Geology Reports⌕ Search

USGS · 70018360

Is there a metric for mineral deposit occurrence probabilities?

Abstract

Traditionally, mineral resource assessments have been used to estimate the physical inventory of critical and strategic mineral commodities that occur in pieces of land and to assess the consequences of supply disruptions of these commodities. More recently, these assessments have been used to estimate the undiscovered mineral wealth in such pieces of land to assess the opportunity cost of using the land for purposes other than mineral production. The field of mineral resource assessment is an interdisciplinary field that draws elements from the disciplines of geology, economic geology (descriptive models), statistics and management science (grade and tonnage models), mineral economics, and operations research (computer simulation models). The purpose of this study is to assert that an occurrenceprobability metric exists that is useful in "filling out" an assessment both for areas in which only a trivial probability exists that a new mining district could be present and for areas where nontrivial probabilities exist for such districts. ?? 1993 Oxford University Press.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

L. J. Drew, W. D. Menzie. 1993. Is there a metric for mineral deposit occurrence probabilities?. https://doi.org/10.1007/bf02272806

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

KEEP EXPLORING

Related USGS reports

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↗