Geological Environment and Engineering
E. Ghadiri Soufi; S. Soltani Mohammadi; M. Yousefi; A. Aalianvari
Abstract
In mining areas, assessing toxic elements (e.g., arsenic) contamination in the soil and stream deposits is a critical issue. It is because mining activities release dangerous elements that enter the environment. In this paper, for modeling the spatial distribution of arsenic contamination in Sarduiyeh-Baft ...
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In mining areas, assessing toxic elements (e.g., arsenic) contamination in the soil and stream deposits is a critical issue. It is because mining activities release dangerous elements that enter the environment. In this paper, for modeling the spatial distribution of arsenic contamination in Sarduiyeh-Baft area, in Kerman Province, across an area of ca. 5000 km2, 1804 stream sediment samples were collected and analyzed. The recommended standard limit for arsenic in soil is 20 ppm, so samples showing arsenic concentration >20 ppm are contaminated samples, which need land reform processes. However, since the number of collected samples is limited, indicator Kriging method was used to identify the possibility of contamination. In the study area, there are 32 known occurrences of porphyry-Cu deposits. Thus, in order to estimate the arsenic contamination in the unsampled locations, indicator kriging method was used. The results indicate arsenic contaminations in north and northwest parts of the study area, which could be occurred by mining of the porphyry-Cu deposits. However, the results show that there is no arsenic contamination in the eastern part although there are several mining sites with high activities.
M. Jalali; Gh. R. Rahimipour; M. R. Dianati; M. Taghvayinejad
Abstract
Facing to unsatisfied results in grade-tonnage estimation especially in dynamic programming is always being a great problem in mining revenue operation. If the problem is to estimate a grade point only, linear kriging estimators can show accurate results. But if target is to achieve to probability distribution ...
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Facing to unsatisfied results in grade-tonnage estimation especially in dynamic programming is always being a great problem in mining revenue operation. If the problem is to estimate a grade point only, linear kriging estimators can show accurate results. But if target is to achieve to probability distribution estimation of a spatial zone for considering ore-waste block mixing control, using linear kriging methods with minimum estimation variance can’t be applied for an appropriate results. Most of probability function is nonlinear, therefore estimation of these function by nonlinear estimator showed an accurate results. Main target of this paper is to achieve to the most exact ore-waste boundaries in 2462.5 benchmark of Sarcheshmeh copper mine using indicator kriging (IK) as nonlinear estimator and comparing with ordinary kriging (OK) as linear estimator to evaluate validity of linear estimator. Because of OK dependency to normal distribution data for a given minimum estimation variance, utility data have been separated to ore and waste group using geological map and mine-sight. After this separation ore groups was approached to normal distribution and OK estimator can be applied for estimation. 25629 blocks were estimated by these two kinds of estimators. IK estimator classified 2905 blocks of total blocks as waste blocks, but OK estimator showed 2475 blocks as waste block. Finally IK estimator recommended as best estimator for ore and waste block separation and after this process using ordinary kriging estimator almost gave more confident estimation in ore blocks grade control process.
