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The Methodology for Extraction of Geochemical Anomalies, Using Regression Formula: an Example from a Granitic Body in Gyeonggi Province  

황상기 (배재대학교 토목환경공학과)
신성천 (한국지질자원연구원)
염승준 (고려대학교 지구환경과학과)
문상원 ((주)일신하이텍)
Publication Information
Economic and Environmental Geology / v.35, no.2, 2002 , pp. 137-147 More about this Journal
Abstract
Natural geological and environmental processes reflect to element abundances in geological materials on the surface. This study aims to elucidate a possibility of geostatistical application to differentiate geochemical anomalies affected by anthropogenic and geogenic factors. A regional geochemical map was produced using 'inverse distance weight interpolation' method for analytical results of stream sediments «150 11m) which were collected from 2,290 first- to second-order streams over the whole Gyeonggi Province. The Jurassic granitic batholith in the southeastern province was selected as a target for the geostatistical examination. Factor analysis was conducted using 22 elements for stream sediments from 445 drainage basins over the granitic body. Co, Cr, Sc, MgO, Fe$_{2}$O$_{3}$, V, and Ni were grouped with high correlation coefficients and the depletion of the components may reflect the whole-rock chemistry of the granite. Regression analysis was done using Co, Cr, and Sc as dependent variables and other six components as independent variables, and the results were drawn as maps. The maps acquired generally show quite similar distribution patterns with those of concentrations of each variable. The similarity in the spatial patterns between the two maps indicates that the application of regression statistics can be valid for the interpretation of regional geochemical data. However, some components show local discrepancies which may be influenced by secondary factors regardless of the basement lithology. The regression analysis may be effective in extracting local geochemical anomalies which may reflect rather anthropogenic pollutions than geogenic influences.
Keywords
geochemical map; stream sediments; granite; regression analysis; geochemical anomaly;
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