Geological Interpretation of the Results of Factor Analysis of XRF- and XRD-Data on Carbonatites and Aluminosilicate Rocks of the Kontozero Alkaline Complex (Kola Peninsula, NW Russia)

• Published in: IOP Conference Series: Earth and Environmental Science
• Main Authors: Kozlov, Evgeniy, Fomina, Ekaterina
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: IOP Publishing 2020
• Subjects: Kola Peninsula; Fomina; kola peninsula
• Online Access: http://dx.doi.org/10.1088/1755-1315/609/1/012049; https://iopscience.iop.org/article/10.1088/1755-1315/609/1/012049/pdf; https://iopscience.iop.org/article/10.1088/1755-1315/609/1/012049

Abstract We applied factor analysis (FA) for statistical comparison of XRF and XRD data on 198 carbonatite and aluminosilicate rock samples of the Kontozero Devonian paleovolcano. This method helps to identify main, secondary, and some accessory minerals, as well as to estimate the approximate contents of these minerals and to assess their contribution on the distribution of major and trace elements (Fomina et al., 2019). With the view of control purposes, qualitative and semiquantitative analysis of diffraction data were performed. Several dozen factors quickly yielded to mineralogical interpretation. For the studied samples, a functional relationship was established between the calculated mineral content and the score of the factor (FS) corresponding to the mineral. Thus, the FS can serve as a handy tool for assessing the contents of minerals. Thereby we visualise the distribution of minerals in the space of the studied complex. We also found out which mineral phases are ubiquitous, and which present only in certain parts of the section or occur sporadically. These data allowed us to make a mineralogical interpretation of the results of FA, performed solely for the geochemical variables. The main trends in the mineralogical and geochemical evolution of carbonatite and aluminosilicate rocks of the Kontozero Devonian paleovolcano were thus determined and statistically substantiated. All these results were obtained in a short time at the earliest stages of the research. A “blind” statistical method guarantees the independence of the results on a priori hypothesis of the researcher. The time-consuming classical techniques (e.g., petrography, mineralogical studies, and quantitative analysis of diffraction data) verified the geological model described by statistics.