NDLI: Classification of mineral deposits into types using mineralogy with a probabilistic neural network

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U.S. department of the interior U.S. geological survey: Fractal lognormal percentage analysis of the U.S. geological survey’s 1995 national assessment of conventional oil and gas resources

Long-term implications of new U.S. gas estimates

Book reviews : Natural Resources Research , Volume 6 , Issue 1

Classification of mineral deposits into types using mineralogy with a probabilistic neural network

Content Provider	Springer Nature Link
Author	Singer, Donald A. Kouda, Ryoichi
Copyright Year	1997
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
Starting Page	27
Ending Page	32
Page Count	6
File Format	PDF
ISSN	09611444
Journal	Natural Resources Research
Volume Number	6
Issue Number	1
e-ISSN	15738981
Language	English
Publisher	Springer US
Publisher Date	1997-01-01
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Probabilistic neural network mineral deposit models mineralogy Bayes Math. Applications in Geosciences Economic Geology Sedimentology Hydrogeology
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Environmental Science

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