Data for the study: Decrypting magnetic fabrics (AMS, AARM, AIRM) through the analysis of mineral shape fabrics and distribution anisotropy

Anisotropy of magnetic susceptibility (AMS) and anisotropy of remanence magnetization (AARM and AIRM) are efficient and versatile techniques to indirectly determine rock fabrics. Yet, deciphering the source of a magnetic fabric remains a crucial and challenging step, notably in the presence of ferrimagnetic phases. Here we use X-ray micro-computed tomography to directly compare magnetite and amphibole Shape-Preferred Orientation and spatial distribution data to AMS, AARM and AIRM data from five hypabyssal trachyandesite samples. This study thus reports quantitative petrofabric data on magnetite shape and distribution anisotropy on magnetic fabrics in igneous rocks. Our results have first-order implications for the interpretation of petrofabrics using magnetic methods.

Data generated during the study Mattsson et al. 'Decrypting magnetic fabrics (AMS, AARM, AIRM) through the analysis of mineral shape fabrics and distribution anisotropy'

-MicroXCT mineral data extracted from the software Blob3D are given in spreadsheet named after the samples. The data files named TT_XX.xls can be opened with the software Tomofab (Petri et al. 2020). The data include extracted grain volume and XYZ position in mm, XYZ radius length in mm, and XYZ orientation in directional cosines of best-fit ellipsoid of the extracted mineral grain. Text files with the same data are included.
-MicroXCT mineral data extracted with Avizo are given in a spreadsheet named 'Avizo_XCT_data'. The data are given as trend and plunge of the extracted grain long axes in geological specimen position.
-AMS data are given in Agico (https://www.agico.com/) AMS data files and in csv files. The files are named after sample name. The AMS data files can be opened with the software Anisoft5 (https://www.agico.com/text/software/anisoft/anisoft.php).
-Magnetic remanance data are given as Agico (https://www.agico.com/) JR6 data files and the 'JR6_data' csv file. The Jr6 files are named after sample name. Analysis parameters are given in an spreadsheet with the name 'Jr6 Analysis Parameters'. Data can be accessed and analysed with the software Rema6. (https://www.agico.com/text/software/rema6/rema6.php).
-Files named Samplename_distance give the distance to the nearest neighbor of magnetite grains.
-The 'SampleOrientation' Spreadsheet gives the reorientation parameters for XCT data to geological specimen position.
-Files with 'T-X' in its name include temperature vs. magnetic susceptibility data and experiment log. The field 'corrected susceptibility' is corrected for the susceptibility of the sample holder.
-Descriptions of headings/variables in the data files are given in the 'variable_codebook' .txt file.

Magnetic and microXCT data were collected on five 21 × 24 mm cores from the trachyandesite samples (CB-15C2, CB-19A1, CB-46A1, CB-55A1 and CB-61B2).

AMS measurements were performed in the Laboratory for Experimental Palaeomagnetism at the Department of Earth Sciences, Uppsala University with an Agico Kappabridge MFK1-FA in semi-automatic spinning mode. A field of 200 A/m and frequency of 976 Hz were used for the measurements.

The thermomagnetic properties (T-X) of the samples were determined by measuring the bulk magnetic susceptibility on powders of the five samples in three steps using the CS-4 attachment of the KLY-5 kappabridge at the University of St. Andrews M3Ore Lab. The samples were first cooled to -194 °C and the susceptibility of the samples was measured until the temperature reached 0 °C. The samples were then heated at a rate of 12 °C/min in an argon atmosphere from room temperature to 700°C and then cooled back to room temperature. The heated sample was again cooled to -194 °C and the bulk magnetic susceptibility was measured until the sample reached 0 °C.

AARM and AIRM measurements were performed with the JR6-a spinner magnetometer at the University of St. Andrews M3Ore Lab. Samples were demagnetized with AGICO’s LDA5 and PAM1 instruments and remanence measured in the JR6-a spinner magnetometer in a near zero field space. Analysis parameters are given in the Excel spreadsheet with the same name.
During IRM acquisition samples were magnetized in fields above 20 mT with a MMPM 10 pulse magnetizer.

In order to assess the rock fabric, five 21 × 24 mm cores from the five samples (CB-15C2, CB-19A1, CB-46A1, CB-55A1 and CB-61B2) were imaged by X-ray computed microtomography (µXCT or µCT). The cores were scanned with a Nikon Metrology XT H 225 ST X-ray microtomograph at the Natural History Museum, University of Oslo. µXCT analyses was conducted using a 140 kV acceleration voltage, a current of 300 µA, 1 s exposure time and 3016 rotational projections and using a 0.25 mm copper filter. The X-rays transmitted through the sample were collected on a planar 1920×1536 pixels detector.

The sample volume was reconstructed using the software Blob3D (Ketcham, 2005); the resulting voxel (volume pixel) size was about 16 µm3 (see Table S1). The obtained stack of 1534 grayscale images on each core represent the attenuation of the X-rays in the scanned volume, i.e., phases of higher densities have lighter grayscale voxels and lower density phases have darker voxels. Magnetite and amphiboles have comparatively higher densities than plagioclase phenocrysts and the groundmass and can therefore easily be distinguished in the scan slices. Beam-hardening effects are visible on the scan slices and is most distinct 1.5 to 2 mm from the edge of the core. Beam-hardening had no effect on magnetite segmentation due to its high attenuation of X-rays, however amphibole segmentation was strongly affected. As a consequence, a 1.5 to 2 mm rim of the scanned core was segmented as a single blob and removed from the sample set during the manual separation of the data.

In order to assess the rock fabric, five 21 × 24 mm cores from the five samples (CB-15C2, CB-19A1, CB-46A1, CB-55A1 and CB-61B2) were imaged by X-ray computed microtomography (µXCT or µCT). The cores were scanned with a Nikon Metrology XT H 225 ST X-ray microtomograph at the Natural History Museum, University of Oslo. µXCT analyses was conducted using a 140 kV acceleration voltage, a current of 300 µA, 1 s exposure time and 3016 rotational projections and using a 0.25 mm copper filter. The X-rays transmitted through the sample were collected on a planar 1920×1536 pixels detector.

The sample volume was reconstructed using the software Blob3D (Ketcham, 2005); the resulting voxel (volume pixel) size was about 16 µm3 . The obtained stack of 1534 grayscale images on each core represent the attenuation of the X-rays in the scanned volume, i.e., phases of higher densities have lighter grayscale voxels and lower density phases have darker voxels. Beam-hardening is visible on the scan slices and is most distinct 1.5 to 2 mm from the edge of the core. Beam-hardening had no effect on magnetite segmentation due to its high attenuation of X-rays, however amphibole segmentation was strongly affected. As a consequence, a 1.5 to 2 mm rim of the scanned core was segmented as a single blob and removed from the sample set during the manual separation of the data.

MicroXCT data was processed with Blob3D from X-ray serial sections. For each grain, the volume, center x, y, z coordinates and the length and orientation of the three orthogonal principal axes (long axis; intermediate axis; short axis) were determined using Blob3D by fitting a best-fit ellipsoid to the separated grain volume surfaces.

We validated the accuracy of the phase separation between Blob3D and the commercial software Avizo Fire edition. In Avizo, magnetite and amphibole were separated from the scanned cores using grayscale thresholding in the labelling module. Magnetite grains smaller than 1000 to 2000 voxels and amphibole grains smaller than 3000 to 4000 voxels were removed to limit noise and the erosion tool was employed to remove breakdown rims on crystals and imaging artefacts. No individual review of separated crystal volumes was performed in Avizo, which resulted in fast extraction of data, but the crystal volumes extracted largely represent several crystals in aggregates.

References

Ketcham, R.A., 2005. Computational methods for quantitative analysis of three-dimensional features in geological specimens. Geosphere 1, 32–41. https://doi.org/10.1130/GES00001.1

Petri, B., Almqvist, B.S.G., Pistone, M., 2020. 3D rock fabric analysis using micro-tomography: An introduction to the open-source TomoFab MATLAB code. Computers & Geosciences. https://doi.org/10.1016/j.cageo.2020.104444 Show less..