Geology Reports⌕ Search
Home/Archive/USGS

USGS · 70017253

One-level prediction-A numerical method for estimating undiscovered metal endowment

R.B. McCammon·J.O. Kork
View original source ↗Export metadata ↓

Abstract

One-level prediction has been developed as a numerical method for estimating undiscovered metal endowment within large areas. The method is based on a presumed relationship between a numerical measure of geologic favorability and the spatial distribution of metal endowment. Metal endowment within an unexplored area for which the favorability measure is greater than a favorability threshold level is estimated to be proportional to the area of that unexplored portion. The constant of proportionality is the ratio of the discovered endowment found within a suitably chosen control region, which has been explored, to the area of that explored region. In addition to the estimate of undiscovered endowment, a measure of the error of the estimate is also calculated. One-level prediction has been used to estimate the undiscovered uranium endowment in the San Juan basin, New Mexico, U.S.A. A subroutine to perform the necessary calculations is included. ?? 1992 Oxford University Press.

Explore related subjects

Nonrenewable ResourcesArticle

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

R.B. McCammon, J.O. Kork. 1992. One-level prediction-A numerical method for estimating undiscovered metal endowment. https://doi.org/10.1007/bf01782267

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↗