Application Note

3D Automated Quantitative Mineralogy
ZEISS Mineralogic

Author: Matthew Andrew, Ph.D.
Carl Zeiss X-ray Microscopy, Inc., California

Rock and mineral assemblages are three dimensional (3D) structures, but traditional microanalyticaltechniques used to examine and address them, such as scanning electron microscope (SEM) based
automated quantitative mineralogy (AQM), or traditional optical petrography, are inherently 2D.
The prevalence of these techniques is understandable as they give a great level of analytical precisionand reliability, but their 2D nature limits the insights that are available to the researcher, as well as
their ability to make quantitative assessments.

X-ray microscopy is a technique whereby 3D volumes can be

The last 20 years have seen a transformation in a wide range ofreconstructed from a set of projections using back-projection.

fields, widely grouped together under the umbrella of “machineThe greyscale value of a voxel (3D pixel) within these volumes

learning.” Recent studies have shown that, when applied to theis proportional to the relative X-ray attenuation within thatchallenge of voxel classification, machine learning techniques

voxel, which is in turn related to the constituent material density,

are more robust, less noise- and artifact-prone, and, critically,

atomic weight and the incident X-ray energy. Frequently, subtle

provide a set of computational techniques that are able to perform

variations in either mineral composition (such as through solidclassifications using this higher dimensional space [3-4]. This issolution, or when mineral compositions are strongly related)done by first computing a range of features from an image using

lead to reconstructed greyscale values that are sufficiently closea set of filters, creating a “pixel feature-vector.” This is then fed

to each-other that they are indistinguishable to traditional

into a machine learning algorithm (such as the “forest of random

segmentation techniques [1]. To add complexity, the absolutetrees” algorithm [5], which ultimately creates a pixel classification.

greyscale value of the reconstructed image can be affected bya range of properties associated with factors such as imaging
geometry, X-ray filtering or the presence of material outside theTraining Information

Machine Learning
Decision Tree Construction

field of view (so-called region of interest scanning, or interior
tomography), even if relative values remain the same. Suchchallenges have limited the application of X-ray microscopy toOriginal Image

Feature 2(e.g. gradient)petrological or mineralogical applications, with most publishedwork requiring intricate image processing and segmentation

Feature 1(e.g. non-local greyscale)

Correlated Data

workflows [2]. While the minerals are challenging to discriminate

Final Segmentation

Feature 3(e.g. texture)
Feature n(n is frequently > 100)by computational techniques, they are often easily discriminable
by eye, which acts to smooth out noise, remove artifacts andFigure 2 Voxel segmentation using machine learning as present in ZEN Intellesis.

perform classification on a much greater range of parameters

Data is processed through a bank of filters, creating a complex pixel-by-pixel

than just a single pixel or voxel greyscale value (Figure 1).

feature vector. Labeled pixels are used to construct a decision tree algorithm,

Manual inspection will show local and non-local greyscale

which is then applied to the entire volume to create the final segmentation.

values, gradients, textures and associations, which are integrated
by the brain to form an ultimate local or region classification.

While such techniques significantly simplify the challengeof 3D mineralogical analysis, significant challenges remain,
specifically training the models required for accurate classificationand the mineralogical assignment of the computationally classified
phases. To solve this challenge, automated quantitative mineralogy(AQM) techniques, such as ZEISS Mineralogic, can be used, andthen correlated to the 3D data. ZEISS Mineralogic uses energy
dispersive X-ray spectroscopy (EDS) scanning, integrated within
a SEM to first produce quantitative chemical maps acrossFigure 1 A) Cross section through 3D X-ray microscope (XRM) image of aextended regions of a sample surface. These chemical maps

multi-mineralogical sandstone composed of quartz, feldspars, clays and pyrite.

are then compared to an extensive mineralogical database toWhile these mineralogical differences can be seen relatively easily by eye,a profile through these minerals (B) shows no simple threshold that can beapplied to separate each mineral phase.

assign unique mineralogical classifications with a resolution
down to 200 nm [6].

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10 mm

10 mm

10 mm

Amphibole
BiotiteChamosite

FeldsparPyritePyriteBiotiteAmphibole
FeldsparABCFigure 3 A) AQM mineral map of the top surface of a drill core. (B) Correlation of the 2D AQM mineral map with the 3D tomography.
(C) 3D classification of mineralogy using ZEN Intellesis.

The propagation of this classification to 3D can occur throughtwo workflows. The simplest technique assigns mineralogyand chemical compositions to phases identified and classified
through manual training of classes identified from the 3D data.
A single composition and classification is then assigned to eachphase, which is then propagated throughout the 3D volume.
Another more sophisticated workflow is to import the classifications found from ZEISS Mineralogic and use them to train theAclassification found in 3D. This provides a more complete training
set, but only for the intersecting region between the 2D AQM
mineral map and the 3D X-ray volume. These two techniques
can be combined with extensive training data provided by
AQM supplemented by additional manual training data
provided from within the 3D volume.
The AQM mineral map is usually performed on an outside surface
of a sample, such as the polished top surface of a drill core,allowing for the easy registration of the 2D data with the 3D
tomography. A more complex workflow is possible where a thinsection is prepared from a central portion of the sample after
3D tomography data has been acquired. This approach has thedisadvantage of both being destructive and making the 2D-3DB

registration more challenging, however it may reduce certain
X-ray imaging artifacts and may be required if AQM data isrequired on deeply buried 3D features.

Figure 4 (A) Correlated AQM mineral map and 3D X-ray tomography.
(B) X-ray mineral classification.

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Figure 5 (A) Absorption tomography volume of a barred olivine chondrule from the Bjurböle meteorite. Olivine is shown with a red overlay.
(B) LabDCT reconstruction of olivine crystal orientation from within the chondrule.
Image reproduced with permission from Dr. Romy Hanna, UT Austin, USA.

ZEISS provides a complete set of integrated software packages

Conclusions and Future Workthat enable and simplify such sophisticated analytical workflows.

One of the exciting future areas of development for 3D mineral

ZEISS Mineralogic provides AQM mapping of SEM data allowingclassification is its integration with techniques such as diffraction

for quantitative major element compositions to be determined

contrast tomography using ZEISS LabDCT. This allows for crystal

and mineral classifications to be assigned. 3D data integration

orientations to be explicitly resolved through Laue conditionand correlation can be performed using ZEN Connect and ORS

X-ray diffraction. One of the challenges when applying theseDragonfly (for 3D visualization). Machine-learning based image

techniques to geological systems is their relative crystallographic

segmentation of complex 2D and 3D data can be performed

imperfection and complexity. An aspect of this complexity isusing ZEN Intellesis.

when multi-modal systems are present. In this case, performing
a 3D mineralogy workflow prior to LabDCT reconstruction allows
for specific domains within the 3D volume where crystallographic
reconstructions can be performed with a known crystal symmetry
for the associated mineral. This in turn allows for both mineralogy
and details of crystallography to be extracted from a sample.

References[1] Lai, M., “Deep Learning for Image Segmentation,” http://arxiv.org/abs/1505.02000[2] Tin Kam Ho, “Random decision forests,” Proceedings of 3rd International Conference on Document Analysis and Recognition,
Montreal, Quebec, Canada, 1995, pp. 278-282 vol.1.
[3] Andrew, M., “A quantified study of segmentation techniques on synthetic geological XRM and FIB-SEM images,”
Computational Geosciences, Vol 22, pg 1503 (2018), https://doi.org/10.1007/s10596-018-9768-y[4] Gaetano, G., et al, “The Sensitivity of Estimates of Multiphase Fluid and Solid Properties of Porous Rocks to Image Processing,”
Transport in Porous Media, Vol. 131, 985-1005(2020), https://doi.org/10.1007/s11242-019-01374-z[5] Breiman, L. Random Forests. Machine Learning 45, 5-32 (2001). https://doi.org/10.1023/A:1010933404324[6] Graham, S.; Keulen, N. Nanoscale Automated Quantitative Mineralogy: A 200-nm Quantitative Mineralogy Assessment of Fault Gouge
Using Mineralogic. Minerals 2019, 9, 665. https://doi.org/10.3390/min9110665

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EN_42_013-313 | CZ 04-2020 | Design, scope of delivery and technical progress subject to change without notice. | © Carl Zeiss Microscopy GmbH