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.
E Ghadiri-Sufi; M Yousefi
Abstract
Integration of different kinds of data is a useful method which can be used in exploration studies to determine the location of undiscovered hidden or outcropping mineral deposits in an area under prospecting. The results obtained by considering all data sources and their relations have better reliability. ...
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Integration of different kinds of data is a useful method which can be used in exploration studies to determine the location of undiscovered hidden or outcropping mineral deposits in an area under prospecting. The results obtained by considering all data sources and their relations have better reliability. In this regard, modeling the mineral potential is commonly used to combine the results achieved by different exploration methods in order to generate target areas. In this research, surface exploration data over the 1:100000 geological map of the Manheshan quadrangle (Zanjan Province) were integrated by a new data-driven and knowledge-driven fuzzy approach to determine areas of high mineralization potential. Various dataset used in this study include geological map, geochemical stream sediment data, and fault distribution map. In this new approach, evidential geochemical and fault density maps were weighted ad produced without the use of any analyst’s subjective judgment and location of known indices. In contrast, the evidential weighted geological map was produced considering the analyst’s subjective judgment. The weighted data layers produced by fuzzy logic were then integrated using OR and Gamma fuzzy logic operators. Finally, known mineral occurrences (Zn-Pb) in the Mahneshan area were used to evaluate the generated models. Results show that the generated target areas have a good spatial coincidence with the position of known mineral occurrences.
M Yousefi; A Kamkar-Rouhani; M Alipoor
Abstract
Study of geochemical stream sediments is an effective method for prospecting mineral deposits especially in preliminary exploration stages. In this regard, generally multivariate analysis, for example factor analysis, is used to elicit an indicator component of the mineralization type sought. There are ...
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Study of geochemical stream sediments is an effective method for prospecting mineral deposits especially in preliminary exploration stages. In this regard, generally multivariate analysis, for example factor analysis, is used to elicit an indicator component of the mineralization type sought. There are still several problems with regard to usage of factor analysis that have been discussed in several published papers. In this research, we have used the stepwise factor analysis, which is a new approach to create geochemical stream sediment evidential map. Using the stepwise factor analysis, we have succeeded in recognizing more effective indicator components, increasing the intensity of geochemical halos and explaining higher percentage of the total variance of the data. We have also improved the prediction rate of mineral occurrences and consequently, increasing the exploration success. In this research, we have successfully used the stepwise factor analysis to generate enhanced geochemical evidential map for prospecting two different deposit-types in two different areas of Iran for case studies. Using the stepwise factor analysis, the total variance relevant to the indicator component of porphyry copper mineralization has been increased from 13.43 to 20.05, and the prediction rate of mineral occurrences has been increased from 34.37% to 46.8% for cumulative percentile of 95% frequency. Hence, the exploration success has been increased up to 13% at least in the study area. Furthermore, using stepwise factor analysis, there are much simultaneous present of geochemical anomalies and geological indicative features.
