内蒙古达来敖包钼多金属矿地质特征及找矿潜力浅析

达来敖包钼多金属矿赋存在深部细粒花岗岩外接触带宝力高庙组变质粉砂岩、细砂岩内。钼矿化与北东向构造裂隙或构造破碎带有关,矿化体呈细脉状。矿体及围岩中,云英岩化、硅化、黄铁矿化比较强烈,并在细粒花岗岩外接触带发育,表明与岩浆热液的多次活动有关,属热液型矿床。通过目前勘查程度,达中——大型钼矿,分析其成矿地质特征、地球物理特征、地球化学特征、成矿地质环境等,认为深部及外围找矿潜力巨大。     

福建屏南灵峰钥矿床地质特征及找矿初探

屏南灵峰钼矿床赋存于中生代北西向浦城—宁德构造岩浆带内钼多金属成矿带东段,主要控矿地质因素为燕山期（含斑）细粒花岗岩和近南北向断裂构造,钼矿化与硅化、绢英岩化和黄铁矿化蚀变密切相关.通过数据分析,表明灵峰钼矿床成因类型属于岩浆热液充填型,成矿时代为燕山晚期白垩纪.     

福建屏南灵峰钼矿床地质特征及找矿初探

屏南灵峰钼矿床赋存于中生代北西向浦城—宁德构造岩浆带内钼多金属成矿带东段,主要控矿地质因素为燕山期(含斑)细粒花岗岩和近南北向断裂构造,钼矿化与硅化、绢英岩化和黄铁矿化蚀变密切相关.通过数据分析,表明灵峰钼矿床成因类型属于岩浆热液充填型,成矿时代为燕山晚期白垩纪.     

黄沙坑地区铀矿岩矿特征及其找矿意义

通过对黄沙坑地区的岩性、蚀变、矿物共生组合等特征进行分析,区内原岩发生了多种类、多期次的围岩蚀变,其中硅化、赤铁矿化、绿泥石化、黄铁矿化、绢云母化、粘土化、碳酸盐化等与铀矿化关系密切,且多为近矿围岩蚀变;根据矿石物质组成的种类、铀矿物与共生矿物等特征,认为黄沙坑地区矿床成因类型属花岗岩型中低温热液矿床。     

北山地区小狐狸山斑岩型钼多金属矿床成矿规律

小狐狸山铅锌钼矿床是近年来在内蒙古北山地区新发现的一个具中——大型规模的隐伏矿床,赋矿岩体划分为三个相带,即边缘相、过渡相和中心相,其岩性分别为云英岩化细粒似斑状花岗岩,钠长石化中细粒似斑状花岗岩,钾长石化中粗粒似斑状花岗岩。辉钼矿呈浸染状、星点状或细脉状分布于小狐狸山岩体的边缘相细粒似斑状花岗岩和过渡相中细粒似斑状花岗岩中,属典型的斑岩型铅锌钼金属矿床。成矿时代为三叠纪,属印支期构造——岩浆活动的产物。该矿床成矿特征典型,总结其成矿规律,该地区继续寻找类似矿床具有很大的潜力。     

湖北夷陵花岩钼矿床地质特征及开采条件分析

花岩钼矿床位于鄂西黄陵背斜,属斑岩型钼矿。钼矿主要产出在第一岩浆旋回晚期过渡相的含钼的石英脉及其蚀变带中与成矿有关的围岩蚀变以中低温热液蚀变为主。区内水文地质、工程地质为简单类型,环境地质条件中采空区引发地面变形和矿山排泄物污染水、土的问题,需要采取相应措施处理和预防,环境地质类型为第二类,该矿床开采技术条件勘查类型为Ⅱ-3,即环境地质问题为主的矿床。     

内蒙古乌拉特后旗查干花铜钼矿床地质特征及找矿标志

查干花钼矿床位于苏尼特右旗晚华力西地槽褶皱带,宝音图台隆中段,是乌力吉——锡林浩特铜、铁、铬、金、萤石成矿带内发现的又一大型斑岩型铜钼矿床。查干花钼矿床主要赋存早二叠世花岗闪长岩体处、北西和北东向断裂构造交汇部位。矿区内围岩蚀变强烈,并具有明显的分带性。辉钼矿呈星散状分布于脉石中形成的浸染状构造。笔者在分析矿床的成矿地质背景、矿床地质特征的基础上,初步总结了成因及找矿标志。认为查干花铜钼矿床的发现是该区域找矿的重大突破,显示出良好的区域找矿前景。     

内蒙古镶黄旗哈登苏木金铜矿矿床地质特征及成因初探

通过对内蒙古镶黄旗哈登苏木金铜矿矿床出露岩体、蚀变特征、矿物组合等地质特征的分析讨论,初步确定与金矿化密切相关的蚀变为钾化、黄铁绢英岩化,金属矿物组合为黄铜矿、自然银等矿物,该矿床为斑岩体有关的金铜矿床。     

对内蒙古自治区斑岩型钼矿成矿规律的几点认识

内蒙古地区的斑岩型钼矿,多处于大陆板块边缘,根据其岩浆特点及矿石品位(辉钼矿品位很少超过0.25%)等特征,大体归类于钙碱性型斑岩钼矿。其成矿母岩多为钙碱性及高钾钙碱性岩浆系列。钼以辉钼矿的形式赋存于网状石英—辉钼矿细脉、石英—辉钼矿大脉或以叶片状或叶片状集合体分散于岩体中。与钼铜成矿关系密切的蚀变主要是硅化、钾长石化、绢云母及云英岩化。     

河南栾川大竹园沟钼矿床地质特征及找矿前景

矿区属中国东部钼成矿域东秦岭—大别山钼成矿带。本文在讨论成矿地质背景基础上,对矿床地质特征进行了研究认为矿体的空间分布与矿区内褶皱、断裂、岩性、围岩热液蚀变强度有关,大竹园沟钼矿床为典型的斑岩型钼矿床。还指出了找矿标志,分析了找矿远景,对以后的找矿工作有一定的指导意义。     

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     

Typing Mineral Deposits Using Their Grades and Tonnages in an Artificial Neural Network

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.     

On a proposed plutonic porphyry gold deposit model

A plutonic porphyry gold deposit model is proposed that is imilar to the plutonic porphyry copper deposit model. However, unlike the plutonic porphyry copper deposit model, the proposed model is deficient in copper and contains less than 1 percent total sulfides. In the proposed model, gold is accompanied by scheelite, molybdenite, arsenopyrite, a variety of bismuth sulfides, tellurides, and native bismuth. The host rock varies from granite to granodiorite stock. Most of the ore is in the pluton. Deposits cited as examples of the proposed model are the Mokrsko deposit in Czechoslovakia, the Fort Knox deposit in the United States, and the Dublin Gulch deposit in Canada. In each of these deposits, pervasive potassic or phyllic alteration zones accompany the gold ore, which is disseminated in quartz-rich stockworks, veinlet swarms, and veins. Tonnages of gold-bearing material are large, but grades are low in the cited deposits. The proposed model is distinct from other gold deposit models because of the low Cu to Au ratio and the association of Au, Bi, W, and Mo.     

Composition and provenance of the Lilljeborgfjellet Conglomerate Haakon VII Land, Spitsbergen

The Siktefjcllet Group of late Silurian or early Devonian age. consisting of the Lilljeborgfjellet Conglomerate and the overlying Siktefjcllet Sandstone, is generally accepted as the oldest part of the Old Red Sandstone in Spitsbergen. Most of the clasts of the conglomerate are only slightly rounded and consist of lithologies typical for the underlying basement. A minor component of quartz porphyry clasts is present; these are well-rounded, indicating a longer transport. The provenance of the quartz porphyry clasts is discussed in relation to the known outcrops of quartz porphyry in Svalbard, one occurring in the neighbourhood of the conglomerate, the other ones far away. The quartz porphyry in close proximity is younger than the Lilljeborgfjellet Conglomerate and therefore not a possible source rock. A close petrographic and geochemical comparison with the quartz porphyries at three localities in Nordaustlandet (150-200 km in easterly direction and of probable Grenvillian age) is presented showing many similarities, but enough differences to question their interrelationship. The porphyries of the Hornsund area (300 km in southerly direction and also of probable Grenvillian age) are found to be chemically and pctrographically distinctly different from the Lilljeborgfjellet clast porphyry. Metarhyolite reported from the Planclfjclla and Har-kerbreen Groups in Ny Fricsland are not comparable with the clast porphyry. As no unquestionable source rock among the quartz porphyries is known in outcrop, the possibility of a hidden or completely eroded parent rock is considered.     

Geochemistry of the late Proterozoic Kapp Hansteen igneous rocks of Nordaustlandet, Svalbard

Proterozoic igneous rocks occur in three areas in Nordaustlandet, Svalbard, and are found in the upper part of the Lower Hecla Hoek succession, the Botniahalvøya Supergroup. The rocks have been called porphyrites in Botniahalvøya, metadiabases in Prins Oscars Land and quartz porphyries in both areas as well as in the Sabinebukta area. All rocks have been metamorphosed under the greenschist facies conditions. The porphyrites are calc-alkaline acid andesites and dacites of medium to high K2O type, possibly showing a transition to tholeiitic series. The quartz porphyries are calc-alkaline rhyolites of high K20 type. The metadiabases are subdivided into two: the basic dykes of low K20 type and relatively high Fe tholeiite series, while the main bodies are acid andesites of medium to high K20 and low Fe tholeiite series. The basic dykes fall in the oceanic rock field of the Tiø2-K20-P20s diagram, and are most likely belonging to the island arc type volcanism. The metadiabases of main bodies and the porphyrites, and possibly the quartz porphyries, are chemically continuous. The medium to high K20 contents, and their Tiø2-K20-P2O5 ratios suggest that these three rock groups are non-oceanic and resemble the rock associations of the areas having thick continental crust. This conclusion agrees with the reported high initial Sr^87/86 ratios and the existence of a distinct unconformity at the base of this volcanogenic succession.     

Enrichment Ratio-Tonnage Diagrams for Resource Assessment

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.     

内蒙古库伦旗拉民稿金多金属矿化区成矿地质特征及找矿方向

拉民稿金多金属矿化区位于通辽市库伦旗西南部拉民稿地区,根据野外地质调查结果,在矿区圈出金多金属矿化带四条（PS1、PS2、PS3、PS4）,采样含金品位一般为（0.11-7.48）×10-6,最高为13.56×10-6,含银最高品位为92.5×10-6。钻孔中圈出矿体可分三类：灰岩（见黄铁矿化、硅化）、板岩（见黄铁矿化...     

Are Fractal Dimensions of the Spatial Distribution of Mineral Deposits Meaningful?

It has been proposed that the spatial distribution of mineral deposits is bifractal. An implication of this property is that the number of deposits in a permissive area is a function of the shape of the area. This is because the fractal density functions of deposits are dependent on the distance from known deposits. A long thin permissive area with most of the deposits in one end, such as the Alaskan porphyry permissive area, has a major portion of the area far from known deposits and consequently a low density of deposits associated with most of the permissive area. On the other hand, a more equi-dimensioned permissive area, such as the Arizona porphyry permissive area, has a more uniform density of deposits. Another implication of the fractal distribution is that the Poisson assumption typically used for estimating deposit numbers is invalid. Based on datasets of mineral deposits classified by type as inputs, the distributions of many different deposit types are found to have characteristically two fractal dimensions over separate non-overlapping spatial scales in the range of 5–1000 km. In particular, one typically observes a local dimension at spatial scales less than 30–60 km, and a regional dimension at larger spatial scales. The deposit type, geologic setting, and sample size influence the fractal dimensions. The consequence of the geologic setting can be diminished by using deposits classified by type. The crossover point between the two fractal domains is proportional to the median size of the deposit type. A plot of the crossover points for porphyry copper deposits from different geologic domains against median deposit sizes defines linear relationships and identifies regions that are significantly underexplored. Plots of the fractal dimension can also be used to define density functions from which the number of undiscovered deposits can be estimated. This density function is only dependent on the distribution of deposits and is independent of the definition of the permissive area. Density functions for porphyry copper deposits appear to be significantly different for regions in the Andes, Mexico, United States, and western Canada. Consequently, depending on which regional density function is used, quite different estimates of numbers of undiscovered deposits can be obtained. These fractal properties suggest that geologic studies based on mapping at scales of 1:24,000 to 1:100,000 may not recognize processes that are important in the formation of mineral deposits at scales larger than the crossover points at 30–60 km.     

The hydrogeochemistry of the Kärkevagge Valley, Lapland, Sweden

The Kärkejokk (jokk = Lappish for brook) is rich in sulfate and calcium, both elements having been considered enigmatic. To resolve these problems we collected waters at 13 sites during 27 June to 1 September 1996. Nine sites were in the Kärkevagge, and the others in the drainage towards lake Torne Träsk. Rain waters were collected the same period. Conductivity, pH, and temperature were measured in the field, whereas salt load and the elements Na, K, Ca, Mg, S, Si, Fe, Al, Mn, Zn, Sr, and Ba were determined in the laboratory.
Mixing models based on rain water and leaching products of the major bedrocks do not explain observed element patterns except in the lower parts of the jokk. However, oxidation of pyrite has formed acid, sulfate–rich solutions that released Ca and Mg from limestones, and Fe, Mn, Al, and Si, from black shales (Malmsten 1998; Malmsten et al. 2000). Conservative mixing models, using rain water, leached bedrock and pyrite, match the jokk waters quite well, and sulfur isotope data corroborate these findings. The nearby Låktajokk, and Vassijokk also contain much S.
Where these waters debouch they may deposit Si, Al, and Ca, but only little S on various rocks. Total rock analyses, thermodynamic and X–ray data suggest that gypsum, barite, or alunite are not formed in major quantities.
These models show that the hydrogeochemistry of the Kärkejokk may be less enigmatic than often assumed.     

The hydrogeochemistry of the Kärkevagge Valley, Lapland, Sweden

The Kärkejokk (jokk = Lappish for brook) is rich in sulfate and calcium, both elements having been considered enigmatic. To resolve these problems we collected waters at 13 sites during 27 June to 1 September 1996. Nine sites were in the Kärkevagge, and the others in the drainage towards lake Torne Träsk. Rain waters were collected the same period. Conductivity, pH, and temperature were measured in the field, whereas salt load and the elements Na, K, Ca, Mg, S, Si, Fe, Al, Mn, Zn, Sr, and Ba were determined in the laboratory.
Mixing models based on rain water and leaching products of the major bedrocks do not explain observed element patterns except in the lower parts of the jokk. However, oxidation of pyrite has formed acid, sulfate–rich solutions that released Ca and Mg from limestones, and Fe, Mn, Al, and Si, from black shales (Malmsten 1998; Malmsten et al. 2000). Conservative mixing models, using rain water, leached bedrock and pyrite, match the jokk waters quite well, and sulfur isotope data corroborate these findings. The nearby Låktajokk, and Vassijokk also contain much S.
Where these waters debouch they may deposit Si, Al, and Ca, but only little S on various rocks. Total rock analyses, thermodynamic and X–ray data suggest that gypsum, barite, or alunite are not formed in major quantities.
These models show that the hydrogeochemistry of the Kärkejokk may be less enigmatic than often assumed.