Automated mineralogy, or more correctly automated quantitative mineralogy (AQM) was developed in the 1980s to analyse the mineralogy, chemistry, and microstructures of mineral ores, fly ashes, and sediments with an energy dispersive spectrometry (EDX) detector mounted to a scanning electron microscope (SEM) [1
]. This developed from a range of automated particle analysis procedures into software platforms, e.g., Qemscan, MLA, ZEISS Mineralogic, AMICS, or TIMA-X, dedicated to multiphase materials in a wide range of research fields including, but not limited to, forensic sciences, archaeometry, oil reservoir geology, urban mining, and material sciences [4
]. Within geosciences, AQM is most widely used on ore minerals (for modal mineralogy, liberation, association, etc.) [13
] and oil reservoir rocks (e.g., to describe mineralogy and porosity or provenance) [16
]. However, other areas within geosciences have so far received less attention, (but see e.g., [19
]). Apart from AQM with SEM-EDX systems, AQM can also be applied with wavelength dispersive spectrometry (WDX), micro-energy-dispersive X-ray fluorescence, laser-induced breakdown spectroscopy and hyperspectral mineral analyses [22
Many AQM systems, like Qemscan, MLA, or TIMA-X, apply spectrum-matching for the classification of the mineralogy of the samples. This AQM technique is based on generating unquantified EDX spectra in user-defined steps or specific spots or a raster on the sample surface. The EDX spectra are not matrix-corrected or quantified but matched against a library of known referenced EDX spectra (based on analyses of standards, or calculated from mineral formulas) [5
]. The development of this library typically requires an extensive workflow on mineral spectrum validation using electron microprobe validation or pre-validated mineral standards.
AQM in geosciences (outside the mining industry) has mainly been used as a qualitative instrument, rather than a laboratory tool for quantitative measurements on mineralogy, chemistry, and morphology. There are several reasons for this: It takes a large effort to obtain reproducible data between AQM systems. This is caused by a lack of precise chemical data for most AQM systems, and for complex and variable mineral systems reduces the reliability and therefore the range of application of these AQM techniques in these research environments. Resulting from this is the fact that each mineral list for each spectrum-matching analysis is as precise as the geological and mineralogical knowledge of the operator. To analyze the same sample in a different AQM system, or under different analytical conditions requires the development of a new mineral library. Furthermore, to produce valuable petrological results, good quality data on textures and on minerals are needed, preferably with the chemical data derived from exactly the minerals that are visualized.
Here, we will present examples where the latest AQM solutions, such as ZEISS Mineralogic can provide new insights into metamorphic textures using advanced visualization and quantification methods. To our knowledge, no study with a metamorphic petrology focus exist based on ZEISS Mineralogic software, and only few studies based on other automated mineralogy platforms exist with metamorphic petrology as a main focus outside a mining and exploration setting [27
]. AQM serves as an ideal tool to visualize metamorphic textures and to simultaneously quantify the mineralogy, chemistry, and grain properties of these textures. The applied software includes the possibility to obtain precise element chemistry and therefore to analyze minor-element contributions to variations in individual mineral compositions and to measure grain properties within multi-phase composites; both features are of interest for metamorphic petrologists. The ZEISS Mineralogic software also allows to exchange mineral lists between Mineralogic users or samples, or to change acceleration voltages without the need to create new mineral lists. These advanced chemical, mineralogical, and textural properties are applied here to visualize mineralogy and textures in a new way.
The examples used in this study are thin sections derived from the Geological Survey of Denmark and Greenland’s (GEUS) sample collection of rocks from southern West-Greenland (Figure 1
). This region comprises Archean basement rocks that are part of the North Atlantic Craton. The majority of the rocks are grey and brown tonalite–trondhjemite–granodiorite (TTG) orthogneisses, intruded by granitic and granodioritic bodies, and by TTG-composition as well as mafic sheets. Intercalated in the orthogneisses are enclaves of supracrustal rocks including amphibolites, mafic granulites and mica-schists, but also lenses of ultramafic rocks. The area also includes the well-known Meso-Archean Fiskenæsset complex (a leucogabbro–anorthosite intrusive complex). The rocks of the Fiskenæsset complex intruded 2.97–2.95 Ga in amphibolites that already were deformed and metamorphosed by the time of the intrusion. The formation age of the amphibolites is estimated to be 2.90 Ga. [30
At least three deformation phases affected the rocks in the area, where deformation was accommodated mainly by folding, but also by thrusting at a meter- to kilometer-scale. The region consists of several blocks or terrane that assembled into a larger unit at the latest part of the Meso-Archean age. The first regionally recognized deformation phase in the rocks can be observed in finely foliated isoclinally folded units (mica-schists, amphibolites, leucogabbroic rocks of the Fiskenæsset complex). The main deformation event metamorphosed the rocks in the area at amphibolite facies and granulite facies conditions. The effects of this main folding phase were overprinted by a later folding and thrusting phase at amphibolite facies conditions, before minor retrogressive overprint at greenschist facies conditions localized around late fault and shear zones. Uplift and erosion brought the rocks to the Earth’s surface [30
]. The tectonometamorphic processes affecting the region developed a range of metamorphic features that are used as examples to highlight the visualization and quantification of metamorphic textures by AQM applying the Mineralogic software.
The five examples of application above show a small set of the vast range of possibilities for modern AQM systems, like Mineralogic, where visualization of the data and the generation of chemical, mineralogical and morphological data are combined. Traditionally, mineral compositions in metamorphic rocks are quantified with optical microscopy or microprobe analyses, while metamorphic textures are investigated with optical microscopy and often unquantified or, mainly in case of deformed samples, quantified by electron back-scatted diffraction. By applying AQM, visualization and quantification can be combined in one tool and executed on the same minerals. The AQM software is creating a false-coloured mineral map, while simultaneously measuring grain morphology and chemistry. The false-coloured map resembles the optical microscope mineral display of metamorphic textures, but also forms the basis to gain quantitative data for the sample. Compared to the microprobe, AQM offers mineral maps and not only element maps. This is combined with the higher analytical speed for EDX analyses compared to the wavelength dispersive spectrometry analyses on the microprobe. The diversity and flexibility in the chemical, textural and mineralogic visualization is demonstrated in this paper as a means of contextualization of what can initially be complex data sets.
AQM is often regarded as a slow analytical tool. However, depending on the information required, analytical speed can be adapted to the optimal combination of mineralochemical precision, map-quality and time. An area of 1.5 × 2 cm can be analysed in 83 min at a 40 µm step size, and provide almost identical mineral concentrations, when compared to runs with smaller step sizes and longer analysis times (Table 3
; Figure 3
and Figure 5
). A time-saving approach could be to first run a large (here 20 µm) step size line-scan or Mineral map (here 40 µm step size) on the selected sample, then build a solid mineral list and run selected area(s) with a smaller step size to obtain detailed information on specific features e.g., reaction rims. Further, these investigations indirectly showed that the general reproducibility of the data, even while generated at different step-sizes (pixel sizes), is good.
Within the Mineralogic software platform, it is also possible to analyze samples with different acceleration voltages, without changing the mineral list. Samples 521106 and 521111 originate from the same area, were analyzed at 20 and 15 kV respectively, and were recalculated with the same mineral list without any adaptions (Table 1
The garnet and ruby examples outline unique capabilities the Mineralogic software is able to access when compared to other AQM platforms. Because Mineralogic’s EDX software is matrix-correcting and quantifying every spectrum for every pixel in the mineral maps and for each spot or feature scan for every epoxy-embedded particle, the exact chemistry is known with the same precision as for EDX point analyses. Elements can be easily detected with concentrations down to ca. 0.5 wt%, and major and minor elements can be analysed with an accuracy of 1–2 wt%. This is a large improvement compared to spectrum matching AQM platforms, which are not able to detect elements in concentrations of less than 5% [22
]. Also, by obtaining the chemistry for every pixel makes the addition of new minerals to the mineral library more certain, and easily adaptable to changes in composition due to slightly different metamorphic conditions (e.g., Mg/Fe ratio dependence on pressure and especially temperature during metamorphism). The ability to measure detailed chemistry of the minerals for every pixel of the mineral maps has been applied to make element concentration maps (Figure 7
c–e) and element ratio maps (Figure 10
c). But the chemical data can also be exported after analysis, e.g., to obtain a bulk geochemical assay or to obtain raw data for a garnet XMg–XFeMn–XCa ternary diagram (Figure 8
). A comparison with whole rock geochemical data and EDS generated data with Mineralogic on a homogeneous sample (Table 2
) shows a good agreement in the results between the two methods. The tested sample had slightly higher Ca and lower Si in the analysed thin section than the bulk rock sample and comparable results for the other elements., The discrepancy in Ca and Si probably occurred due to the presence of a calcite vein in the thin section.
The garnet example shows how the zonation into core-inner rim-outer rim could be observed by the garnet elements Fe, Ca, and Mg. These three zones in garnet were each associated with their own set of inclusion minerals, visualized in the mineral map of the sample (Figure 6
and Figure 7
a), revealing three stages of the metamorphic history of the garnet. Similarly, the Si/Al element ratio map show how silica-concentration is increasing in the reaction rims around corundum in the sample, causing the formation of sapphirine, cordierite and anorthite, each with a higher Si/Al ratio. Sapphirine, cordierite and anorthite are clearly associated with the ruby, as is visible in the mineral association map for these three minerals (Figure 9
and Figure 10
). Mineral mapping of the reaction rim showed that these are a prograde/peak metamorphic feature and not part of the retrograde reaction path [41
The chromite-bearing gabbro example and the biotite example show the power of morphochemical classifications of the minerals (Figure 11
). The ability to precisely select certain minerals and to cluster and colour-code them by morphological features is a very powerful feature in AQM software products. For the chromite-bearing leucogabbro sample it was demonstrated that the Mineralogic software can evaluate and colour-code the grain size distribution of grains in a rock. Image processing tools help to automatically separate near-touching grains (though not the entirely clustered grains). The strong preference of chromite for the hornblende layers is quantified by the mineral association data that AQM software is able to produce.
The biotite sample’s Feret analysis is an example of a monomineralic display used to illustrate a morphological criterion. Here, the most important mineral is mapped out only, and colour-coded by its mineral orientation (Figure 12
). However, the results are not only displayed, but the data for the analysed particles from the mineral map formed the basis for association data, Feret angle orientation and Feret elongation, grain size (area) as well as modal mineralogy (Figure 13
and Table 4
), showing how intensive isoclinal foliation causes mineral layering in the formation of a schistosity.
Even though most scientific investigations cannot exist without the availability of numerical data and the quantification of processes. However, the visualization of results and qualitative observations still remain very important in order to fully understand the data, and often this visualization is the key to gaining new insights through conceptualization of the data.
Here, we show that AQM software in general and especially Mineralogic software is able to generate many different ways of displaying the same sample and thereby highlighting several aspects of same sample. In the examples above we apply BSE and CL images, mineral maps, element concentration maps, element ratio maps, single mineral maps, mineral association maps on paired mineral groups, and morphology maps highlighting grain shape and orientation. Still these range of maps are only a small selection of the range ways the same sample can be displayed. As most humans are visually oriented, this displaying of results makes it easier for us to gain new insights. Visualization of the data adds context and meaning to the data tables. For example: the zonation of the garnets would not be visible without the Fe, Ca, and Mg concentration maps (Figure 7
) and the orientation of biotite with respect to the local and main orientation of the foliation was more easily visible with colour coded oriented minerals (Figure 12
The flexibility of having platform independent, acceleration voltage independent mineral classification, precise chemical measurements allowing for element concentrations and element ratio maps supplement and support the quantified data analysis, and interpretations, and on these topics ZEISS Mineralogic has proven its worth for metamorphic texture investigations, as well as a large number of other areas of investigation.