Classification of mineral deposits into types using mineralogy with a probabilistic neural network |
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Authors: | Donald A Singer Ryoichi Kouda |
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Institution: | (1) U.S. Geological Survey, 345 Middlefield Road, Menlo Park, 94025 California;(2) Geological Survey of Japan, 1-1-3 Higashi, 305 Tsukuba, Ibaraki-ken, Japan |
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Abstract: | 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 |
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Keywords: | Probabilistic neural network mineral deposit models mineralogy Bayes |
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