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1.
Estimates of the number of undiscovered deposits offer a unique perspective on the nation's undiscovered mineral resources. As part of the 1998 assessment of undiscovered deposits of gold, silver, copper, lead, and zinc, estimates of the number of deposits were made for 305 of the 447 permissive tracts delineated in 19 assessment regions of the country. By aggregating number of undiscovered deposits by deposit type and by assessment region, a picture of the nation's undiscovered resources has emerged. For the nation as a whole, the mean estimate for the number of undiscovered deposits is 950. There is a 90% chance there are at least 747 undiscovered deposits and a 10% chance there are as many as 1,160 undiscovered deposits. For Alaska, the mean estimate for the number of undiscovered deposits is 281. There is a 90% chance there are at least 168 undiscovered deposits and a 10% chance there are as many as 402 undiscovered deposits. Assuming that the majority of deposits used to create the grade and tonnage models that formed the basis for estimating the number of undiscovered deposits are significant deposits, there remain about as many undiscovered deposits as have already been discovered. Consideration of the number of undiscovered deposits as part of national assessments carried out on a recurring basis serves as a leading indicator of the nation's total mineral resources. 相似文献
2.
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
3.
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
4.
Gary L. Raines 《Natural Resources Research》2008,17(2):87-97
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. 相似文献
5.
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
6.
Estimates of the number of undiscovered deposits on a statewide basis offer a different perspective on the nation's undiscovered resources of gold, silver, copper, lead, and zinc. Mean estimates of the number of undiscovered deposits statewide were extracted from the estimates of undiscovered deposits nationwide. More than 50 undiscovered deposits are estimated to occur in Alaska, Arizona, Nevada, and Wisconsin. Estimating the number of undiscovered deposits statewide serves as a measure of a state's total remaining mineral resources in known conventional deposit types. 相似文献
7.
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
8.
Basic concepts in three-part quantitative assessments of undiscovered mineral resources 总被引：23，自引：0，他引：23
Donald A. Singer 《Natural Resources Research》1993,2(2):69-81
Since 1975, mineral resource assessments have been made for over 27 areas covering 5×106 km2 at various scales using what is now called the three-part form of quantitative assessment. In these assessments, (1) areas are delineated according to the types of deposits permitted by the geology,(2) the amount of metal and some ore characteristics are estimated using grade and tonnage models, and (3) the number of undiscovered deposits of each type is estimated.Permissive boundaries are drawn for one or more deposit types such that the probability of a deposit lying outside the boundary is negligible, that is, less than 1 in 100,000 to 1,000,000. 相似文献
9.
The U.S. Geological Survey (USGS) is proposing to conduct a global mineral-resource assessment using geologic maps, significant deposits, and exploration history as minimal data requirements. Using a geologic map and locations of significant pluton-related deposits, the pluton-related-deposit tract maps from the USGS national mineral-resource assessment have been reproduced with GIS-based analysis and modeling techniques. Agreement, kappa, and Jaccard's C correlation statistics between the expert USGS and calculated tract maps of 87%, 40%, and 28%, respectively, have been achieved using a combination of weights-of-evidence and weighted logistic regression methods. Between the experts' and calculated maps, the ranking of states measured by total permissive area correlates at 84%. The disagreement between the experts and calculated results can be explained primarily by tracts defined by geophysical evidence not considered in the calculations, generalization of tracts by the experts, differences in map scales, and the experts' inclusion of large tracts that are arguably not permissive. This analysis shows that tracts for regional mineral-resource assessment approximating those delineated by USGS experts can be calculated using weights of evidence and weighted logistic regression, a geologic map, and the location of significant deposits. Weights of evidence and weighted logistic regression applied to a global geologic map could provide quickly a useful reconnaissance definition of tracts for mineral assessment that is tied to the data and is reproducible. 相似文献
10.
Dennis P. Cox 《Natural Resources Research》1993,2(2):82-91
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
11.
The potential for mining hydrothermal mineral deposits on the seafloor, such as seafloor massive sulfides, has become technically possible, and some companies (currently not many) are considering their exploration and development. Yet, no present methodology has been designed to quantify the ore potential and assess the risks relative to prospectivity at prospect and regional scales. Multi-scale exploration techniques, similar to those of the play analysis that are used in the oil and gas industry, can help to fulfill this task by identifying the characteristics of geologic environments indicative of ore-forming processes. Such characteristics can represent a combination of, e.g., heat source, pathway, trap and reservoir that all dictate how and where ore components are mobilized from source to deposition. In this study, the understanding of these key elements is developed as a mineral system, which serves as a guide for mapping the risk of the presence or absence of ore-forming processes within the region of interest (the permissive tract). The risk analysis is carried out using geoscience data, and it is paired with quantitative resource estimation analysis to estimate the in-place mineral potential. Resource estimates are simulated stochastically with the help of available data (bathymetric features in this study), conventional grade–tonnage models and Monte Carlo simulation techniques. In this paper, the workflow for a multi-scale quantitative risk analysis, from the definition to the evaluation of a permissive tract and related prospect(s), is described with the help of multi-beam data of a known hydrothermal vent site.
相似文献12.
The concept of geologic/geographic clusters was developed particularly to study grade and tonnage models for sandstone-type uranium deposits. A cluster is a grouping of mined as well as unmined uranium occurrences within an arbitrary area about 8 km across. A cluster is a statistical sample that will reflect accurately the distribution of uranium in large regions relative to various geologic and geographic features. The example of the Colorado Plateau Uranium Province reveals that only 3 percent of the total number of clusters is in the largest tonnage-size category, greater than 10,000 short tons U3O8, and that 80 percent of the clusters are hosted by Triassic and Jurassic rocks. The distributions of grade and tonnage for clusters in the Powder River Basin show a wide variation; the grade distribution is highly variable, reflecting a difference between roll-front deposits and concretionary deposits, and the Basin contains about half the number in the greater-than-10,000 tonnage-size class as does the Colorado Plateau, even though it is much smaller. The grade and tonnage models should prove useful in finding the richest and largest uranium deposits. 相似文献
13.
14.
Evaluation of Weights of Evidence to Predict Epithermal-Gold Deposits in the Great Basin of the Western United States 总被引：7，自引：0，他引：7
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.
We propose an information-structure-analysis (ISA) method to quantify the correlations between quantitative and qualitative variables as well as within each type of variable. This method is applied to the evaluation of mineral resources in the western Zheijiang Province of China. The district contains a number of silver-bearing Fe–Cu–Pb–Zn mineral deposits near igneous complexes and FeCuPbZn zones away from the complexes. Silver anomalies occur not only in the known Fe–Cu–Zn–Pb deposits, but also in the country rock, suggesting the possible existence of silver deposits far from the igneous complexes.The tonnage distribution of silver is modeled by Monte Carlo simulation. This simulation is conducted on the basis of the correlations between silver (Ag) and lead (Pb), since no known data on silver is available. The known tonnage distribution of lead in 11 control cells was used to approximate the tonnage distribution of silver in the Monte Carlo simulation. With ISA and Monte Carlo methods, the total amount of potential polymetallic resources in 49 cells in the western Zhejiang Provice is predicted. Significantly, a deposit with about 24 tonnes of silver has been found within our exploration target area. 相似文献
16.
There are multiple ways to characterize uncertainty in the assessment of coal resources, but not all of them are equally satisfactory. Increasingly, the tendency is toward borrowing from the statistical tools developed in the last 50 years for the quantitative assessment of other mineral commodities. Here, we briefly review the most recent of such methods and formulate a procedure for the systematic assessment of multi-seam coal deposits taking into account several geological factors, such as fluctuations in thickness, erosion, oxidation, and bed boundaries. A lignite deposit explored in three stages is used for validating models based on comparing a first set of drill holes against data from infill and development drilling. Results were fully consistent with reality, providing a variety of maps, histograms, and scatterplots characterizing the deposit and associated uncertainty in the assessments. The geostatistical approach was particularly informative in providing a probability distribution modeling deposit wide uncertainty about total resources and a cumulative distribution of coal tonnage as a function of local uncertainty. 相似文献
17.
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
18.
Joseph S. Duval 《Natural Resources Research》2003,12(2):121-126
The U.S. Geological Survey has developed a technique that allows mineral resource experts to apply economic filters to estimates of undiscovered mineral resources. This technique builds on previous work that developed quantitative methods for mineral resource assessments. A Monte-Carlo calculation uses mineral deposit models to estimate commodity grades and tonnages of undiscovered deposits. The results then are analyzed using simple estimates of capital expenditures and daily operating costs for a mine and associated mill. The daily operating costs and the value of the ore are used to calculate the net present value of the deposit, which is compared to the capital expenditures to determine whether the deposit is economic. Repetition of these calculations for many deposits produces a table that can be interpreted in terms of the probability of there being deposits that have anet present value exceeding some specified amount. Sample calculations indicate that applying economic filters to simulated mineral resources might change the perception of the results compared to presenting the calculations in terms of the expected mean gross-in-place value of the minerals. 相似文献
19.
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
20.
James D. Bliss 《Natural Resources Research》1992,1(3):214-230
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