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Abstract

The article describes the influence of anomalous values and local variability on the structure of variability and the estimation of deposit parameters. The research was carried out using statistical and geostatistical methods based on the Pb accumulation index in the shale series in part of the Cu-Ag ore deposit, LGCD (Lubin-Głogów Copper District). The authors recommend the use of a geostatistical tool, the so-called semivariogram cloud to determine the anomalous values. Anomalous values determined by the geostatistical method and removed from the dataset have resulted in a significant reduction of the relative variability of data, which is still very large in the case of the analyzed parameter or parameters with similar statistical features such as extreme variability and strongly asymmetric distribution. Calculations of the resources of this element can be treated only as estimates and formally classified to category D. The hypothetical assumption of the absence of sampling errors, resulting in a decrease in the magnitude of local variation, leads to a certain reduction of the median error of resource estimates. However, they are still high (> 35%). This is due to the large natural variability of the accumulation index of Pb on the local observation scale. The current method for collecting samples from mine workings of the Cu-Ag deposits in the Lubin-Głogów Copper District (LGCD), aimed at the proper assessment of copper resources, the Cu content, and at estimating the quality of copper output, makes it impossible to achieve an accuracy of estimates of Pb resources similar to that obtained for the main metal. Theoretically, this effect can be achieved by a strong concentration of the sample collection points and thanks to a multiple increase in the samples weight; this, however, is unrealistic for both economic and organizational reasons. It is therefore to be expected that the assessment of Pb resources and other accompanying elements of similar statistical features (e.g. As), located in parts of the deposit where mining activities are to be carried out, will be subject to significant errors.
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Abstract

The correlation-regression method, as one of the indirect sampling methods, is only sporadically used in geological and mining activities. Theoretically, it should be particularly useful for predicting the content of some chemical components in limestone and marl deposits due to the correlation between them. The results of simple and multiple correlation and regression analysis for 5 selected components (CaO, SiO2, Al2O3, MgO, and SO3), determined in samples from exploratory boreholes and blast holes carried out in the Barcin-Piechcin-Pakość deposit, are presented in the article. The determination coefficients were used as a measure of the correlation power and the quality of the regression models. A very strong linear correlation between CaO and SiO2 content and strong linear correlations between CaO and Al2O3 and SiO2 with Al2O3 have been found. The correlation relationships of the remaining pairs of oxides are weak or very weak and do not provide a basis for prediction of their content based on regression models binding them with the content of other components. The use of nonlinear models for these pairs of oxides results in only a slight improvement in the quality of regression, insignificant from a practical point of view. The application of multiple regression models, linking the content of the mentioned components (with the exception of CaO), leads to similar conclusions. Compared to the determination coefficients of a simple linear correlation, a strong increase in determination coefficients obtained in two cases was found to be artificial and caused by a correlation between the content of the selected components acting as independent variables. From the geological and mining point of view, the results of the analysis indicate the possibility of a fully reliable prediction of SiO2 content and the limited reliability of the Al2O3 content prediction when the CaO content is determined using simple linear regression models.
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