GIGeoscientific Instrumentation, Methods and Data SystemsGIGeosci. Instrum. Method. Data Syst.2193-0864Copernicus PublicationsGöttingen, Germany10.5194/gi-6-429-2017Automated mineralogy based on micro-energy-dispersive X-ray fluorescence
microscopy (µ-EDXRF) applied to plutonic rock thin sections in
comparison to a mineral liberation analyzerNikonowWilhelmwilhelm.nikonow@bgr.deRammlmairDieterhttps://orcid.org/0000-0001-7958-9337Federal Institute for Geosciences and Natural Resources (BGR),
Stilleweg 2, 30655 Hanover, GermanyWilhelm Nikonow (wilhelm.nikonow@bgr.de)18October2017624294379May20177June201731August201712September2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://gi.copernicus.org/articles/6/429/2017/gi-6-429-2017.htmlThe full text article is available as a PDF file from https://gi.copernicus.org/articles/6/429/2017/gi-6-429-2017.pdf
Recent developments in the application of micro-energy-dispersive X-ray fluorescence spectrometry mapping (µ-EDXRF)
have opened up new opportunities for fast geoscientific analyses. Acquiring
spatially resolved spectral and chemical information non-destructively for
large samples of up to 20 cm length provides valuable information for
geoscientific interpretation. Using supervised classification of the
spectral information, mineral distribution maps can be obtained. In this
work, thin sections of plutonic rocks are analyzed by µ-EDXRF and
classified using the supervised classification algorithm spectral angle
mapper (SAM). Based on the mineral distribution maps, it is possible to
obtain quantitative mineral information, i.e., to calculate the modal
mineralogy, search and locate minerals of interest, and perform image
analysis. The results are compared to automated mineralogy obtained from the
mineral liberation analyzer (MLA) of a scanning electron microscope (SEM)
and show good accordance, revealing variation resulting mostly from the
limit of spatial resolution of the µ-EDXRF instrument. Taking into
account the little time needed for sample preparation and measurement, this
method seems suitable for fast sample overviews with valuable chemical,
mineralogical and textural information. Additionally, it enables the
researcher to make better and more targeted decisions for subsequent
analyses.
Introduction
Micro-energy-dispersive X-ray fluorescence microscopy (µ-EDXRF) is a new
and versatile technique commonly used in various fields such as art (Keune
et al., 2016), archeology (Kozak et al., 2016), biology (Figueroa et
al., 2014), medicine (Wandzilak et al., 2015) and also with
increasing extent in geosciences, mostly for visualization and
quantification of element distributions (Belissont et al., 2016;
Croudace and Rothwell, 2015; Flude and Storey, 2016; Gergely et al., 2016;
Kéri et al., 2016; Lombi et al., 2011; Melcher et al., 2006; Poonoosamy
et al., 2016; Rammlmair et al., 2001, 2006; Redwan et al.,
2016). The combination of spatial and spectral information for large samples
of up to 20 cm length with almost no sample preparation opens many fields of
applications. It provides quick textural and chemical overview with
spatially resolved main and trace element information (Nikonow
and Rammlmair, 2016) at a relatively low cost and with easy operability
compared to, for example, a scanning electron microscope (SEM). Applying supervised spectral classification, the chemical data
can be used to derive mineral maps for quantitative petrography and the
modal mineralogy (Nikonow and Rammlmair, 2016), which is key
information for rock classification (Streckeisen, 1976).
Conventional automated mineralogy based on SEM and EDXRF is commonly used in
different scientific fields, especially in the mining industry. It is
applied in mineral and ore characterization or mineral processing
(Fandrich et al., 2007; Sandmann, 2015; Sutherland and Gottlieb, 1991)
and provides high-resolution data, but requires preparation of polished or
thin sections, coating, a skilled operator and from several hours up to days for
measurement. The mineral liberation analyzer (MLA) was developed in the
last decade of the last century by Gu and Napier-Munn (1997) and
dominated, together with QEMSCAN (Quantitative Evaluation of Minerals
by Scanning Electron Microscopy; Sandmann, 2015), the market in the following years. It
combines the information of SEM with several EDX detectors. It uses the
BSE (backscattered electrons) grey values to separate different grains and analyzes each identified
grain center in order to provide the modal mineralogy and a set of
quantitative geometric grain parameters (Gu, 2003).
For evaluation of modal analysis and automated mineralogy, three types of
errors have to be taken into account (Solomon, 1963): the first is the false
classification of a mineral, which is dependent on the operator-designed
mineral database (operator error); the second is the 2-D effect, since 1-D information
is extrapolated to a 2-D area (counting error); the third type of error is
the 3-D effect resulting from extrapolation of the 2-D sample surface to the
3-D properties of the sample (sampling error). Being able to quantify these
errors, the classification data becomes more reliable. Using µ-EDXRF
and spectral classification, the reliability of the classification algorithm
can be measured by the classification thresholds. Mapping the whole sample
surface will eliminate the 1-D extrapolation effect; only the extrapolation
to the 3-D volume of the sample will remain, but it can be decreased by mapping
large areas or series of rock slices or thin sections due to progress in
measurement speed and partial automatization.
Details of the M4 Tornado mapping and SEM + MLA measurement.
DeviceM4 TornadoMLA XBSEMLA GXMAPSample typeca. 25×35 mm thin section28×38 mm thin section28×38 mm thin sectionExciting energy50 kV, 600 µA25 kV, 222 µA25 kV, 222 µAStep size12 µm1grain centers26 µm (EDX)1Dwell time per spot2 ms7 ms per grain7 msMeasurement time6 h9–12 h40 h
1 Continuous mapping. 2 Grain segmentation by BSE – one EDX measurement per
grain center.
M4 Tornado chemical analysis of a K-feldspar spectrum from Fig. 1,
EMPA from three different spots on K feldspar and literature value from Deer
et al. (2013).
Assessment of 2-D classification data has been applied and discussed widely
among remote sensing scientists. In most cases, hyperspectral images are
evaluated by comparison to reference images (Foody, 2002), which are
supposed to have true classification values (ground truth images), e.g.,
through manual control. Each pixel of the reference image is compared to the
new classification, and the numbers of pixels assigned to each class are
entered into an error matrix (or confusion matrix); the reference pixels are
listed in columns and the new data in rows. The central diagonal represents
the pixels that were assigned to the correct class; all others have been
assigned to a different class. The classification's overall accuracy can be
calculated by dividing the sum of the correctly classified pixels (central
diagonal) by the total pixel number (Congalton, 1991).
In this work we evaluate the utility of µ-EDXRF-based spectral
classification and image analysis of thin sections using hyperspectral
software (ENVI) for automated mineralogy compared to SEM + MLA.
µ-EDXRF spectrum of a K feldspar with the main
constituents:
Al (1.04 keV), Si (1,7 keV) and K (Kα: 3,3 keV, Kβ:
3,6 keV).
Block scheme for the workflow of the µ-EDXRF analysis.
Thin section scan and classification results from mineral liberation
analyzer (MLA) and M4 Tornado – ENVI. The size of thin sections 424 and 484
is about 30×20 mm; thin section 342 is about 40×25 mm; the color key for
the classified minerals is displayed in Fig. 4.
Material and methodsSamples
Three polished thin sections of plutonic rocks from the collection of the
Federal Institute for Geosciences and Natural Resources (BGR, Germany) were
selected to be analyzed both with µ-EDXRF and SEM + MLA. In addition,
samples with different properties regarding mineral content and
grain size were selected. Prior to this work, the thin sections were
analyzed under a polarized light microscope and classified as quartz diorite
(sample 484), monzogranite (sample 342) and syenogranite (424) with varying
mineral content regarding the main minerals quartz, feldspars, biotite,
hornblende, and pyroxenes, as well as trace minerals such as magnetite, ilmenite,
titanite, calcite, apatite, zircon and allanite. The thin section size is
about 35×23 mm.
For µ-EDXRF data acquisition, the M4 Tornado from Bruker was used. The X-ray radiation is generated by a tube with a
a rhodium
target operating with a maximum power of 30 W. The polychromatic beam is focused by
a polycapillary lens, resulting in a spot size of 17 µm at 17.48 keV
(molybdenum (Mo) Kα). In this work, the M4 Tornado is equipped with two silicon
drift detectors facing each other at 180 and 90∘
to the tube. The maximum tube excitation of 50 kV and 600 µA was
chosen in order to differentiate elements with
overlapping lines such as zircon, tin and barium, which have overlapping L lines
with K lines of phosphorus, calcium and titanium, respectively. The thin sections were
measured with a step size (i.e., pixel size) of 12 µm and a dwell time
of 2 ms per pixel. The total resolution of the measurement is about 2200×1600 pixels. The measurement takes about 3 h for one detector. In order to
eliminate the effect of diffraction, the samples were measured with both
detectors separately and the minimum intensity for each pixel was calculated
and used for the classification. For more details regarding diffraction
elimination see Nikonow and Rammlmair (2016). The measurement
data were saved into a data cube, which contains a full spectrum for each
measured pixel. With the M4 Tornado software, these results can be presented
as element distribution maps with element intensities in false colors or
grey scales. From these element maps, regions of interest
can be selected and quantified chemically using the fundamental parameter approach.
Scanning electron microscope (SEM) and mineral liberation analyzer
(MLA)
For comparison and validation of the µ-EDXRF-based mineral
classification and analysis, the SEM FEI Quanta 650F MLA-FEG was
used. For this work, two modes of MLA were applied: (1) samples 424 and 342
were measured in the XBSE mode, where grains are classified and separated
according to their grey level in the BSE image. Then, each separated grain is
measured in the center with the X-ray detectors and classified chemically
using a predefined mineral database. For the sample 484 the XBSE mode was
not suitable, since the grey values of hornblende and biotite were too
similar for a correct grain separation. Therefore, this sample was measured
in (2) GXMAP mode. In this mode, grains are not separated by their grey
values, but the whole sample was continuously mapped with an EDX analysis
every 6 µm. The details of the SEM image acquisition are listed in
Table 1. A detailed description of the functionality of MLA and the
measuring modes can be found in the literature (Dobbe et al., 2009; Fandrich et
al., 2007; Gu, 2003).
Modal mineralogy from the M4 classification in area-% (MLA
indicates thin section analyzed by MLA in XBSE mode, GX indicates MLA in
GXMAP mode, M4 indicates thin section analyzed by the M4 Tornado. < 0.0 means that the mineral was detected in a quantity less than one decimal.
For analysis of the element distribution maps obtained by M4 Tornado, ENVI
5.1 by Exelis (Exelis Visual Information Solutions, 2015) was used.
The supervised classification algorithm spectral angle mapper (SAM)
(dos Reis Salles et al., 2016; Kruse et al., 1993) was applied on
the 2-D data from M4 Tornado for the mineral classification. The
classification algorithm allocates a mineral name to each pixel in the
element distribution map according to a previously defined database of mineral
spectra (endmember collection). It calculates the spectral similarity of two
spectra, which is described by the angle between the vectors of both
spectra. The angle of the spectral similarity can have values from 0 to π/2 in radians (Masoumi et al., 2017). The vectors are in an
n-dimensional space, where n is the number of bands (here, element lines)
(Masoumi et al., 2017). The SAM was developed for classification of
hyperspectral images and is most widely applied in context with
mineralogical classification (Van der Meer and De Jong, 2003; Girouard et al., 2004).
To establish the spectral database, thin sections were studied under a
polarized light microscope. Some minerals were also analyzed by electron
microprobe analysis (EMPA; Table 2; Sobańska, 2009). Knowing the
mineral name and its location on the thin section, the spectra of the
corresponding pixels of the EDXRF measurements were defined as mineral
endmembers for the mineral database. Additionally, EDXRF spectra of selected
areas such as mineral grains can be selected to calculate a sum spectrum and
quantify the element ratios for a quantitative chemical analysis using the
Bruker fundamental parameter algorithm. Figure 1
shows the spectrum of a K feldspar, and the quantification results are listed
in Table 2, which match well the chemical data of a K feldspar from Deer
et al. (2013). In comparison to EMPA data, light elements such as Al and Na
seem to be slightly overestimated by M4 Tornado quantification but are
still in an identifiable range of a K feldspar. The workflow of the µ-EDXRF measurement and following analysis is displayed in the block scheme
of Fig. 2.
Results: comparison between ENVI and MLA for plutonic rock thin
sections
For comparison and verification of the µ-EDXRF-ENVI classification,
three thin sections of plutonic rocks were analyzed and classified with ENVI
and compared to MLA. The classification results and the mineral distribution
maps are shown in Fig. 3; the modal mineralogy of both methods is listed in
Table 3.
In general, the mineral distribution maps of both classifications correspond
well with the thin section photo. Both methods recognized the present
minerals. Single grains can be identified in thin section and both mineral
distribution maps. Minerals that sometimes are difficult to differentiate in
thin section microscopy such as quartz and plagioclase can be identified and
separated using the chemical information, since plagioclase contains
silicon as well as aluminum, sodium and calcium. Texture and grain
structures of even complex intergrowth are recognizably well mapped. A few
differences can be found in details: MLA is able to detect
microstructures such as microperthitic intergrowth in sample 424, due to the
smaller beam diameter and the sampling depth limited to a few micrometers,
whereas the data based on the M4 Tornado measurement integrate information
of a 17 µm spot size. In the ENVI classification of sample 342,
clinopyroxene grains surrounded by plagioclase sometimes have small rims of
hornblende. This is due to the overlap of both minerals producing mixed
signals which are chemically similar to hornblende. The MLA data (6 µm pixel size) were
resized to an M4 pixel size of 12 µm by combining 2 by 2 pixels (nearest neighbor) for technical
reasons and image to image registration (Table 4). To compare both classifications in detail,
an error matrix was calculated with ENVI.
The error matrix shows fair overall accuracy of 76 %. There seem to be
three classes of accuracy: the first class, with good accuracy of about 80 %, consists of K feldspar, quartz, allanite and hornblende. The second
class, with fair to medium accuracy between 60 and 70 %, consists of ilmenite,
plagioclase and orthopyroxene. Minerals with low accuracy are clinopyroxene,
which is mostly confused with hornblende; and magnetite, zircon and
apatite, which are mostly unclassified in MLA.
Error matrix in percent for sample 342 with the MLA as reference
data in the columns and the µ-EDXRF in lines with mineral
abbreviations as follows: unclassified, Uncl.; clinopyroxene, Cpx;
hornblende,
Hbl; allanite, Aln; magnetite, Mag; ilmenite, Ilm; quartz, Qtz; K feldspar,
Afs; zircon, Zrn; apatite, Ap; orthopyroxene, Opx.
Detailed view of a section in the upper right corner of sample 342:
mineral distribution map from MLA (a) and ENVI (b).
For a detailed visual comparison of both classifications, a section of
sample 342 is shown in Fig. 4. As mentioned before,
small plagioclase veins cannot be identified with the M4 Tornado; it is also
noticeable that some minerals such as allanite, ilmenite or orthopyroxene have
small unclassified (white) rims in the ENVI map, which is due to the mixed
fluorescence signals coming from a different depth in the M4 Tornado
measurement. Separation of clinopyroxene and hornblende is difficult,
because both minerals are chemically similar. Main elements such as silicon,
calcium and iron are present in both minerals, and other elements such as
aluminum, potassium or titanium have very low X-ray fluorescence intensities
in the M4 Tornado measurement due to their low content or low atomic number.
Nevertheless, the grain outlines in general are comparable.
Discussion
The proposed method depends mainly on the correct mineral classification.
The key is to create a comprehensive mineral database that contains all
present minerals and is able to distinguish minerals of similar spectral
features. Having information about the geological system of the sample and
the possible paragenesis will improve the classification and decrease the
occurrence of unclassified areas. Since many minerals are parts of solid
solution series, e.g., plagioclase, pyroxene or biotite, the mineral database
can consist of several endmembers of one mineral group in order to classify
chemical changes within one solid solution series.
Isochemical minerals such as rutile, anatase and brookite (TiO2) are not
distinguishable with an M4 Tornado measurement. Only with further
information from other methods such as Raman spectroscopy or X-ray diffraction
could more information about the crystal system be obtained and used for the
classification. Until then, identification of the mineral is based on the
chemical information. However, the classification can be extended from mineral
groups to mineral endmembers easily, when more detailed information is
available. The rock classification should “grow with the science”
(Carr and Hibbard, 1991).
A similar problem creates the range of detectable elements. Since the
lightest detectable element is sodium, minerals that contain lighter
elements are not clearly identifiable. Apatite, for example, can be
identified by the abundance of phosphorus, calcium and possibly chlorine.
Distinguishing between fluorapatite and hydroxylapatite is not possible with
this method. Therefore, the group name apatite should be used. A special
case is iron. If there is a mineral containing iron solely, there are
several possibilities: the iron oxides magnetite and hematite would fit the
chemical data since oxygen is not detectable. The presence of titanium would
indicate magnetite or titanomagnetite. Iron hydroxide and oxide-hydroxide
fit the chemical data, too, as well as siderite (iron carbonate). Little
amounts of calcium, magnesium or manganese would indicate the iron
carbonate, but several possibilities still remain for iron.
Furthermore, the resolution limits of each device have to be taken into
account. Looking at the modal analysis, differences occur mostly from the
estimation of quartz and plagioclase and the intimate intergrowth of
feldspars. According to the MLA measurement, sample 484 contains about 5 %
less plagioclase than what the M4 classification determined. These
differences result from the microperthitic intergrowth of the sample. The
perthite shows small lamellae of plagioclase in the K-feldspar host. When
these lamellae are smaller than the beam diameter of 17 µm, the pixel
is classified as an alkali feldspar with elevated sodium and calcium
content, while a small fraction of plagioclase is lost in the modal
mineralogy. Similarly, differences can occur from other mineral combinations
or overlaps in one pixel, which may even result in a different mineral
classification or in unclassified grain boundaries. Plagioclase overlapping
with pyroxene can be chemically similar to hornblende and result in a
small hornblende rim between plagioclase and pyroxene grains. Minerals with
low classification accuracy, compared to MLA, such as zircon, magnetite and
apatite, are present mostly as small and single grains surrounded by
plagioclase. This results in an unclassified rim due to overlapping signals
of both minerals. Because of the small grain sizes, a number of
unclassified border pixels form a relatively large proportion compared to
the number of core pixels and, therefore, result in a very low
classification accuracy.
Comparing the M4 classification procedure to MLA, some difficulties should
be mentioned. With MLA, differentiation of minerals
with a similar average atomic number is difficult when the measurement
should involve all rock-forming and accessory minerals from very low to
very high average atomic numbers. The grey values are a function of the
average atomic number of the mineral, and the XBSE mode uses the grey
values of the BSE image for grain separation.
The grain separation in XBSE mode is
based on the grey value of the BSE image. Minerals of similar atomic number
will be eventually combined (e.g., plagioclase–quartz–muscovite or
biotite–hornblende–pyroxene), and only one measurement in the center of
the particle will be performed. Contrast and brightness of the BSE images are
adapted for the present mineral assemblage. If a good contrast for distinction
of light minerals (dark in BSE) such as quartz and plagioclase were needed, the
grey level spectrum would be stretched in the lower region to differentiate light minerals.
Consequently, heavier minerals such as allanite or zircon would appear white, and
adjacent minerals of equal grey value would be identified as one grain and would not be separated.
Therefore, it was necessary to measure the
thin section of sample 484 in GXMAP mode, which increased the measuring time
from 12 to 40 h. Another source of error was found to be the frame
overlapping. Some frames are shifted apart, and the automatic particle
joining had to be corrected manually several times.
The combination of µ-EDXRF and hyperspectral classification shows
good applicability for heavy accessory minerals and sulfide ores, since
heavy elements are easy to detect due to their good fluorescence response.
Even small grains of accessories can be detected, since the EDXRF mapping
provides spatially resolved data. If there is a sample area of 20×15 cm
with just one gold grain large enough to detect, the element distribution
map will show the gold grain. ENVI offers the possibility of locating such
minor minerals of interest and providing the pixel coordinates of each grain
of interest. Furthermore, the mineral distribution maps offer opportunities
to perform image analysis, e.g., for calculation of geometric grain
parameters (Nikonow and Rammlmair, 2016).
An important factor to consider for scientist and laboratories is the financial
aspect, such as purchase and maintenance costs as well as easy handling and
usability of the devices. In this case, the financial advantage is on the
side of the µ-EDXRF. The acquisition costs for the µ-EDXRF
including two detectors and the software (ENVI) is about EUR 250 000,
whereas the SEM including the MLA software may be around 4 times higher.
To operate the SEM, high vacuum pumps and a nitrogen supply are necessary in addition to
a skilled operator, whereas the µ-EDXRF can operate at
atmospheric pressure or in low vacuum and is relatively easy to operate for
scientists and also students. Both devices should preferably be operated in
an air-conditioned laboratory; however, in this work the µ-EDXRF was
not in an air-conditioned room.
Taking into account the limits and potentials of both methods, it is
important to analyze the question or the problem the data should answer or
solve. If the samples have fine intergrowths or small grains that need to be
resolved in high detail, the spot size of 17 µm might be too large.
Also if the presence of light elements such as carbon and oxygen needs to be
known, other methods such as MLA should be preferred. On the other hand, the
big advantage of µ-EDXRF is the little preparation and measuring
time required. It is possible to have chemical information of a large sample area
hours after having taken the samples. Whereas chemical analysis can take days
to pulverize or digest the samples, EDXRF mapping can give a good overview
in a short time period, although within a certain error limit. Moreover, the
sample stays intact for further analyses. For detailed chemical analysis
either the bulk sample has to be processed or small areas or minerals have
to be separated, which is very time-consuming.
µ-EDXRF provides spatially resolved chemical data, and therefore even
small areas of interest can be analyzed separately. For microscopy and
petrographic analysis thin sections have to be prepared, which are limited
in most cases to an area of a few cm2. Microscopy is very
helpful, but it can be advantageous to be able to see more than a few
thin sections. Since the EDXRF maps are fast to obtain, depending on the
size and chosen acquisition time, results can be obtained within minutes or
several hours.
Conclusions
In this work we describe the multispectral classification of plutonic rock
thin sections based on µ-EDXRF data. The SAM classification was shown
to work well for primary, mostly unweathered plutonic rocks. Compared to
MLA, the mineral classification results correspond well on thin sections.
Problems arise due to the technical limits of the used µ-EDXRF
instrument including resolution and not-measurable elements, whereas a lot
of valuable information of even larger samples than thin section size can be
obtained faster with multispectral classification.
This method is suitable for obtaining a fast sample overview with chemical,
textural and mineralogical information, and even geometric grain information,
as it works non-destructively and covers an area of 20×15 cm. Therefore,
it can be seen as the first step in a series of geoscientific analyses
providing a large scale overview, while the samples remain intact. This method can
help in choosing the areas of interest for more detailed measurements, thin
section preparation, MLA, or other high-resolution or bulk geochemical
analyses. Having spatial and chemical information about the samples can
decrease the number of thin sections that need to be prepared or the following
chemical analyses, since the choice can be made more targeted and
systematic. Overall, it is an objective, repeatable, and quantifiable way for
modal mineralogy and petrographic image analysis.
Raw data are available upon request from the corresponding author.
The authors declare that they have no conflict of
interest.
Acknowledgements
The results of this work are part of research that is funded by the German
Federal Ministry of Education and Research (BMBF) within the project
SecMinStratEl (grant no. 033R118B). The authors are thankful to Jeannet Meima
for the many helpful comments on the manuscript, Katarzyna Krasniqi for
parts of the mineral database, Dominic Göricke for technical support
with the SEM and Gerhard Heide from the TU Bergakademie Freiberg for the
fruitful discussions.
Edited by: Lev Eppelbaum
Reviewed by: two anonymous referees
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