Chemical weathering indices are useful tools in characterizing weathering profiles and determining the extent of weathering. However, the predictive performance of the conventional indices is critically dependent on the composition of the unweathered parent rock. To overcome this limitation, the present paper introduces an alternative statistical empirical index of chemical weathering that is extracted by the principal component analysis (PCA) of a large dataset derived from unweathered igneous rocks and their weathering profiles. The PCA analysis yields two principal components (PC1 and PC2), which capture 39.23% and 35.17% of total variability, respectively. The extent of weathering is reflected by variation along PC1, primarily due to the loss of Na2O and CaO during weathering. In contrast, PC2 is the direction along which the projections of unweathered felsic, intermediate and mafic igneous rocks appear to be best discriminated; therefore, PC1 and PC2 represent independent latent variables that correspond to the extent of weathering and the chemistry of the unweathered parent rock. Subsequently, PC1 and PC2 were then mapped onto a ternary diagram (MFW diagram). The M and F vertices characterize mafic and felsic rock source, respectively, while the W vertex identifies the degree of weathering of these sources, independent of the chemistry of the unweathered parent rock.
The W index has a number of significant properties that are not found in conventional weathering indices. First, the W index is sensitive to chemical changes that occur during weathering because it is based on eight major oxides, whereas most conventional indices are defined by between two and four oxides. Second, the W index provides robust results even for highly weathered sesquioxide-rich samples. Third, the W index is applicable to a wide range of felsic, intermediate and mafic igneous rock types. Finally, the MFW diagram is expected to facilitate provenance analysis of sedimentary rocks by identifying their weathering trends and thereby enabling a backward estimate of the composition of the unweathered source rock. 相似文献
The principal components transformation generates, from any data array, a new set of variables—the scores of the components—characterized by a total variance exactly equal to that of the initial set. It is in this sense that the transformed variables are said to contain, preserve, or account for, the variance of the original set. The scores, however, are uncorrelated. In the course of the transformation, what becomes of the strong interdependence of variance and covariance so characteristic of closed arrays? The question seems to have attracted little attention; we are aware of no study of it in the earth sciences. Experimental work reported here shows quite clearly that the overall equivalence of variance and covariance imposed by closure, though absent from the component scores,may emerge in relations between the coefficientsof each of the lower-order components; if the raw data are complete rock analyses, the sum of all the covariances of the coefficients of such a component is negative, and is very nearly equal to the sum of all the variances in absolute value. (In all cases so far examined, the absolute value of the first sum is a little less than that of the second.) The principal components transformation provides an elegant escape from closure correlation if a petrographic problem can be restated entirely in terms of component scores, but not if a physical interpretation of the component vectors is required.相似文献