Study of diffraction data sets using factor analysis: a new technique for comparing mineralogical and geochemical data and rapid diagnostics of the mineral composition of large collections of rock samples

Fomina, Ekaterina; Kozlov, Evgeniy; Ivashevskaja, Svetlana

doi:10.1017/s0885715619000435

Cited by 5 publications

(6 citation statements)

References 28 publications

(35 reference statements)

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“…On the factor plane determined by these factors, the figurative points of the elements shape the fields that reflect the chemical specificity of the Petyayan-Vara carbonatites (Figure 18). In order to provide a mineralogical meaning to the geochemical variables, the technique outlined by [118] was used. As demonstrated by this study, in most cases, each component is concentrated primarily in one or two minerals.…”

Section: Geochemistrymentioning

confidence: 99%

“…The colored fields distinguish the groups of elements associated with certain minerals from one or other mineral assemblages. Illustration is based on the work of [118].…”

Section: Geochemistrymentioning

confidence: 99%

“…The projection of samples onto this factor plane yielded the first classification of carbonatites. Thus, the statistical data processing of a combination of chemical compositions and X-ray diffraction patterns provides the following advantages: (1) simplified determination of minerals, (2) revealing the effect of an identified mineral on the distribution of each element, and (3) tracing the connection between results of the statistical processing of geochemical data and minerals/mineral assemblages ( Figure 18, the relationship between elements and minerals) [118]. The detailed petrographic study presented in this work fully confirmed the results obtained using statistical methods.…”

Section: Geochemistrymentioning

confidence: 99%

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The Petyayan-Vara Carbonatite-Hosted Rare Earth Deposit (Vuoriyarvi, NW Russia): Mineralogy and Geochemistry

et al. 2020

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The Vuoriyarvi Devonian carbonatite–ijolite–pyroxenite–olivinite complex comprises several carbonatite fields: Neske Vara, Tukhta-Vara, and Petyayan-Vara. The most common carbonatites in the Tukhta-Vara and Neske-Vara fields are calciocarbonatites, which host several P, Fe, Nb, and Ta deposits. This paper focuses on the Petyayan-Vara field, in which the primary magmatic carbonatites are magnesian. The least altered magnesiocarbonatites are composed of dolomite with burbankite and are rich in REE (up to 2.0 wt. %), Sr (up to 1.2 wt. %), and Ba (up to 0.8 wt. %). These carbonatites underwent several stages of metasomatism. Each metasomatic event produced a new rock type with specific mineralization. The introduction of K, Si, Al, Fe, Ti, and Nb by a F-rich fluid (or fluid-saturated melt) resulted in the formation of high-Ti magnesiocarbonatites and silicocarbonatites, composed of dolomite, microcline, Ti-rich phlogopite, and Fe–Ti oxides. Alteration by a phosphate–fluoride fluid caused the crystallization of apatite in the carbonatites. A sulfate-rich Ba–Sr–rare-earth elements (REE) fluid (probably brine-melt) promoted the massive precipitation of ancylite and baryte and, to a lesser extent, strontianite, bastnäsite, and synchysite. Varieties of carbonatite that contain the highest concentrations of REE are ancylite-dominant. The influence of sulfate-rich Ba-Sr-REE fluid on the apatite-bearing rocks resulted in the dissolution and reprecipitation of apatite in situ. The newly formed apatite generation is rich in HREE, Sr, and S. During late-stage transformations, breccias of magnesiocarbonatites with quartz-bastnäsite matrixes were formed. Simultaneously, strontianite, quartz, calcite, monazite, HREE-rich thorite, and Fe-hydroxides were deposited. Breccias with quartz-bastnäsite matrix are poorer in REE (up to 4.5 wt. % total REE) than the ancylite-dominant rocks (up to 11 wt. % total REE).

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Section: Geochemistrymentioning

confidence: 99%

“…The colored fields distinguish the groups of elements associated with certain minerals from one or other mineral assemblages. Illustration is based on the work of [118].…”

Section: Geochemistrymentioning

confidence: 99%

Section: Geochemistrymentioning

confidence: 99%

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The Petyayan-Vara Carbonatite-Hosted Rare Earth Deposit (Vuoriyarvi, NW Russia): Mineralogy and Geochemistry

et al. 2020

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“…This work is based on the results of the factor analysis of XRPD and XRF data for Kontozero rocks, carried out according to the method described in [17]. XRPD data were also the basis for semiquantitative phase analysis, performed using the QualX v. 2.24 program (Institute of Crystallography (IC)-CNR, Bari, Italy; [30,31]), and quantitative phase analysis, conducted using the MAUD v. 2.94 program (University of Trento, Trento, Italy; [32,33]).…”

Section: Data Processingmentioning

confidence: 99%

“…The complex mineral composition of Kontozero rocks does not allow for the use of a single algorithm for normative calculation throughout the entire sample set, which consists of almost 200 samples. In this regard, we applied an original technique based on the statistical processing of XRPD data using factor analysis [17]. Like modal XRPD, this technique deals with data of the bulk sample (i.e., on a significant volume of rock).…”

Section: Introductionmentioning

confidence: 99%

Factor Analysis of XRF and XRPD Data on the Example of the Rocks of the Kontozero Carbonatite Complex (NW Russia). Part II: Geological Interpretation

2020

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Numerical comparison of mineralogical and geochemical data, which is required in a variety of geological applications, is a challenging task, especially when analyzing extensive sample collections. Herein, we apply factor analysis (FA) to a collection of 198 diffraction patterns of bulk rock samples from the Kontozero carbonatite complex. The mineralogical information hidden in the X-ray powder diffraction (XRPD) data is thereby squeezed down to a set of two dozen variables represented by factor scores (FS). The values of these FSs show a functional relationship with the contents of the minerals composing the rocks. Therefore, factor scores can be considered as a beneficial tool for rapid qualitative and semiquantitative analysis of the mineral composition of rocks. Supplementing principal component analysis (PCA) with FSs as independent variables characterizing the mineral content of rocks allows for the numerical comparison of mineralogical and geochemical data. By PCA, we reveal the main trends in the mineralogical and geochemical evolution of the investigated rocks of the Kontozero complex. Furthermore, the results are obtained in the very first stages of the research. This fact elucidates the potential use of the proposed technique in geological studies and mining.

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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)

Kozlov

Fomina

2020

IOP Conf. Ser.: Earth Environ. Sci.

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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.

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Study of diffraction data sets using factor analysis: a new technique for comparing mineralogical and geochemical data and rapid diagnostics of the mineral composition of large collections of rock samples

Cited by 5 publications

References 28 publications

The Petyayan-Vara Carbonatite-Hosted Rare Earth Deposit (Vuoriyarvi, NW Russia): Mineralogy and Geochemistry

The Petyayan-Vara Carbonatite-Hosted Rare Earth Deposit (Vuoriyarvi, NW Russia): Mineralogy and Geochemistry

Factor Analysis of XRF and XRPD Data on the Example of the Rocks of the Kontozero Carbonatite Complex (NW Russia). Part II: Geological Interpretation

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)

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