Search USGS⌕ Search
Home/Archive/USGS

USGS · 70016991

Grade-tonnage and other models for diamond kimberlite pipes

J. D. Bliss
View original source ↗Export metadata ↓

Abstract

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. ?? 1992 Oxford University Press.

Explore related subjects

Nonrenewable ResourcesArticle

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

J. D. Bliss. 1992. Grade-tonnage and other models for diamond kimberlite pipes. https://doi.org/10.1007/bf01782275

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS1997

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.

Nonrenewable Resources↗