Multivariate statistical analysis and 3D-coupled Markov chain modeling approach for the prediction of subsurface heterogeneity of contaminated soil management in abandoned Guryong Mine Tailings, Korea

Various geological materials on the ground surface can be natural or artificial sources of pollutions. The spatial distribution of tailings is required to investigate the geological material pollutions. The objectives of this study were to determine the main factors influencing tailing zonations using a factor analysis, to determine the zonation of tailings with a cluster analysis, and to simulate zonations with three-dimensional coupled Markov chain (3D-CMC) modeling. The database was composed of 12 excavated exploratory holes in the Guryong mine tailings, for which there were analytical data covering the physical, chemical, and mineralogical aspects. The principal component analysis indicated that the tailing composition was mainly affected by three factors out of 21 variables: pH, cation exchange capacity, and mineral composition. Based on these main factors, the tailings were classified into five groups using a cluster analysis. Group I was approximately 50 cm deep from surface and had secondary gypsum (CaSO4·2H2O) and jarosite (KFe3(SO4)2(OH)6). Group II had low pH values caused by strong pyrite oxidation and the greatest amounts of the secondary minerals. In group III and IV, the quantity of the secondary minerals decreased. Group V was characterized by primary calcite (CaCO3) composition. These results were applied to the CMC modeling, and the quantitative 3D distribution of tailing was verified. For the cost-saving prediction of subsurface heterogeneity, 3D-CMC modeling was executed using the selected eight holes data among twelve holes. The unknown four holes, GS3, GS6, GS8 and GS11, are identified as 89.7, 88.6, 80.7 and 81.1 %, respectively. They are recognized as 85.0 % of the total zonation. The zonation method of tailings executed in this study can be utilized in predicting the 3D distribution of the pollution factor. This may be a useful and economical method to identify the environmentally hazardous materials in underground systems.