni、Cu平均含量分别为16.15%、15.38%、0.32%、0.33%、0.10%;CaO/P2O5均值为5.93,表明九州-帕劳海脊南段铁锰结壳样品均未发生磷酸盐化作用;铁锰结壳明显富集稀土元素（含Y,REYs）,平均含量为1194 μg/g,轻稀土显著富集,稀土元素经后太古代澳大利亚页岩（PAAS）标准化后配分模式整体相对平坦,呈现Ce正异常而Eu无异常,与海水呈现镜像关系,说明铁锰结壳稀土元素主要来源于海水。铁锰结壳的矿物组成和元素判别图均表明九州-帕劳海脊南段铁锰结壳属于水成型,未受明显的成岩作用影响。     

西南印度洋中脊富钴结壳的矿物学和地球化学

基于采自西南印度洋超慢速扩张中脊的8块富钴结壳样品,利用XRD、XRF等方法对其进行了矿物学及地球化学组成分析,阐明了该区富钴结壳的矿物物相和元素组成特点,结合主成分分析探讨了该区结壳的成因类型和成矿物质来源。结果表明:该区结壳矿物组成以锰、铁氧化物相为主,其中锰相矿物以水羟锰矿占优势,含少量钡镁锰矿,铁相矿物出现了针铁矿及非晶质-隐晶质相;该区结壳Mn、Fe、Co、Cu、Ni的平均值分别为18.96%、19.71%、0.53%、0.099%、0.47%,它们介于中、西太平洋结壳和大西洋、印度洋结壳的相应的元素含量之间;矿物组成、元素组合、元素比值等皆表明该区结壳属于水成成因,受成岩作用影响微弱,但是未见热液活动的明显影响。     

大兴安岭中南段昌图锡力银铅锌锰矿床矿物学特征及其对矿床成因的约束

昌图锡力银铅锌锰矿是近年在大兴安岭成矿带中南段锡林浩特-霍林郭勒多金属成矿亚带上新发现的1处银铅锌多金属矿床。矿床中的主要矿物分析结果表明:矿床中的银矿物有银黝铜矿、硫锑铜银矿、深红银矿等, 主要以独立的银矿物分布于金属硫化物中;银黝铜矿的Ag含量为8.25%~13.11%, 平均为10.54%, Cu含量为27.65%~31.43%, 平均为29.64%, 面扫描图像显示Ag以类质同像的形式赋存于银黝铜矿中。硫锑铜银矿的Ag含量较高, 平均为68.99%, 主要分布于方铅矿的边缘及其裂隙中。闪锌矿的Fe含量为0.30%~0.38%, 平均为0.33%, 属于贫铁闪锌矿, Cd与Zn具有很好的相关性, 可作为寻找闪锌矿的地球化学标志。菱锰矿(MnCO3)为晚期低温石英-碳酸盐阶段的产物, 是主要的碳酸盐矿物;锰的氧化物主要为软锰矿及硬锰矿, 软锰矿呈环带结构、胶状构造。除Mn外, 同时也富集高品位Pb。综合分析昌图锡力矿床的矿物学特征及矿床地质特征, 认为该矿为浅成低温热液型银铅锌锰多金属矿床。     

河南石板沟金矿矿床地质特征及成因探讨

在总结石板沟金矿床矿化地质特征的基础上,通过对矿石氢氧同位素测试和矿石微量元素相关分析,认为成矿热液主要来源于变质水,矿床成矿环境应属低温环境,矿床成因类型属受构造剪切带控制的低温变质热液蚀变型金矿床.     

鄂尔多斯盆地北缘延安组2号煤层稀土元素异常原因及其地质意义

鄂尔多斯盆地北缘延安组2号煤层稀土元素的异常原因一直存在着争议,以2号煤发育较完整的榆林大海则煤矿为例,运用电感耦合等离子质谱（ICP-MS）、扫描电子显微镜（SEM）等方法,分析煤及夹矸中稀土元素（REE）含量及其矿物学特征,并揭示稀土元素异常原因。结果显示：煤中总稀土元素（∑REE）含量为3.71~46.21 μg/g,轻稀土元素（LREE,La-Eu）比重稀土元素（HREE,Gd-Lu）更富集;稀土元素标准化配分模式图显示少数样品为Eu正异常;稀土元素含量较高的样品和拥有Eu正异常的样品主要分布在与夹矸距离较近的煤层中,表明煤中稀土元素分布受到了夹矸的影响;在夹矸中发现很多晶型较好的锆石、磷灰石、透长石及锐钛矿,这些矿物是在聚煤过程中接受火山物质的直接证据。综合认为,鄂尔多斯盆地延安组2号煤沉积期,盆地周缘存在一次火山活动,火山灰降落覆盖在泥炭沼泽之上,影响了聚煤作用,致使煤中稀土元素分布异常。研究结果解释了鄂尔多斯盆地北缘的煤中稀土元素异常原因,为研究区煤的加工利用方式提供参考。     

义县金矿成困矿物学研究

义县金矿是我国新发现的一个金矿床,矿床规模较大,品位高,矿石成分简单,易采易选。矿床受地层、断裂、岩脉活动等因素影响,并有多源特证。     

Lateglacial and Holocene climate and soil erosion in southeastern France: A case study from Etang du Pourra,Provence

A synthesis of pollen evidence from Etang du Pourra, a small pond in Rhodanian Provence, France, complemented by new sedimentological, mineralogical and palynological data allow a reassessment of the Lateglacial-Holocene vegetation and climatic history of the area. The Etang du Pourra succession is shown to consist of two parts; the lower part dates from the Lateglacial and the upper reveals the Holocene pollen-stratigraphic succession typical of the region. In the Lateglacial, a humid and cooler phase is recorded before the Younger Dryas conditions progressively set in. In the Holocene succession, the consequences of human modifications on the environment are clearly visible in changes in mineralogy and pollen stratigraphy.     

因硫碲铋矿在中国的首次发现

在陕西黄龙金矿发现了多粒碲铋矿物,根据电子探针分析数据,其中有几粒的成分与因硫碲铋矿相近,颗粒大小为十几到几十微米,与黄铁矿、闪锌矿、赤铁矿等共生。Au与Bi在矿石和围岩中的含量呈共消长关系,Te与Bi可能对Au的迁移富集起了重要作用。     

Classification of mineral deposits into types using mineralogy with a probabilistic neural network

In order to determine whether it is desirable to quantify mineral-deposit models further, a test of the ability of a probabilistic neural network to classify deposits into types based on mineralogy was conducted. Presence or absence of ore and alteration mineralogy in well-typed deposits were used to train the network. To reduce the number of minerals considered, the analyzed data were restricted to minerals present in at least 20% of at least one deposit type. An advantage of this restriction is that single or rare occurrences of minerals did not dominate the results. Probabilistic neural networks can provide mathematically sound confidence measures based on Bayes theorem and are relatively insensitive to outliers. Founded on Parzen density estimation, they require no assumptions about distributions of random variables used for classification, even handling multimodal distributions. They train quickly and work as well as, or better than, multiple-layer feedforward networks. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class and each variable. The training set was reduced to the presence or absence of 58 reported minerals in eight deposit types. The training set included: 49 Cyprus massive sulfide deposits; 200 kuroko massive sulfide deposits; 59 Comstock epithermal vein gold districts; 17 quartzalunite epithermal gold deposits; 25 Creede epithermal gold deposits; 28 sedimentary-exhalative zinc-lead deposits; 28 Sado epithermal vein gold deposits; and 100 porphyry copper deposits. The most common training problem was the error of classifying about 27% of Cyprus-type deposits in the training set as kuroko. In independent tests with deposits not used in the training set, 88% of 224 kuroko massive sulfide deposits were classed correctly, 92% of 25 porphyry copper deposits, 78% of 9 Comstock epithermal gold-silver districts, and 83% of six quartzalunite epithermal gold deposits were classed correctly. Across all deposit types, 88% of deposits in the validation dataset were correctly classed. Misclassifications were most common if a deposit was characterized by only a few minerals, e.g., pyrite, chalcopyrite,and sphalerite. The success rate jumped to 98% correctly classed deposits when just two rock types were added. Such a high success rate of the probabilistic neural network suggests that not only should this preliminary test be expanded to include other deposit types, but that other deposit features should be added