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  • 1
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    Application of a feedforward neural network in the search for Kuroko deposits in the Hokuroku district, Japan (1996)
    Springer
    Details
    Publication Date: 1996-11-01
    Print ISSN: 1874-8961
    Electronic ISSN: 1874-8953
    Topics: Geosciences , Mathematics
    Published by Springer
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  • 2
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    Classification of mineral deposits into types using mineralogy with a probabilistic neural network (1997)
    Springer
    Details
    Publication Date: 1997-03-01
    Print ISSN: 1520-7439
    Electronic ISSN: 1573-8981
    Topics: Geosciences
    Published by Springer
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  • 3
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    World class base and precious metal deposits; a quantitative analysis (1995)
    Society of Economic Geologists
    Details
    Publication Date: 1995-02-01
    Print ISSN: 0361-0128
    Electronic ISSN: 1554-0774
    Topics: Geosciences
    Published by Society of Economic Geologists
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  • 4
    Electronic Resource
    Electronic Resource
    Application of a feedforward neural network in the search for Kuroko deposits in the Hokuroku district, Japan (1996)
    Springer
    Mathematical geology 28 (1996), S. 1017-1023 
    Details
    ISSN: 1573-8868
    Keywords: neural network ; kuroko massive sulfide ; exploration
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Mathematics
    Notes: Abstract A feedforward neural network with one hidden layer and five neurons was trained to recognize the distance to kuroko mineral deposits. Average amounts per hole of pyrite, sericite, and gypsum plus anhydrite as measured by X-rays in 69 drillholes were used to train the net. Drillholes near and between the Fukazawa, Furutobe, and Shakanai mines were used. The training data were selected carefully to represent well-explored areas where some confidence of the distance to ore was assured. A logarithmic transform was applied to remove the skewness of distance and each variable was scaled and centered by subtracting the median and dividing by the interquartile range. The learning algorithm of annealing plus conjugate gradients was used to minimize the mean squared error of the scaled distance to ore. The trained network then was applied to all of the 152 drillholes that had measured gypsum, sericite, and pyrite. A contour plot of the neural net predicted distance to ore shows fairly wide areas of 1 km or less to ore; each of the known deposit groups is within the 1 km contour. The high and low distances on the margins of the contoured distance plot are in part the result of boundary effects of the contouring algorithm. For example, the short distances to ore predicted west of the Shakanai (Hanaoka) deposits are in basement. However, the short distances to ore predicted northeast of Furotobe, just off the figure, coincide with the location of the Nurukawa kuroko deposit and the Omaki deposit, south of the Shakanai-Hanaoka deposits, seems to be on an extension of short distance to ore contour, but is beyond the 3 km limit from drillholes. Also of interest are some areas only a few kilometers from the Fukazawa and Shakanai groups of deposits that are estimated to be many kilometers from ore, apparently reflecting the network's recognition of the extreme local variability of the geology near some deposits.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02068587
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  • 5
    Electronic Resource
    Electronic Resource
    Examining Risk in Mineral Exploration (1999)
    Springer
    Natural resources research 8 (1999), S. 111-122 
    Details
    ISSN: 1573-8981
    Keywords: Risk ; mineral exploration ; decision-making ; mineral economics ; mineral resources ; risk assessment ; Bayes
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences
    Notes: Abstract Successful mineral exploration strategy requires identification of some of the risk sources and considering them in the decision-making process so that controllable risk can be reduced. Risk is defined as chance of failure or loss. Exploration is an economic activity involving risk and uncertainty, so risk also must be defined in an economic context. Risk reduction can be addressed in three fundamental ways: (1) increasing the number of examinations; (2) increasing success probabilities; and (3) changing success probabilities per test by learning. These provide the framework for examining exploration risk. First, the number of prospects examined is increased, such as by joint venturing, thereby reducing chance of gambler's ruin. Second, success probability is increased by exploring for deposit types more likely to be economic, such as those with a high proportion of world-class deposits. For example, in looking for 100+ ton (〉3 million oz) Au deposits, porphyry Cu-Au, or epithermal quartz alunite Au types require examining fewer deposits than Comstock epithermal vein and most other deposit types. For porphyry copper exploration, a strong positive relationship between area of sulfide minerals and deposits' contained Cu can be used to reduce exploration risk by only examining large sulfide systems. In some situations, success probabilities can be increased by examining certain geologic environments. Only 8% of kuroko massive sulfide deposits are world class, but success chances can be increased to about 15% by looking in settings containing sediments and rhyolitic rocks. It is possible to reduce risk of loss during mining by sequentially developing and expanding a mine—thus reducing capital exposed at early stages and reducing present value of risked capital. Because this strategy is easier to apply in some deposit types than in others, the strategy can affect deposit types sought. Third, risk is reduced by using prior information and by changing the independence of trials assumption, that is, by learning. Bayes' formula is used to change the probability of existence of the deposit sought on the basis of successive exploration stages. Perhaps the most important way to reduce exploration risk is to employ personnel with the appropriate experience and yet who are learning.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1021838618750
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  • 6
    Electronic Resource
    Electronic Resource
    A Comparison of the Weights-of-Evidence Method and Probabilistic Neural Networks (1999)
    Springer
    Natural resources research 8 (1999), S. 287-298 
    Details
    ISSN: 1573-8981
    Keywords: Geographic information systems (GIS) ; resource assessment ; weights of evidence ; Bayesian methods
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences
    Notes: Abstract The need to integrate large quantities of digital geoscience information to classify locations as mineral deposits or nondeposits has been met by the weights-of-evidence method in many situations. Widespread selection of this method may be more the result of its ease of use and interpretation rather than comparisons with alternative methods. A comparison of the weights-of-evidence method to probabilistic neural networks is performed here with data from Chisel Lake-Andeson Lake, Manitoba, Canada. Each method is designed to estimate the probability of belonging to learned classes where the estimated probabilities are used to classify the unknowns. Using these data, significantly lower classification error rates were observed for the neural network, not only when test and training data were the same (0.02 versus 23%), but also when validation data, not used in any training, were used to test the efficiency of classification (0.7 versus 17%). Despite these data containing too few deposits, these tests of this set of data demonstrate the neural network's ability at making unbiased probability estimates and lower error rates when measured by number of polygons or by the area of land misclassified. For both methods, independent validation tests are required to ensure that estimates are representative of real-world results. Results from the weights-of-evidence method demonstrate a strong bias where most errors are barren areas misclassified as deposits. The weights-of-evidence method is based on Bayes rule, which requires independent variables in order to make unbiased estimates. The chi-square test for independence indicates no significant correlations among the variables in the Chisel Lake–Andeson Lake data. However, the expected number of deposits test clearly demonstrates that these data violate the independence assumption. Other, independent simulations with three variables show that using variables with correlations of 1.0 can double the expected number of deposits as can correlations of −1.0. Studies done in the 1970s on methods that use Bayes rule show that moderate correlations among attributes seriously affect estimates and even small correlations lead to increases in misclassifications. Adverse effects have been observed with small to moderate correlations when only six to eight variables were used. Consistent evidence of upward biased probability estimates from multivariate methods founded on Bayes rule must be of considerable concern to institutions and governmental agencies where unbiased estimates are required. In addition to increasing the misclassification rate, biased probability estimates make classification into deposit and nondeposit classes an arbitrary subjective decision. The probabilistic neural network has no problem dealing with correlated variables—its performance depends strongly on having a thoroughly representative training set. Probabilistic neural networks or logistic regression should receive serious consideration where unbiased estimates are required. The weights-of-evidence method would serve to estimate thresholds between anomalies and background and for exploratory data analysis.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1021606417010
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