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  • Articles  (4)
  • economic geology  (2)
  • mathematics  (2)
  • 2010-2014
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  • 1970-1974  (4)
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  • 1974  (4)
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  • Articles  (4)
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  • 2010-2014
  • 1990-1994
  • 1980-1984
  • 1970-1974  (4)
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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Mathematical geology 6 (1974), S. 33-45 
    ISSN: 1573-8868
    Keywords: inversion of data ; mathematics ; numerical analysis ; regression analysis ; geophysics ; petroleum ; well logging
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Mathematics
    Notes: Abstract Conventional methods of analyzing sonic log data do not always yield accurate information on each velocity segment of a well. It is shown here that the velocity-depth parameters and the sections of approximately constant velocity may be more precisely defined by using an exponential spline to model the data.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Mathematical geology 6 (1974), S. 107-115 
    ISSN: 1573-8868
    Keywords: functional equations ; mathematics ; numerical analysis ; rock description ; sedimentology ; stratigraphy
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Mathematics
    Notes: Abstract Equations describing ten microfacies of Ste. Genevieve Limestone established previously on qualitative analysis of petrographic features are derived here in terms of measured lithic parameters. Mean values of each lithic parameter for each facies comprise independent variables with eigenvalues from discriminant analysis serving as coefficients for quasifunctional equations. This provides a unique quantitative expression for each qualitative microfacies, the dependent variable. Although these equations are not truly functional equations, they do provide explicit description of Ste. Genevieve Limestone facies. Quasifunctional equations form a necessary first step toward establishment of true functional equations describing unit lithology as functions of depositional parameters. Functional equations for lithic units offer a potential for an ultimate unification of classifications for all geologic materials. An immediate value of these quasifunctional equations resides in their explicit description and identification of subjective lithic classifications (facies), their utilization in comparative studies of lithic components in one or more stratigraphie units, their facility for an objective and automated digital reduction and graphic presentation of data, and their invitation for more careful examination and critical evaluation of natural relationships.
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  • 3
    Electronic Resource
    Electronic Resource
    Springer
    Mathematical geology 6 (1974), S. 47-57 
    ISSN: 1573-8868
    Keywords: data processing ; multivariate statistics ; economic geology
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Mathematics
    Notes: Abstract Multivariate statistical methods may be used to enhance large data bases by predicting missing information and verifying unchecked data vectors. In addition, new techniques may be used for exploratory purposes to investigate the interrelationships between variables. The techniques are being applied successfully in coal mining.
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  • 4
    Electronic Resource
    Electronic Resource
    Springer
    Mathematical geology 6 (1974), S. 219-233 
    ISSN: 1573-8868
    Keywords: sampling ; simulation ; statistics ; assay weighting ; economic geology ; geochemistry ; mining ; ore-reserve calculations
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Mathematics
    Notes: Abstract Weighting of vein assays is desirable where assays represent variable vein widths and variable distances along the vein. The method of weighting used is important in ore-reserve calculations and other investigations. Two possible methods are considered here. The first method, commonly applied in mining, assumes in effect linear variation of assay-width products between assay points. The second method, developed in this paper, assumes linear variation of assays and widths separately. The weighted average assay in an interval dby the second method (aw1, 2)is given by the expression $$a_{w1,2} = V/[(w_1 + w_2 )/2]$$ where $$\begin{gathered} v = (1/3)(\Delta a)(\Delta w)d + (1/2)(\Delta a)w_1 d + (1/2)(\Delta w)a_1 d + a_1 w_1 d \hfill \\ \Delta a = a_2 - a_1 {\text{,}}\Delta w = w_2 - w_1 \hfill \\ \end{gathered} $$ a1, w1 are assay and width at point 1, a2, w2 are assay and width at point 2,and dis the distance between points 1and 2.Statistical testing of the two methods, using 8,913 pairs of copper assay-width data from the Belmont mine area at Butte, suggests the following: within major veins or within veins which show near-linear width variations, the second method may be used with the expectation that the average grade calculated will more closely represent the actual average grade than if the first method were used.
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