In this study we present a novel method for the automatic
detection of minerals and elements using hyperspectral transmittance imaging
microscopy measurements of complete thin sections (HyperTIM). This is
accomplished by using a hyperspectral camera system that operates in the
visible and near-infrared (VNIR) range with a specifically designed sample
holder, scanning setup, and a microscope lens. We utilize this method on a
monazite ore thin section from Steenkampskraal (South Africa), which we
analyzed for the rare earth element (REE)-bearing mineral monazite ((Ce,Nd,La)PO4), with high
concentrations of Nd. The transmittance analyses with the hyperspectral VNIR
camera can be used to identify REE minerals and Nd in thin sections. We
propose a three-point band depth index, the Nd feature depth index (NdFD),
and its related product the Nd band depth index (NdBDI), which enables
automatic mineral detection and classification for the Nd-bearing monazites
in thin sections. In combination with the average concentration of the
relative Nd content, it permits a destruction-free, total concentration
calculation for Nd across the entire thin section.
Introduction
The detection and classification of minerals and elements are crucial in
mineral exploration and outcrop characterization. They are the main drivers
of past and recent scientific research in spectroscopy and imaging
spectroscopy (Hunt, 1977; Boesche et al., 2015). Hyperspectral imaging
cameras have been successfully used to detect minerals and elements in
outcrops, rock samples, and drill cores using reflectance measurements (van
der Meer et al., 2012). Whereas hyperspectral imaging is widely used for
reflectance measurements in the geosciences, transmittance measurements
using imaging spectrometers are mostly applied in biology, agricultural, and
food sciences. Examples of hyperspectral imaging transmittance applications
include imaging transmittance measurements of leaf traits (Bergsträsser
et al., 2015), plant–pathogen interaction (Thomas et al., 2017), quality of
blueberries (Leiva-Valenzuela et al., 2014), and insect damage in soybeans
(Huang et al., 2013). In geosciences, hyperspectral transmittance
measurements have been conducted using point spectrometers. Examples of
transmittance measurements involving (non-imaging) point spectrometers
include the measurement of thin sections and powdered minerals for
evaluating the effects of mineral orientation using a Fourier transform
infrared (FTIR) micro-spectrometer (Ekaterina and Veronique, 2009), as well as
measurements of the transmittance, reflectance, and emission of meteorite
samples over a spectral range from the ultra-violet (UV) to the infrared
(IR) using two FTIR spectrometers (Maturilli et al., 2016).
This study presents and discusses a new method for the detection of minerals
and chemical elements involving hyperspectral imaging transmittance
measurements of complete thin sections from rock samples. The measurements
are performed by using a hyperspectral push broom imaging camera that
operates in the visible and near-infrared (VNIR). It is designed for a broad
variety of applications in the laboratory. The camera is equipped with a
microscope lens for the use in the laboratory environment. A special
measurement setup to mount the thin sections for transmittance measurements
was constructed. The method was developed to analyze several thin sections
containing Nd-bearing monazites from various regions including Norway,
Sweden, South Africa, and Namibia.
In this study we present an application of this method to a polished thin
section from a monazite-rich sample, collected from the Steenkampskraal
monazite mine in South Africa. The thin section was additionally examined
using a petrographic microscope and analyzed using an electron microprobe
in order to provide a geochemical reference frame for comparison with our
analytical results. Using this sample as an example, we present the mineral
and element detection capabilities of the hyperspectral imaging camera when
measuring transmittance in the VNIR spectrum.
Geology of Steenkampskraal
The Steenkampskraal monazite ore body is located ca. 70 km north of
Vanhynsdorp in the Western Cape Province, South Africa. It represents one of
the largest vein-type monazite–apatite–chalcopyrite–magnetite deposits in
the Mesoproterozoic Bushmanland subprovince of the Namaqua-Natal
Metamorphic Belt (e.g., Kilian, 2011; Basson et al., 2016). The ore body is
lenticular-shaped, with an average thickness of about 0.5 m. Mesoproterozoic
granite gneiss hosts the ore body, which is situated in a structural zone
characterized by early ductile shearing and overprinted by later brittle
deformation and faulting. The ore occurs as fine-grained massive lode that
is exposed on the surface for about 400 m, with a known down-dip extension
of 450 m. Preliminary U–Pb geochronological data on monazite suggest that
formation of the Steenkampskraal monazite ore body occurred at 1046 ± 7.5 Ma (Knoper et al., unpublished data, cited by Basson et al., 2016).
Mining of monazite for Th was active from 1952 to 1963. Detailed studies of
the ore body have been made by Pike (1959), De Waal and Viljoen (1970),
Andreoli et al. (1994, 2006), Basson et al. (2016), Read et al. (2002),
Kilian (2011), Jones and Burnett (2013), and Jones and Hancox (2012). The
ore body contains up to 55 % following Andreoli et al. (1994) and up to 80 vol % monazite with a total rare earth element (REE) concentration of ca. 45 wt % following
Kilian (2011), with the light rare earth elements (LREEs) predominating. Highly fractionated P-rich
immiscible liquids in a tectonically instable environment are suggested to
form the deposit (Andreoli et al., 1994).
Methodology
In order to perform the transmittance measurements, we used a HySpex VNIR
1600 hyperspectral imaging camera. The push broom line scanner utilizes a
Kodak KAI-2020 CCD as the detector element, which is part of the
Adimec1600m/D camera (Lenhard et al., 2015). It operates in the spectral
range from 400 to 1000 nm with 160 bands and 1600 spatial pixels per line
(Lenhard et al., 2015). The resulting hyperspectral images were analyzed
with feature-fitting techniques, and then compared to petrographic
microscope images and electron microprobe analyses (EMPAs).
Measurement setup
All analyses with the HySpex camera were conducted in the optical laboratory
of the German Research Centre for Geosciences (GFZ) in Potsdam. The HySpex
camera is mounted on an aluminum frame for translational measurements in the
laboratory (Rogass et al., 2017) (Fig. 1, label 1). The frame supports a
motorized sample tray (translation stage) that carries the samples (Fig. 1,
label 7). The speed with which the translation stage moves and transports the
samples through the field of view of the sensors depends on the integration
time and the fore-optics of the fixed camera (Rogass et al., 2017). To
measure transmittance with the HySpex camera a special sample holder was
constructed (Fig. 1, label 3). The holder was painted in matte black to
reduce reflection and to buffer and reduce the effects of specular
reflection by the light source. The light from the lamp was reflected by a
white reference panel with a spectrally known diffuse, Lambertian spectral
reflectance standard: 95 % reflectance, SphereOptics SG3151 95 % A
(Fig. 1, label 6). The lamp irradiates the white reference with a 45∘
angle through the open side of the sample holder (Fig. 1, label 5). The white
reference diffusely reflects the light through the opening slit at the top
of the sample holder where the thin section is mounted (Fig. 1, label 4). The
HySpex camera is mantled in order to reduce the stray light from
sources other than the opening slit of the sample holder. Black cellular rubber
was used to darken the space between sample holder and camera as this
material mainly absorbs light in the considered wavelength range (Fig. 1,
label 2). Measurements with an epoxy-covered microscope slide were conducted
in order to subtract the irradiation, reflected by the white reference plate
and the absorption and transmittance features of the epoxy and glass of the
microscope slides, from the spectrum of the rock thin sections. The
integration time was calibrated from several test runs until a strong signal
was acquired and oversaturated pixels were not present (Table 1). A high
signal to noise ratio (SNR) mode was used to reduce noise in the collected
spectra. It is embedded into the software of the HySpex data acquisition
tool and determines how often a frame is repeatedly captured. These
measurements are averaged to reduce the zero mean, white Gaussian noise in
the measurements. Since the measurement time for each thin section is
roughly multiplied by the number of measurements set at the high SNR
setting, an optimum between signal quality and practical measurement time
had to be found. A high SNR mode of 6, which resulted in a roughly 60 min
overall measurement time, was chosen for the thin section with the given
integration time shown in Table 1.
The setup to perform the transmittance measurements. The HySpex
VNIR-1600 camera was used for the measurements in this study.
Specifications of the HySpex VNIR-1600 camera (Lenhard et al.,
2015) and the applied setting.
First the raw image data with the “HySpex RAD” software was processed,
rescaling the digital numbers (DNs) into radiance in accordance with the
camera's radiometric profile (calibration is done by NEO yearly). The files
were then spatially resized. The parts of the images not covering the thin
sections (the black surface of the sample holder) are cropped to shrink the
file size in order to speed up the following calculations and also save data
storage space and memory. After the images have been cropped, the
transmittance of the rock thin sections are isolated. Each pixel of the
according image is divided by an averaged spectrum of the measured epoxy-coated reference slide. This spectrum includes that of the light source
(white reference) so that the images are normalized and only the
transmittance of the thin sections remains, ranging between 0 and 1.
Identification of minerals
For comparative data analysis, we fitted spectral reflectance library data
to the obtained hyperspectral image data. The USGS Spectral Library version 7 (Kokaly et al., 2017) had to be spectrally resampled with respect to
HySpex spectral bands using the spectral resampling tool embedded in ENVI.
The spectral resampling tool in ENVI utilizes a Gaussian model of the
spectral response functions (SRFs) for all HySpex bands, which is derived
from former calibration measurements (Norsk Elektro Optikk, 2020). The
spectral signatures of certain minerals and elements in Nd-bearing monazites
are characterized by multiple absorption features in the VNIR range (Boesche
et al., 2014). Absorption features can then be used to identify minerals in
hyperspectral imaging spectroscopy (Boesche et al., 2014). The automatic
identification and mapping of Nd-bearing monazite were implemented through the
following steps. Three characteristic absorption features of Nd-bearing
monazite for mineral identification and mapping were used: ∼ 580, ∼ 740, and ∼ 800 nm, following Boesche et
al. (2014). The law of conservation of energy justifies using the position
of the absorption features derived from reflectance spectra for
transmittance measurements. The representative spectral absorption features
of elements and minerals have the same position in measured transmittance
spectra, given that they are not opaque. A three-point band depth index for
each of the three main absorption features of Nd was used for detection and
mapping.
The Fe feature depth index (IFD) was adapted (Mielke et al., 2014) (Eq. 1) to
the three features of the monazite-bound Nd (∼ 580,
∼ 740, ∼ 800 nm), resulting in the Nd feature
depth index (NdFD), with the shoulders and maxima shown in Table 2. The
NdFD is the difference between the transmittance (t) in the absorption
maxima (t(AM)) of the specific feature (see Table 2) and the linearly
interpolated transmittance value of the specific feature (tint(AM)) see Eq. (1) (adapted from Mielke et al., 2014). tint(AM) is calculated following Eq. (2), where t(LS) is the
transmittance of the band closest to the position of the left shoulder of
the feature (see Table 2), and t(RS) is the transmittance of
the band closest to the position of the right shoulder of the feature (see
Table 2). Equation (3) shows the application of Eqs. (1) and (2) for the
first main absorption feature of Nd.
1NdFD=tint(AM)-t(AM)2tint(AM)=t(LS)+(t(RS)-t(LS))×λAM-λLSλRS-λLS3NdFD1=tint(578.46nm)-t(578.46nm)tint(578.46nm)=t(563.91nm)+(t(618.46nm)-t(563.91nm))×λ578.46nm-λ563.91nmλ618.46nm-λ563.91nm
The NdFD was applied to each pixel of the hyperspectral image to highlight
the monazite and Nd ((Nd)PO4) distribution in the thin section (see
Sect. 4.2). The three bands in the resulting image represent the absorption
depth of the three characteristic absorption features for Nd: NdFD 1:
Band 1 at ∼ 580 nm; NdFD 2: Band 2 at
∼ 740 nm; and NdFD 3: Band 3 at ∼ 800 nm. The
resulting three bands are multiplied by each other to create the Nd band depth
index (NdBDI) (see Eq. 4). The NdBDI image is
a single-band greyscale image used for classification mapping and can be
compared, for example, with SEM/BSE (scanning electron microscope/backscattered electron) results.
NdFD1×NdFD2×NdFD3=NdBDI
The classification map is then created using the NdBDI greyscale value image. The resulting monazite map displays Nd-bearing monazite
as red pixels, while the rest (no detection of Nd) is displayed as black
pixels. The red pixels of the monazite classification map can also be
overlaid on a true color RGB image of the thin section to mark the
monazites. The pixel-wise classification of Nd-bearing monazite and the
knowledge of the relative Nd content (wt %) of the monazites (either
content estimate by a database or more exact by EMPA) allows for the free
determination of the total weight content (W) of Nd in the thin
section by the calculation shown in Eq. (5).
W(Element)=A(Pixel)×T×N(Mineral)×ρ(Mineral)×C(Element),
where weight content of the element is W(Element) (µg),
pixel size is A(Pixel) (µm2),
thickness of the thin section is T (µm),
number of pixels classified as mineral is N(Mineral), bulk density (electron density) of classified mineral is ρ(Mineral) (µg µm-3), and
relative element concentration in the mineral is C(Element) (0–1).
The absorption maxima and left and right shoulders of the three
absorption features of monazite-bound neodymium used for each
NdFD calculation.
The results from the mineral chemical analysis are compared to the results
from the processed hyperspectral images of the thin sections for method
assessment. This includes the analysis by a petrographic microscope and
EMPA. All calculations were conducted using the programming language Python 3. The three-point band depth index, the IFD following Mielke et al. (2014), and
the automatic calculations can be adapted for any element or mineral that
has characteristic absorption bands.
EMPA measurements
A profile across the main monazite bands of thin section STK 115c was mapped
on a JEOL JXA-8200 electron microprobe at the University of Potsdam. The
elemental profile map was generated with an acceleration voltage of 20 kV, a
beam current of 20 nA, a pixel size of 2, and a dwell time of 20 s. The
conditions used for additional analyses of monazite grains along the profile
were 20 kV acceleration voltage, 40 nA beam current, and a beam size of 2 µm. Counting times were between 10 to 25 s on the peak for major
elements and 50 s for REE and other trace elements. Spectral
lines, standards, and data reduction were applied following Lorenz et al. (2019).
The following spectral lines and mainly Smithsonian natural mineral
standards were used: fluorapatite (P Kα, Ca Kα),
wollastonite (Si Kα), YPO4 (Y Lα), LaPO4 (La Lα),
CePO4 (Ce Lα), PrPO4 (Pr Lβ), NdPO4 (Nd Lβ), SmPO4 (Sm
Lβ), EuPO4 (Eu Lα), GdPO4 (Gd Lα), DyPO4 (Dy Lβ), HoPO4 (Ho Lβ), ErPO4 (Er Lβ), YbPO4 (Yb Lα), LuPO4
(Lu Lα), monazite (Th Mα), and crocoite (Pb Mβ). The EMPA
data were reduced using the PRZ-XXP (Phi-Rho-Z method) correction routine.
Results and discussionMicroscope and electron microprobe results
The thin section used (sample STK 115c) contains medium-grained monazite
forming layers intergrown with quartz or allanite. In the monazite layers,
pyrite occurs as an interstitial phase along monazite grain boundaries.
Layers, adjacent to the monazite-rich part, are dominated by coarse-grained
anhedral quartz and anhedral to euhedral pyrite or quartz–allanite rich
layers. More fine-grained layers or coronas between quartz contain allanite
(ca. 20 wt % LREE, Ce > Nd > La), monazite, and
fluorapatite (Table 3, Figs. 2 and 5c).
Thin section under crossed nicols (Mnz: monazite; Qtz: quartz;
All: allanite + quartz + apatite; Py: pyrite). The monazites show strong
birefringence (interference colors).
Chemical composition of the REE-bearing monazites (wt %)
from five locations and the average of them (below detection limit (bdl) is
counted as 0). Microprobe analyses.
Chemical composition of monazites along the mapped profile in sample STK115c (wt %) 12345AverageSiO20.690.760.600.710.620.676P2O529.1129.3728.9129.6829.0129.216CaO1.271.141.281.181.271.228Y2O32.102.142.122.122.142.124La2O312.6612.7812.7812.7312.7912.748Ce2O327.9527.4528.2527.6428.0727.872Pr2O32.962.882.872.802.942.89Nd2O310.1610.0110.249.9910.1210.104Sm2O31.431.381.401.341.421.394Eu2O30.150.070.110.110.120.112Gd2O31.651.641.601.661.701.65Dy2O30.420.430.410.440.400.42Ho2O30.090.09bdlbdl0.060.048Er2O30.190.210.210.240.110.192Yb2O30.060.07bdlbdl0.070.04Lu2O3bdlbdlbdlbdlbdlbdlPbO0.420.430.470.420.460.44ThO28.868.898.498.578.538.668Sum100.1699.7399.7599.6399.8399.822Hyperspectral transmittance imaging microscopy (HyperTIM) results
The false color image in Fig. 3 is the result of the transmittance
calculation. The RGB bands at R 603.91 nm, G 578.46 nm, and B 563.9 nm are
displayed in order to highlight the monazites by one major absorption band
(AM at ∼ 580 nm). Color saturation was adjusted (300 %). This
image already enables a quick manual assessment of Nd-bearing monazites
through the purple color as the greenish part of the RGB representation is
absorbed (Fig. 3a). A comparison between the measured spectral signatures of
a 3×3 pixel area on a monazite and a measured monazite from the USGS
Spectral Library reveals that the absorption features share the same
position and shape, only varying in feature depth (Fig. 3b). This similarity
in reflectance and transmittance measurements is expected as the absorption
feature positions are the same. Hence hyperspectral signal processing
methods, other than a three-point band depth index, could also be used for
analyzing the images as they can be treated as being very similar to more
classical reflectance measurements, with the exception of opaque minerals.
The false color RGB image calculated by the three-point band depth index (R:
NdFD 1: Band 1 =∼ 580 nm; G: NdFD 2: Band 2 =∼ 740 nm; B: NdFD 3: Band 3 =∼ 800 nm) displays and highlights the monazites through purple
and bluish pixels, while other minerals remain dark (Fig. 4a). Color
enhancement (Saturation 400 %) improved the visibility of the monazite.
The NdFD image highlights Nd bound to monazite due to the three
characteristic absorption bands of the embedded Nd (∼ 580,
∼ 740, ∼ 800 nm), whereas other minerals that
do not contain Nd in detectable concentrations are displayed as dark.
Hence, the relative absorption depth of the three bands (R: NdFD 1:
Band 1 =∼ 580 nm; G: NdFD 2: Band 2 =∼ 740 nm; B: NdFD3: Band 3 =∼ 800 nm) determine the brightness and color of the pixel. The variation from
purple to blue for the monazites is the result of differences in the
relative absorption depth of the three bands towards each other and might be
caused by slight chemical variations in the monazite Nd content by two
different temporal stages of formation or from different mineral
orientations. The effects of slight variations in the relative feature depth
is eliminated with the calculation of the NdBDI image by Eq. (4). In the
NdBDI single-band greyscale image, the overall absorption depth of the
three features determines the brightness of each pixel. Hence the stronger
the cumulative absorption is, the brighter the pixel is displayed (Fig. 4b).
The single-band greyscale image allows an easy comparison with standard
analysis techniques such as SEM/BSE imaging. We interpret the greyscale
feature depth image as a correlation-scale image representing the
correlation towards the Nd bound to monazite. Our algorithm then uses the
greyscale image to create the classification image, which displays the
pixels identified as monazites in red and non-classified pixels in black
(Fig. 4c). The resulting classification image can be overlaid with a true
color RGB or over a crossed nicols image for easy context interpretation
(Fig. 5a). The classified pixels are counted automatically, resulting in
363 522 pixels that have been classified as monazite through their
Nd content by the algorithm. HyperTIM is able to detect Nd in all non-opaque
crystals in the thin section through its spectral signature, but in order to
calculate an estimated value for the total content of Nd in Eq. (5)
(W(Nd) in µg) in the thin section by applying
Eq. (5), this method requires an average concentration of the relative Nd
content (wt %), which is here provided by an EMPA measurement.
W(Nd)=A(VNIR-Pixel)×T×N(Monazite)×ρ(Monazite)×C(Nd)W(Nd)=576µm2×30µm×363522×4.58×10-6µgµm-3×0.0866W(Nd)=,
where
A(VNIR-Pixel) is pixel size: 24 µm × 24 µm = 576 (µm2);
T is thickness of the thin section: 30µm;
N(Monazite) is number of pixels classified as monazite
(through Nd content): 363 522; and
ρ(Monazite(Ce)) is bulk density (electron density) of
monazite:
4.58gcm-3=4.58×10-6µgµm-3
(Webmineral, 2020). C(Nd) is the relative element concentration in the mineral:
10.104(Nd2O3)⋅0.857(conversionfactor)100=0.0866[0–1]
(see Table 3).
(a) False color (saturation 300 %) hyperspectral transmittance
image of the thin section to highlight the monazites by the first Nd
absorption feature (Feature 1 in b) at R: LS: 563.91 nm; G: AM: 578.46 nm; and B: RS: 618.46 nm. (b) Comparison between the measured spectral
signatures of a 3×3 pixel area on a monazite and a measured monazite from
the USGS Spectral Library (modified based on Kokaly et al., 2017). The three
main absorption features of Nd are highlighted, and the left shoulder (LS),
the absorption maximum (AM), and the right shoulder (RS) of the first feature
are indicated.
(a) Three-point band depth index (R: NdFD 1: ∼ 580 nm; G: NdFD 2: ∼ 740 nm; B: NdFD 3: ∼ 800 nm) highlighting the monazites via light purple and blue pixels
(saturation 400 %); other minerals appear dark. (b) Single-band greyscale image derived from the three-point band depth index image by Eq. (3).
The overall absorption depth of the three features (∼ 580,
∼ 740, ∼ 800 nm) determines the brightness of
each pixel. (c) Classification image/map. The pixels from the greyscale
that show absorption at all three features are identified as monazite (Nd)
(red), and other minerals/elements are not classified (black).
The total Nd (W(Nd)) content in the thin section was
estimated/determined to be 2491.84 µg. The accuracy of this value is
dependent on the accuracy of the classification and the assumption that the
Nd concentration in the Monazite is 0.0866 on average. Further study would
be required to geochemically validate and evaluate the accuracy of these
measurements and calculations, which are only shown here as a first proof of
the concept.
Comparison of the results
A comparison of the processed images (Figs. 3 and 4) with the XPL (cross-polarized light) image
(Fig. 2) reveals that the images created by the three-point band depth index
(RGB (NdFD) and greyscale (NdBDI)) (Fig. 4a and b) already enable a
quick visual detection of the monazites. Figure 4a and b also have the
advantage over classical XPL analysis by highlighting only Nd-bearing
monazites, while the other minerals are not displayed. The classification
image/map, on the other hand, also shows monazites at the edges of the thin
section and others that are only slightly visible in the greyscale image.
This is caused by a weaker absorption depth probably due to imperfect
lighting, thickness variation, or a lower Nd content, but they still
classify as monazites in the map as all three absorption bands are present.
Figure 5 shows a side-by-side comparison of the HyperTIM classification and
an area of the thin section that has been mapped with an electron microprobe
(Fig. 5c). The result from Nd2O3 mapping by electron microprobe
across the profile region shows that the highest Nd2O3
concentrations occur within the monazites (green) and lower concentrations
within the allanites (blue) (Fig. 5c).
(a) True color RGB (R: 640.27 nm; G: 549.36 nm; B: 458.44 nm) with
monazite (Nd) map classification overlaid. Red pixels are classified as
monazite (Nd). The other pixels are displayed according to their true color
information (R: 640.27 nm; G: 549.36 nm; B: 458.44 nm). Area shown in (b)
and (c) is indicated by a white rectangle. (b) Close up of (a) (indicated
region). (c) Result of Nd2O3 mapping by electron microprobe across the
profile region indicated in (a) (white rectangle), showing the highest Nd2O3
concentrations within monazites (green) and lower concentrations within
allanites (blue).
In the following the advantages and disadvantage of HyperTIM are discussed
and compared to chosen classical analytical methods used in this study such
as XPL and EMPA.
Advantages of HyperTIM
The comparison shows that HyperTIM can accurately map non-opaque monazites
based on their Nd content (Fig. 5). HyperTIM's largest advantage over
classical methods is the time required to fully map a thin section. This is
due to the automation of the mapping process. XPL requires a trained expert
to manually look over a thin section. While analysis software for light
microscopes automates the mapping to some degree, the complete automatized
mapping of an entire thin section is a novel feature of the HyperTIM method
presented here. Compared to EMPA, HyperTIM has the advantage that
hyperspectral cameras are usable with less training, mostly cost less, and
require less operating expense to use.
Thin sections analyzed with HyperTIM require no prior preparation or
coating, while for EMPA a carbon coating of the thin sections is needed. A
HyperTIM measurement of an entire thin section requires roughly 1 h
(plus 30 min of classification/mapping), while electron microprobe
element mapping for a small fraction (shown in Fig. 5c) of the thin section
required 84 h. Hence, the time and preparation efficiencies of HyperTIM
are the largest advantages over classical analytical methods. The full
automation possibility and speed of HyperTIM measurements (unsupervised
classification) of entire thin sections could be used to map large amounts
of thin sections in a relatively short amount of time.
Disadvantages of HyperTIM
The biggest disadvantage of HyperTIM is that it is not possible to map
opaque minerals. This can be seen in the case of the allanite present in the
studied sample. The opaque allanite (< 10 Nd wt %) is mapped by
the electron microprobe, while due to its opaque nature it was not mapped
through HyperTIM. HyperTIM is also only able to map elements and minerals
based on their spectral absorption properties and create classification
(pixel-based location maps) of these. The total element content calculation
we have demonstrated in this study relies on the average concentration of
the relative Nd content (wt %). This was provided by an EMPA measurement.
It is still unknown how many minerals and elements can or could be mapped
with HyperTIM as this depends on the instrument used and wavelength ranges
used for the measurements, and no mineralogical databank exists yet for
hyperspectral transmittance measurements.
Conclusion
HyperTIM represents the first scanning transmittance measurement method that
involves imaging microscopy for entire rock thin sections. This study shows
that HyperTIM can be used to detect, classify, and map minerals and their
associated elements such as Nd-bearing monazite in thin sections. The
classification of the minerals is fully automated through the programming
language Python. In the processing, a three-point band depth index algorithm
is applied (NdFD), and a single-band greyscale image (NdBDI) is derived
from it. A classification image/map is then derived from the greyscale
image, which in combination with an average concentration of the relative Nd
content (wt %) (from EMPA) is used to calculate an estimate of the total
mass content of Nd in the entire thin section. HyperTIM is a time-efficient,
novel method for destruction-free mineral and element detection,
classification, and quantization in entire thin sections, which complements
more classical methods and especially EMPA, which in combination allows for a
more exact element quantization. While it is not possible to map opaque
minerals using HyperTIM, it is a much more time and cost-effective method
for mapping non-opaque minerals. The authors of this study therefore
conclude that the ability of HyperTIM to map entire thin sections in
relatively short time compared to classical methods and the ability to use
automatic mapping algorithms to speed up analysis of thin sections are
strong advantages over classical methods.
Outlook
As this study intends to function as a first technical proof of concept for
a novel analytical method, a thin section with an abundance of monazite and
neodymium was chosen as a showcase. Further research with more thin sections
is needed to assess the sensitivity and accuracy, as well as to validate the element
content calculations/estimation geochemically. HyperTIM is a
destruction-free process/method and can be applied to other minerals, elements, and substances with characteristic absorption bands, including
REEs and other economically critical resources. The general methodology of
HyperTIM can also be applied to other wavelength ranges including UV (ultra-violet), SWIR (short-wave infrared), or TIR (thermal infrared) if used with other hyperspectral instrumentation.
Although most absorption features of elements and minerals should share the
same position in transmittance and reflectance measurements as explained in
Sect. 3, their intensity could vary to such a degree that some might be
exclusively measurable in transmittance. As HyperTIM measurements of rock
thin sections are a novel field, absorption features of elements and
minerals exclusive to transmittance measurements due to stronger crystal
lattice effects and other absorption effects than present in reflectance
measurements might present a possible new research field. This possible
difference in measurable absorption in reflectance and transmittance of
naturally occurring minerals is suggested by research in the field of
crystallography, industrial applications, and in crystal thin film research.
Also, a combination of reflectance and transmittance measurements is a
possible future solution to map minerals that are opaque and do not allow
transmittance measurements in the studied wavelength ranges. This makes
HyperTIM very promising for further research and studies on absorption
characteristics and element abundances in thin sections of all rock types.
Data availability
All data derived from this research are available upon request from the
corresponding author.
Author contributions
HLCD wrote and prepared the original draft, conceptualized and performed
the hyperspectral transmittance microscope measurements, related
calculations, and constructed the sample holder. CM contributed to the
development of the band ratios. NK and ML performed the microprobe
measurements and analysis. NK also helped with the calculations. CR was
involved in the conceptualization and was consulting on the methodology. UA
and MK provided mineralogical analysis and geological background on the
sample and reviewed the manuscript. DEH reviewed the manuscript and
consulted on the mineralogical background. MK and DEH provided the sample
STK 115c for the measurements.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
The authors thank the Helmholtz Centre Potsdam, GFZ German Research Centre
for Geosciences, for making the measurements with the HySpex camera possible
and in general for enabling the research. The authors also thank the
University of Potsdam for making the measurements with their electron
microprobe and use of their microscopes possible.
Financial support
Supported within the funding program “Open Access Publikationskosten” Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – project number 491075472.
The article processing charges for this open-access publication were covered by the Helmholtz Centre Potsdam – GFZ German Research Centre for Geosciences.
Review statement
This paper was edited by Alessandro Pavese and reviewed by Simona Ferrando and one anonymous referee.
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