共查询到17条相似文献，搜索用时 140 毫秒
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《西部资源》2016,(3)
通过对吉源地区1∶5万土壤地球化学测量数据处理发现,土壤中Ag、As、Au、Bi、Cd、Mo、Nb、Ni、Pb、Sb的变异系数均1.0,La、U、Zn、Sn、Y、W、Co、Cu的变异系数均1.0,聚类分析和因子分析结果显示,研究区土壤所分析的18种元素分为4个组合:Zn、Ni、Co、Cd、Cu组合为一套与中基性岩或Cu、Zn矿化有关的元素组合;W、Mo、Pb、Ag、Bi组合为一套与Pb、Ag、W、Mo等矿化有关的元素组合;Nb、Y、Sn、U、La组合为一套与Nb、Y、Sn等矿化有关的元素组合;As、Sb、Au组合为一套与Au等元素矿化有关的元素组合。分布在吉源地区的综合异常显示Nb、Y、Sn、U、La组合与W、Mo、Pb、Ag、Bi组合及Cu、Zn组合元素浓集中心分布表现出明显的分带性,且Nb、Y、Mo等元素三级浓度分带明显,且高值点较多,异常规模、异常强度、异常面积均较大,同时异常所处地质背景成矿条件也较为优越,虽然区内未发现Nb、Y等稀有、稀土元素矿化点,但地质背景和土壤化探异常特征都显示出吉源地区具有很大的找寻Nb、Y稀有、稀土元素矿产的潜力,同时找寻Pb、Zn等多金属矿产也较为有利。 相似文献
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东昆仑东段下三叠统洪水川群,地质特征复杂,其自下而上可分为砂砾岩组、火山碎屑岩组和碳酸盐岩组三个岩组。洪水川群火山岩属于钙碱性系列的中酸性火山岩。洪水川群地层是良好的含矿层位,有较高的Au丰度,B、Ni、V、Ti、Co、Cr、As、Cu、W、Pb、Zn、Ag等元素亦有高背景值,洪水川群具备有利的成矿背景,可形成与钙碱性系列的中酸性火山岩成矿密切相关的Au、Ag、Cu、Pb、Zn、Co等矿床。 相似文献
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文章以构造叠加晕找矿法理论和方法为指导,系统研究了该矿床元素组合、含矿构造中原生晕空间分布分带等地球化学特征规律,研究表明,矿床成矿热液伴生Ag、Cu、Pb、Zn、As、Hg、B、F、Bi、Mo、Mn、Co、Ni、W等多元素活动特点,利用构造叠加晕盲矿预测标志,对矿床深部存在进行了预测,提出了盲矿预测靶位,为进一步探矿增储提供了依据。 相似文献
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对比Na BH4碱性溶液Cu、Ag、Ni、Pt和Au电极的循环伏安特性发现,BH-4在Au电极上活性最强,Pt有一定的活性,而Cu、Ag及Ni几乎没有活性。用三电极体系研究了BH-4在Au电极上的氧化机理,分析了碱性体系中不同浓度Na BH4溶液的线性循环伏安特性,讨论了扫描速度、OH-和B(OH)-4浓度等对BH-4氧化峰电位及峰电流的影响。结果表明,以Au电极为工作电极的峰值电流法是BH-4离子定性和定量检测的一种简单、快速、准确的方法。同时,溶液中较大浓度范围内变化的OH-和B(OH)-4对Na BH4的分析无明显影响。 相似文献
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对沅江入湖沉积物进行钻探取样,利用等离子质谱仪（ICP-MS）对沉积物重金属进行分析。结果表明：重金属Ba、Sc、V、Th、U、Cu、Co、Ni、Cr等在沉积物中含量变化相对稳定,分布相对均匀;而Mn、Zn、Pb、Mo、Cd、Tl、Bi等重金属的含量变化大,分布不均匀。重金属含量柱状剖面变化特征及富集系数（EF值）的计算结果显示：沉积物中Cd达显著富集,而Sc、V、Mn、Pb、Bi等为中等富集程度。沉积物中存在3个重金属富集层,即中下部Pb、Tl、Bi富集层;中上部Pb、Cr、Ni、Cu富集层;浅表部V、Cr、Mn、Ni、Cu、Zn、Pb、Cd、Tl、Bi等多种重金属富集层。地累积指数（Igeo）和综合富集指数（EI）评价结果显示：沅江入湖沉积物重金属污染程度自河床深部向浅部,污染程度趋于增强,污染元素组合趋于由Pb-Bi的单一元素组合向由V-Cr-Mn-Ni-Cu-Zn-Pb-Cd-Bi组成的复合元素组合变化。且自上游向下游,沉积物重金属污染程度趋于降低。这种重金属污染空间变化特征与区域人为活动有关,值得进一步研究。 相似文献
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对沅江入湖沉积物进行钻探取样,利用等离子质谱仪(ICP-MS)对沉积物重金属进行分析。结果表明:重金属Ba、Sc、V、Th、U、Cu、Co、Ni、Cr等在沉积物中含量变化相对稳定,分布相对均匀;而Mn、Zn、Pb、Mo、Cd、Tl、Bi等重金属的含量变化大,分布不均匀。重金属含量柱状剖面变化特征及富集系数(EF值)的计算结果显示:沉积物中Cd达显著富集,而Sc、V、Mn、Pb、Bi等为中等富集程度。沉积物中存在3个重金属富集层,即中下部Pb、Tl、Bi富集层;中上部Pb、Cr、Ni、Cu富集层;浅表部V、Cr、Mn、Ni、Cu、Zn、Pb、Cd、Tl、Bi等多种重金属富集层。地累积指数(Igeo)和综合富集指数(EI)评价结果显示:沅江入湖沉积物重金属污染程度自河床深部向浅部,污染程度趋于增强,污染元素组合趋于由Pb-Bi的单一元素组合向由V-Cr-Mn-Ni-Cu-ZnPb-Cd-Bi组成的复合元素组合变化。且自上游向下游,沉积物重金属污染程度趋于降低。这种重金属污染空间变化特征与区域人为活动有关,值得进一步研究。 相似文献
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本文对马来半岛中央金成矿带北段的New Discovery金矿地球化学特征进行了分析。主要得出:主量元素特征表明该矿床火山碎屑岩为钙碱性铝过饱和系列岩石。微量元素表明可将13种元素分成4个组合:Cr、Co、Cu、Sc、W、Ba;Pb、Ag、Zn;Sb、Ni;Bi、Mo。第一组合为成矿成晕组合,第二组合为多金属矿化组合,第三组合为硫化物蚀变组合;元素组合也对应于不同的矿化阶段。稀土元素分析结果表明,该岩石属轻稀土富集型,铕弱负异常。稀土配分曲线为平缓的右倾。New Discovery金矿床的原岩为中性火山岩,且形成于岛弧——活动大陆边缘区域。 相似文献
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用仪器中子活化测定了从南极回收的GRV98003铁陨石和乌珠穆沁铁陨石Cr,Co,Ni,Cu,Ga,Ge,As,Sb,W,Re,Ir,Pt及Au的浓度。根据化学组成,重新对这两个未分群的铁陨石进行了分类工作,其中将GRV98003铁陨石划分为IAB群,乌珠穆沁铁陨石划分为与IAB群相关的单独铁陨石。GRV98003铁陨石的元素丰度模式、元素对相关性(如NiAu,CoAu,AsAu,WAu,CuAu,SbAu)等与NWA468铁陨石(IAB)相似,但前者具有明显的难熔亲铁元素(Re,Ir)和中等挥发性元素(Ga,Ge)的贫化。此外,本文还对南丹铁陨石(IIICD)和邕宁铁陨石(IA)的化学群重新进行了讨论,提出将前者划分为IAB复合群的主群,而将后者划分为与IAB复合群相关的单独铁陨石的新观点。 相似文献
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A test of the ability of a probabilistic neural network to classify deposits into types on the basis of deposit tonnage and average Cu, Mo, Ag, Au, Zn, and Pb grades is conducted. The purpose is to examine whether this type of system might serve as a basis for integrating geoscience information available in large mineral databases to classify sites by deposit type. Benefits of proper classification of many sites in large regions are relatively rapid identification of terranes permissive for deposit types and recognition of specific sites perhaps worthy of exploring further.Total tonnages and average grades of 1,137 well-explored deposits identified in published grade and tonnage models representing 13 deposit types were used to train and test the network. Tonnages were transformed by logarithms and grades by square roots to reduce effects of skewness. All values were scaled by subtracting the variable's mean and dividing by its standard deviation. Half of the deposits were selected randomly to be used in training the probabilistic neural network and the other half were used for independent testing. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class (type) and each variable (grade or tonnage).Deposit types were selected to challenge the neural network. For many types, tonnages or average grades are significantly different from other types, but individual deposits may plot in the grade and tonnage space of more than one type. Porphyry Cu, porphyry Cu-Au, and porphyry Cu-Mo types have similar tonnages and relatively small differences in grades. Redbed Cu deposits typically have tonnages that could be confused with porphyry Cu deposits, also contain Cu and, in some situations, Ag. Cyprus and kuroko massive sulfide types have about the same tonnages. Cu, Zn, Ag, and Au grades. Polymetallic vein, sedimentary exhalative Zn-Pb, and Zn-Pb skarn types contain many of the same metals. Sediment-hosted Au, Comstock Au-Ag, and low-sulfide Au-quartz vein types are principally Au deposits with differing amounts of Ag.Given the intent to test the neural network under the most difficult conditions, an overall 75% agreement between the experts and the neural network is considered excellent. Among the largestclassification errors are skarn Zn-Pb and Cyprus massive sulfide deposits classed by the neuralnetwork as kuroko massive sulfides—24 and 63% error respectively. Other large errors are the classification of 92% of porphyry Cu-Mo as porphyry Cu deposits. Most of the larger classification errors involve 25 or fewer training deposits, suggesting that some errors might be the result of small sample size. About 91% of the gold deposit types were classed properly and 98% of porphyry Cu deposits were classes as some type of porphyry Cu deposit. An experienced economic geologist would not make many of the classification errors that were made by the neural network because the geologic settings of deposits would be used to reduce errors. In a separate test, the probabilistic neural network correctly classed 93% of 336 deposits in eight deposit types when trained with presence or absence of 58 minerals and six generalized rock types. The overall success rate of the probabilistic neural network when trained on tonnage and average grades would probably be more than 90% with additional information on the presence of a few rock types. 相似文献
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The enrichment ratio (ER), defined as the ratio of grade of a metal element in a deposit to the crustal abundance of the metal, is proposed for assessing mineral resources. According to the definition, the enrichment ratio of a polymetallic deposit is given as a sum of enrichment ratios of all metals. The relation between ER and the cumulative tonnage integrated from the high ER side of about 4750 deposits in the world is approximated by the combination of three exponential functions crossing at ER values of 16 · 103 and 600. High ER deposits are expected for the commodities Ag, Pb, and Au+Ag, and for epithermal, mesothermal, unconformity-related and vein types. In contrast, low ER deposits are typical for the commodities Cu, Mn, Mo, Ni, and U, and for chemically precipitated, Cyprus, laterite, orthomagmatic, pegmatite, placer, porphyry, and sandstone deposits. The critical ER value of the low ER class (the differential metal amount decreases with decreasing ER in the regions lower than the value) is 250 in all deposits, 610 in W+Mo, 2800 in Pb+Zn and 360 in Au+Ag, 530 in massive sulfides, 160 in the orthomagmatic type, 170 in placers, 220 in the porphyry type, 1900 in the replacement type, 580 in the stratabound type, 3400 in the unconformity-related type, and 1700 in vein type deposits. The frequency proportion determined by a keyword and a commodity provides valuable suggestions for mineral exploration: for example, the exploration target for chromite is a deposit characterized as orthomagmatic, whereas the expected commodity of a newly developed orthomagmatic deposit is chromite. 相似文献
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Ore value-tonnage diagrams for resource assessment 总被引：4，自引：0，他引：4
An ore value-tonnage diagram has been proposed for assessing mineral resources. Diagrams of W+Mo, and Pb+Zn deposits show
a good linearity between ore value and logarithms of cumulative ore tonnage. Diagrams of the massive sulfide, orthomagmatic,
placer, porphyry, replacement, and stratabound types are also linear. It is assumed, therefore, that deposits of each of these
commodities and these types belong to a single population. In contrast, the ore value-tonnage relations of all the deposits
analyzed here is approximated by the combination of two exponential functions. The same feature is seen for deposits of the
Cu+W+Mo, Cu+Pb+Zn, and Au+Ag commodities, and of the vein and unconformity-related types. This suggests that deposits belonging
to each of such categories are divided into the high and low value groups. We can expect, accordingly, to find high value
deposits of such categories. 相似文献
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Yu Yu Jinming Song Liqin Duan Xuegang Li Huamao Yuan Ning Li 《Journal of Paleolimnology》2014,52(4):277-292
Total and chemical fractions of nine potential harmful elements (V, Cr, Co, Ni, Cu, Zn, Cd, Pb and Bi) and five lithogenic elements (Li, Sc, Rb, Cs and Th) in two 210Pb-dated cores from the East China Sea were analyzed to investigate their applications in paleoenvironment studies, and to reconstruct the histories of environmental changes. The residual fraction was the largest pool of trace elements in sediments. Potential harmful elements exhibited distributions that were similar to residual fractions and the lithogenic elements, indicating their terrestrial origin mainly derived from the Changjiang River and the old Huanghe delta. In the coastal core, the distributions of total and residual trace elements recorded the dry/wet variations of the Changjiang runoff since the 1850s. Total potential harmful elements and lithogenic elements in the offshore core reflected fluctuations in the strength of the Jiangsu coastal current and the East Asia Winter Monsoon over the last century. The response mechanisms of sedimentary trace elements to the runoff and monsoon variations involved direct terrestrial input of elements and the impacts of TOC and sediment grain size on trace elements in sediments. Enrichment factors (EFs), chemical fractions and principal component analysis (PCA) were used to evaluate the anthropogenic disturbance on potential harmful elements. PCA identified the lithogenic fraction of trace elements in both cores and the anthropogenic/authigenic input in the coastal core. Increases of the EFs and labile fractions of Zn, Pb and Bi in upper sediments of the coastal core indicated increased anthropogenic input of Zn and Pb since the 1980s, and increased Bi input since the 1940s. Increases in oxidizable Co, Ni, Cu, Zn, Pb and Bi above 16 cm were related to eutrophication and elevated marine organic matter in inshore East China Sea after the 1990s. Sediment records in offshore did not show any evidence of anthropogenic influence on the potential harmful elements. This study revealed that trace elements in sediments were good proxies for natural and human-induced environmental changes in waters. 相似文献
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Zonation in primary halos and geochemical prospecting pattern for the Guilaizhuang gold deposit, eastern China 总被引：3，自引：0，他引：3
The Guilaizhuang gold deposit, with an average grade of 8.10 g/t Au and reserves of over 30 mt, is a subvolcanic epithermal
deposit. The deposit is hosted in Paleozoic carbonate rocks in the western Shandong metallogenic terrane of the littorine
Pacific metallogenic domain, eastern China, and is associated spatially with an early Mesozoic subvolcanic alkalic intrusive
complex (188–190 Ma). The orebody was discovered at the end of the 1980s based on anomalies of Au in stream sediment samples
at a map scale of 1∶200,000. The ore is rich in Au, Ag, Te, V, F, As, Sb, Tl, W, and Mo but poor in Cu, Pb, and Zn. The ore
is similar in its trace elements to Carlin-type Au deposits. The transverse element association zonation of the deposit is
as follows: (on the hanging wall) F⟸ W−Mo−As−Tl⟸Se−Sb−Bi⟸Au−Ag−Te (orebody) ⟹ Se−Sb−Bi (on the foot wall). The axial zonation
is as follows: Au⟹Ag⟹Sb⟹V⟹Zn ⟹W⟹F⟹Mo⟹Tl⟹As. Indexes such as (Au+Sb)
d
/ (As+Tl)
d
and (Au+ Ag)
d
/(As+Tl)
d
decrease with depth but dramatically increase at the level where the orebody pinches out, which indicates another orebody
might exist at depth. Multivariate statistical analysis suggests that the ore (halo)-forming process can be divided into two
stages: alteration and mineralization. The former includes: potash feldsparization, albitization, silicification, and fluoritization.
The latter includes the following substages: arsenopyritization and scheelitization; pyritization, chalcopyritization, and
sphaleritization; and native gold, electrum, and calaveritization. The last substage is considered to be the main ore-forming
stage in the formation of the deposit. 相似文献