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1.
The method of making quantitative assessments of mineral resources sufficiently detailed for economic analysis is outlined in three steps. The steps are (1) determination of types of deposits that may be present in an area, (2) estimation of the numbers of deposits of the permissible deposit types, and (3) combination by Monte Carlo simulation of the estimated numbers of deposits with the historical grades and tonnages of these deposits to produce a probability distribution of the quantities of contained metal.Two examples of the estimation of the number of deposits (step 2) are given. The first example is for mercury deposits in southwestern Alaska and the second is for lode tin deposits in the Seward Peninsula.The flow of the Monte Carlo simulation program is presented with particular attention to the dependencies between grades and tonnages of deposits and between grades of different metals in the same deposit.  相似文献   

2.
Quantitative mineral resource assessments used by the United States Geological Survey are based on deposit models. These assessments consist of three parts: (1) selecting appropriate deposit models and delineating on maps areas permissive for each type of deposit; (2) constructing a grade-tonnage model for each deposit model; and (3) estimating the number of undiscovered deposits of each type. In this article, I focus on the estimation of undiscovered deposits using two methods: the deposit density method and the target counting method.In the deposit density method, estimates are made by analogy with well-explored areas that are geologically similar to the study area and that contain a known density of deposits per unit area. The deposit density method is useful for regions where there is little or no data. This method was used to estimate undiscovered low-sulfide gold-quartz vein deposits in Venezuela.Estimates can also be made by counting targets such as mineral occurrences, geophysical or geochemical anomalies, or exploration plays and by assigning to each target a probability that it represents an undiscovered deposit that is a member of the grade-tonnage distribution. This method is useful in areas where detailed geological, geophysical, geochemical, and mineral occurrence data exist. Using this method, porphyry copper-gold deposits were estimated in Puerto Rico.  相似文献   

3.
A desirable guide for estimating the number of undiscovered mineral deposits is the number of known deposits per unit area from another well-explored permissive terrain. An analysis of the distribution of 805 podiform chromite deposits among ultramafic rocks in 12 subareas of Oregon and 27 counties of California is used to examine and extend this guide. The average number of deposits in this sample of 39 areas is 0.225 deposits per km2 of ultramafic rock; the frequency distribution is significantly skewed to the right. Probabilistic estimates can be made by using the observation that the lognormal distribution fits the distribution of deposits per unit area. A further improvement in the estimates is available by using the relationship between the area of ultramafic rock and the number of deposits.The number (N) of exposed podiform chromite deposits can be estimated by the following relationship: log10(N)=–0.194+0.577 log10(area of ultramafic rock). The slope is significantly different from both 0.0 and 1.0. Because the slope is less than 1.0, the ratio of deposits to area of permissive rock is a biased estimator when the area of ultramafic rock is different from the median 93 km2. Unbiased estimates of the number of podiform chromite deposits can be made with the regression equation and 80 percent confidence limits presented herein.  相似文献   

4.
Estimates of numbers of undiscovered mineral deposits, fundamental to assessing mineral resources, are affected by map scale. Where consistently defined deposits of a particular type are estimated, spatial and frequency distributions of deposits are linked in that some frequency distributions can be generated by processes randomly in space whereas others are generated by processes suggesting clustering in space. Possible spatial distributions of mineral deposits and their related frequency distributions are affected by map scale and associated inclusions of non-permissive or covered geological settings. More generalized map scales are more likely to cause inclusion of geologic settings that are not really permissive for the deposit type, or that include unreported cover over permissive areas, resulting in the appearance of deposit clustering. Thus, overly generalized map scales can cause deposits to appear clustered. We propose a model that captures the effects of map scale and the related inclusion of non-permissive geologic settings on numbers of deposits estimates, the zero-inflated Poisson distribution. Effects of map scale as represented by the zero-inflated Poisson distribution suggest that the appearance of deposit clustering should diminish as mapping becomes more detailed because the number of inflated zeros would decrease with more detailed maps. Based on observed worldwide relationships between map scale and areas permissive for deposit types, mapping at a scale with twice the detail should cut permissive area size of a porphyry copper tract to 29% and a volcanic-hosted massive sulfide tract to 50% of their original sizes. Thus some direct benefits of mapping an area at a more detailed scale are indicated by significant reductions in areas permissive for deposit types, increased deposit density and, as a consequence, reduced uncertainty in the estimate of number of undiscovered deposits. Exploration enterprises benefit from reduced areas requiring detailed and expensive exploration, and land-use planners benefit from reduced areas of concern.  相似文献   

5.
Mineral-deposit models are an integral part of quantitative mineral-resource assessment. As the focus of mineral-deposit modeling has moved from metals to industrial minerals, procedure has been modified and may be sufficient to model surficial sand and gravel deposits. Sand and gravel models are needed to assess resource-supply analyses for planning future development and renewal of infrastructure. Successful modeling of sand and gravel deposits must address (1) deposit volumes and geometries, (2) sizes of fragments within the deposits, (3) physical characteristics of the material, and (4) chemical composition and chemical reactivity of the material. Several models of sand and gravel volumes and geometries have been prepared and suggest the following: Sand and gravel deposits in alluvial fans have a median volume of 35 million m3. Deposits in all other geologic settings have a median volume of 5.4 million m3, a median area of 120 ha, and a median thickness of 4 m. The area of a sand and gravel deposit can be predicted from volume using a regression model (log [area (ha)] =1.47+0.79 log [volume (million m3)]). In similar fashion, the volume of a sand and gravel deposit can be predicted from area using the regression (log [volume (million m3)]=–1.45+1.07 log [area (ha)]). Classifying deposits by fragment size can be done using models of the percentage of sand, gravel, and silt within deposits. A classification scheme based on fragment size is sufficiently general to be applied anywhere.  相似文献   

6.
The quantitative probabilistic assessment of the undiscovered mineral resources of the 17.1-million-acre Tongass National Forest (the largest in the United States) and its adjacent lands is a nonaggregated, mineral-resource-tract-oriented assessment designed for land-planning purposes. As such, it includes the renewed use of gross-in-place values (GIPV's) in dollars of the estimated amounts of metal contained in the undiscovered resources as a measure for land-use planning.Southeastern Alaska is geologically complex and contains a wide variety of known mineral deposits, some of which have produced important amounts of metals during the past 100 years. Regional geological, economic geological, geochemical, geophysical, and mineral exploration history information for the region was integrated to define 124 tracts likely to contain undiscovered mineral resources. Some tracts were judged to contain more than one type of mineral deposit. Each type of deposit may contain one or more metallic elements of economic interest. For tracts where information was sufficient, the minimum number of as-yet-undiscovered deposits of each type was estimated at probability levels of 0.95, 0.90, 0.50, 0.10, and 0.05.The undiscovered mineral resources of the individual tracts were estimated using the U.S. Geological Survey's MARK3 mineral-resource endowment simulator; those estimates were used to calculate GIPV's for the individual tracts. Those GIPV's were aggregated to estimate the value of the undiscovered mineral resources of southeastern Alaska. The aggregated GIPV of the estimates is $40.9 billion.Analysis of this study indicates that (1) there is only a crude positive correlation between the size of individual tracts and their mean GIPV's: and (2) the number of mineral-deposit types in a tract does not dominate the GIPV's of the tracts, but the inferred presence of synorogenic-synvolcanic nickel-copper, porphyry copper skarn-related, iron skarn, and porphyry copper-molybdenum deposits does. The influence of this study on the U.S. Forest Service planning process is yet to be determined.  相似文献   

7.
The deposit size frequency (DSF) method has been developed as a generalization of the method that was used in the National Uranium Resource Evaluation (NURE) program to estimate the uranium endowment of the United States. The DSF method overcomes difficulties encountered during the NURE program when geologists were asked to provide subjective estimates of (1) the endowed fraction of an area judged favorable (factorF) for the occurrence of undiscovered uranium deposits and (2) the tons of endowed rock per unit area (factorT) within the endowed fraction of the favorable area. Because the magnitudes of factorsF andT were unfamiliar to nearly all of the geologists, most geologists responded by estimating the number of undiscovered deposits likely to occur within the favorable area and the average size of these deposits. The DSF method combines factorsF andT into a single factor (F·T) that represents the tons of endowed rock per unit area of the undiscovered deposits within the favorable area. FactorF·T, provided by the geologist, is the estimated number of undiscovered deposits per unit area in each of a number of specified deposit-size classes. The number of deposit-size classes and the size interval of each class are based on the data collected from the deposits in known (control) areas. The DSF method affords greater latitude in making subjective estimates than the NURE method and emphasizes more of the everyday experience of exploration geologists. Using the DSF method, new assessments have been made for the young, organic-rich surficial uranium deposits in Washington and idaho and for the solution-collapse breccia pipe uranium deposits in the Grand Canyon region in Arizona and adjacent Utah.  相似文献   

8.
Empirical evidence indicates that processes affecting number and quantity of resources in geologic settings are very general across deposit types. Sizes of permissive tracts that geologically could contain the deposits are excellent predictors of numbers of deposits. In addition, total ore tonnage of mineral deposits of a particular type in a tract is proportional to the type’s median tonnage in a tract. Regressions using size of permissive tracts and median tonnage allow estimation of number of deposits and of total tonnage of mineralization. These powerful estimators, based on 10 different deposit types from 109 permissive worldwide control tracts, generalize across deposit types. Estimates of number of deposits and of total tonnage of mineral deposits are made by regressing permissive area, and mean (in logs) tons in deposits of the type, against number of deposits and total tonnage of deposits in the tract for the 50th percentile estimates. The regression equations (R 2 = 0.91 and 0.95) can be used for all deposit types just by inserting logarithmic values of permissive area in square kilometers, and mean tons in deposits in millions of metric tons. The regression equations provide estimates at the 50th percentile, and other equations are provided for 90% confidence limits for lower estimates and 10% confidence limits for upper estimates of number of deposits and total tonnage. Equations for these percentile estimates along with expected value estimates are presented here along with comparisons with independent expert estimates. Also provided are the equations for correcting for the known well-explored deposits in a tract. These deposit-density models require internally consistent grade and tonnage models and delineations for arriving at unbiased estimates.  相似文献   

9.
In this article, we examine the use of an unconventional procedure, PETRIMES, to estimate mineral resources of mercury deposits in California. The study, which is based on the nonparametric discovery process model and Q-Q plots, suggests that a lognormal distribution is appropriate for the mercury deposits in California. The results of the assessment are summarized as follows: (1) the total number of mercury deposits in the population is approximately 165; (2) the median value of the largest undiscovered deposit size is 487 flasks; (3) the mean of the remaining mercury potential is 2,500 flasks; and (4) the population resource ranges from 1,040,000 to 4,300,000 flasks (at a 0.9 probability level).  相似文献   

10.
Grade-tonnage and other quantitative models help give reasonable answers to questions about diamond kimberlite pipes. Diamond kimberlite pipes are those diamondiferous kimberlite pipes that either have been worked or are expected to be worked for diamonds. These models are not applicable to kimberlite dikes and sills or to lamproite pipes. Diamond kimberlite pipes contain a median 26 million metric tons (mt); the median diamond grade is 0.25 carat/metric ton (ct/mt). Deposit-specific models suggest that the median of the average diamond size is 0.07 ct and the median percentage of diamonds that are industrial quality is 67 percent. The percentage of diamonds that are industrial quality can be predicted from deposit grade using a regression model (log[industrial diamonds (percent)]=1.9+0.2 log[grade (ct/mt)]). The largest diamond in a diamond kimberlite pipe can be predicted from deposit tonnage using a regression model (log[largest diamond (ct)]=–1.5+0.54 log[size (mt]). The median outcrop area of diamond pipes is 12 hectares (ha). Because the pipes have similar forms, the tonnage of the deposits can be predicted by the outcrop area (log[size (mt)]=6.5+1.0 log[outcrop area (ha)]). Once a kimberlite pipe is identified, the probability is approximately .005 that it can be worked for diamonds. If a newly discovered pipe is a member of a cluster that contains a known diamond kimberlite pipe, the probability that the new discovery can be mined for diamonds is 56 times that for a newly discovered kimberlite pipe in a cluster without a diamond kimberlite pipe. About 30 percent of pipes with worked residual caps at the surface will be worked at depth. Based on the number of discovered deposits and the area of stable craton rocks thought to be well explored in South Africa, about 10–5 diamond kimberlite pipes are present per square kilometer. If this density is applicable to the South American Precambrian Shield, more than 70 undiscovered kimberlite pipes are predicted to be present.  相似文献   

11.
Examining Risk in Mineral Exploration   总被引：4，自引：0，他引：4  
Successful mineral exploration strategy requires identification of some of the risk sources and considering them in the decision-making process so that controllable risk can be reduced. Risk is defined as chance of failure or loss. Exploration is an economic activity involving risk and uncertainty, so risk also must be defined in an economic context. Risk reduction can be addressed in three fundamental ways: (1) increasing the number of examinations; (2) increasing success probabilities; and (3) changing success probabilities per test by learning. These provide the framework for examining exploration risk. First, the number of prospects examined is increased, such as by joint venturing, thereby reducing chance of gambler's ruin. Second, success probability is increased by exploring for deposit types more likely to be economic, such as those with a high proportion of world-class deposits. For example, in looking for 100+ ton (>3 million oz) Au deposits, porphyry Cu-Au, or epithermal quartz alunite Au types require examining fewer deposits than Comstock epithermal vein and most other deposit types. For porphyry copper exploration, a strong positive relationship between area of sulfide minerals and deposits' contained Cu can be used to reduce exploration risk by only examining large sulfide systems. In some situations, success probabilities can be increased by examining certain geologic environments. Only 8% of kuroko massive sulfide deposits are world class, but success chances can be increased to about 15% by looking in settings containing sediments and rhyolitic rocks. It is possible to reduce risk of loss during mining by sequentially developing and expanding a mine—thus reducing capital exposed at early stages and reducing present value of risked capital. Because this strategy is easier to apply in some deposit types than in others, the strategy can affect deposit types sought. Third, risk is reduced by using prior information and by changing the independence of trials assumption, that is, by learning. Bayes' formula is used to change the probability of existence of the deposit sought on the basis of successive exploration stages. Perhaps the most important way to reduce exploration risk is to employ personnel with the appropriate experience and yet who are learning.  相似文献   

12.
A personal computer-based geographic information system (GIS) is used to develop a geographic expert system (GES) for mapping and evaluating volcanogenic massive sulfide (VMS) deposit potential. The GES consists of an inference network to represent expert knowledge, and a GIS to handle the spatial analysis and mapping. Evidence from input maps is propagated through the inference network, combining information by means of fuzzy logic and Bayesian updating to yield new maps showing evaluation of hypotheses. Maps of evidence and hypotheses are defined on a probability scale between 0 and 1. Evaluation of the final hypothesis results in a mineral potential map, and the various intermediate hypotheses can also be shown in map form.The inference net, with associated parameters for weighting evidence, is based on a VMS deposit model for the Chisel Lake deposit, a producing mine in the Early Protoerzoic Snow Lake greenstone belt of northwest Manitoba. The model is applied to a small area mapped at a scale of 1:15,840. The geological map, showing lithological and alteration units, provides the basic input to the model. Spatial proximity to contacts of various kinds are particularly important. Three types of evidence are considered: stratigraphic, heat source, and alteration. The final product is a map showing the relative favorability for VMS deposits. The model is implemented as aFortran program, interfaced with the GIS. The sensitivity of the model to changes in the parameters is evaluated by comparing predicted areas of elevated potential with the spatial distribution of known VMS occurrences.  相似文献   

13.
Reconstruction of prehistoric tropical cyclone (TC) activity often relies on the identification of distinctive overwash deposits (tempestites) in coastal lagoon sediments. Similar sediment deposits, however, can result from high-energy events other than TCs. In this study we assessed the utility of using the geochemistry of ostracod valves, specifically their stable oxygen isotope composition (δ18O), as a potential validation variable that could reduce the chances of misidentifying an overwash deposit as having been generated by a TC, when in fact it formed from another high-energy depositional process (type 1 error). We applied this technique to a sediment core recovered from Laguna Alejandro, Dominican Republic, which had already been analyzed for other sedimentary TC proxies. Negative δ18O anomalies identified in the ostracod valve stable isotope record are associated with TC deposits and are most easily explained by large influxes of 18O-depleted meteoric waters typical of intense tropical storms. There is potential for this technique to be used to identify TC landfalls that are not represented by overwash deposits. We, however, propose a more conservative approach and suggest this technique be used to validate the origin of a storm deposit and reduce the odds of a type 1 error.  相似文献   

14.
The weights-of-evidence method provides a simple approach to the integration of diverse geologic information. The application addressed is to construct a model that predicts the locations of epithermal-gold mineral deposits in the Great Basin of the western United States. Weights of evidence is a data-driven method requiring known deposits and occurrences that are used as training sites in the evaluated area. Four hundred and fifteen known hot spring gold–silver, Comstock vein, hot spring mercury, epithermal manganese, and volcanogenic uranium deposits and occurrences in Nevada were used to define an area of 327.4 km2 as training sites to develop the model. The model consists of nine weighted-map patterns that are combined to produce a favorability map predicting the distribution of epithermal-gold deposits. Using a measure of the association of training sites with predictor features (or patterns), the patterns can be ranked from best to worst predictors. Based on proximity analysis, the strongest predictor is the area within 8 km of volcanic rocks younger than 43 Ma. Being close to volcanic rocks is not highly weighted, but being far from volcanic rocks causes a strong negative weight. These weights suggest that proximity to volcanic rocks define where deposits do not occur. The second best pattern is the area within 1 km of hydrothermally altered areas. The next best pattern is the area within 1 km of known placer-gold sites. The proximity analysis for gold placers weights this pattern as useful when close to known placer sites, but unimportant where placers do not exist. The remaining patterns are significantly weaker predictors. In order of decreasing correlation, they are: proximity to volcanic vents, proximity to east-west to northwest faults, elevated airborne radiometric uranium, proximity to northwest to west and north-northwest linear features, elevated aeromagnetics, and anomalous geochemistry. This ordering of the patterns is a function of the quality, applicability, and use of the data. The nine-pattern favorability map can be evaluated by comparison with the USGS National Assessment for hot spring gold–silver deposits. The Spearman's ranked correlation coefficient between the favorability and the National Assessment permissive tracts is 0.5. Tabulations of the areas of agreement and disagreement between the two maps show 74% agreement for the Great Basin. The posterior probabilities for 51 significant deposits in the Great Basin, both used and not used in the model, show the following: 26 classified as favorable; 25 classified as permissive; and 1, Florida Canyon, classified as nonpermissive.The Florida Canyon deposit has a low favorability because there are no volcanic rocks near the deposit on the Nevada geologic map used. The largest areas of disagreement are caused by the USGS National Assessment team concluding that volcanic rocks older than 27 Ma in Nevada are not permissive, which was not assumed in this model. The weights-of-evidence model is evaluated as reasonable and delineates permissive areas for epithermal deposits comparable to expert's delineation. The weights-of-evidence model has the additional characteristics that it is well defined, reproducible, objective, and provides a quantitative measure of confidence.  相似文献   

15.
The Bendigo and Stawell zones in Victoria and the Mossman Orogen in north Queensland host numerous orogenic gold deposits and are likely to contain significant undiscovered gold resources. This paper discusses applications of Zipf’s law to estimate the scale of residual gold endowment in each of the regions. Testing various plausible scenarios on whether or not the largest deposit in each region has been discovered and its endowment adequately evaluated provided some measure of uncertainty of assessment results. The Bendigo and Stawell zones are estimated to host 12 undiscovered ore fields with >31 t (1 Moz) of contained gold and another 35 undiscovered ore fields with >10 t (0.32 Moz) of gold, containing in total 1600 t (51 Moz) of gold. The total residual orogenic gold endowment of the Mossman Orogen is estimated to be between 3 and 30 t of gold, contained in extensions of known deposits and up to six significant undiscovered gold ore fields each containing >1 t of gold. These estimates are comparable to results of recent three-part quantitative mineral resource assessments for those areas.  相似文献   

16.
察尔汗盐湖赋存有硫酸镁亚型和氯化物型两种水化学类型,沉积了中国最大的液体钾镁盐矿床。为解释察尔汗盐湖卤水矿床成因,拟在别勒滩、达布逊、察尔汗和霍布逊区段选择4个剖面,剖面深度在0~7 m之间,属于全新统上含盐组的上部盐层,每隔10cm进行采样,运用XRD半定量方法分析矿物组合特征。结果表明,整个湖区矿物组合由石盐、石膏、水氯镁石和碳酸盐组成,其平均含量分别为70%、4.7%、3.4%和1%;显示其矿物组合特征简单,盐层主要沉积石盐而贫石膏和碳酸盐矿物。同时,研究发现各区段石膏(硫酸盐矿物)平均含量自西向东明显下降,含镁矿物平均含量自西南向东北明显下降。结合察尔汗盐湖区卤水化学组成和水化学类型的分带,基本符合盐湖北部和东北部卤水富Ca~(2+),贫Mg~(2+)和SO_4~(2-)的沉积事实,进一步说明盐湖北部和东北部卤水和盐类沉积受具有氯化物型盐泉水的补给影响,为察尔汗盐湖混合掺杂成因提供了一定的矿物学证据。  相似文献   

17.
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   

18.
Bedded trona (Na2CO3·NaHCO3·2H2O) in the lacustrine Green River Formation of Eocene age in the Green River Basin, southwest Wyoming, constitutes the largest known resource of natural sodium carbonate in the world. In this study, 116 gigatons (Gt) of trona ore are estimated to be present in 22 beds, ranging from 1.2 to 11 meters (m) in thickness. Of this total, 69 Gt of trona ore are estimated to be in beds containing less than 2 percent halite and 47 Gt in beds containing 2 or more percent halite. These 22 beds underlie areas of about 130 to more than 2,000 km2 at depths ranging from about 200 m to more than 900 m below the surface. The total resource of trona ore in the basin for which drilling information is available is estimated to be about 135 Gt.Underveloped trona beds in the deeper southern part of the basin may be best developed by solution mining. Additional unevaluated sodium carbonate resources are present in disseminated shortite (Na2CO3·2CaCO3) in strata interbedded with the trona and in shallow sodium carbonate brines in the northeast part of the basin. Estimates of the shortite and brine resources were not made.  相似文献   

19.
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   

20.
盐湖沉积记录了区域气候和水文变化,是重要的古气候研究对象。年代学是盐湖古气候研究最重要的一项内容,是后续工作的基础。盐湖沉积最常用的定年方法有14C定年、铀系定年、光释光(OSL)定年、古地磁定年。受各种定年方法自身局限性以及盐湖沉积特殊性的制约,存在不同方法测出的年龄差异较大的现象。准确测定盐湖沉积的年代还较为困难,一定程度限制了盐湖古气候研究的发展。最新研究表明,由于盐湖沉积有机质含量低,易受现代碳的污染,14C测年中存在复杂的碳库效应,其14C年代老于30 cal ka BP时,测出的年龄可能被低估,需谨慎对待。未来需要加强铀系定年和光释光定年等方法在盐湖沉积中的基础研究,并开发新的更好的测年方法,提高盐湖沉积测年的准确度,为深入开展盐湖古气候变化及成盐成矿规律研究提供坚实基础。  相似文献   

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