Application of Structural Equation Modeling in Geochemical Data Analysis

In order to find a combination of geochemical elements reflecting the abnormal mineralization, this paper provides a method of structural equation modeling (SEM) for geochemical data processing based on principal component analysis (PCA). Different from the PCA, the structural equation model takes the favorable fitness with studying object as the criterion to determine the optimal solution, through which the new component will be determined; it is a combination of factor analysis and path analysis. Therefore, the component determined by the structural model is not necessarily the one with the largest variability, but the one closest to the object of study, which can thus better reflect the research target. This study not only describes the principle of structural equation modeling, but also makes use of it in the geochemical data analysis experiment. The geochemical data is measured from lake sediments samples obtained from the Southwest Nova Scotia, Canada, a model of geochemical elements related to the hydrothermal fluid gold mine is established. The spatial distribution law of the composite variables given by the structural model, and the relation between those variables and the gold deposits as well, is studied. A comparison with those of the PCA results shows that the factor variables related to the gold mine computed by the structural equation modeling are highly correlated to the space of the gold deposits, and they can also better predict the gold deposits.