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Advancements in automated mineralogy offer an opportunity to develop new approaches to the study of fine-grained sedimentary lithologies including paleosols and pedogenic minerals that hold valuable paleoclimate information. Automated mineralogy is a non-destructive analytical technique that relies on BSE imaging with spectral data to output multimodal hypermaps. Spatial domains are delineated and assigned mineral phases using whole spectrum best matching to reference spectra, providing quantitative sample composition estimates with high throughput data collection. We targeted a Spodosol in the lower part of the Upper Pennsylvanian Casselman Fm. of the Appalachian basin to evaluate the utility of automated mineralogy in determining paleosol composition. During deposition of the lower Casselman Fm., tropical climate during the Late Paleozoic Ice Age began a return to a more humid regime following the Kasimovian–Gzhelian boundary (~304 Ma) warming event. The Spodosol is a composite paleosol approximately 1.4 m thick that displays redoximorphic mottling, small scale (≤ 3 cm) slickensides and weak angular platy ped development. We performed automated mineralogy analysis on 9 paleosol samples, which were formed into 25 mm polished epoxy mounts of disaggregated peds, and generated complete mineralogical maps of the samples. These results indicate that phyllosilicate clays, mainly illite, formed the dominant mineralogic group (50-85%) with lesser amounts of quartz (~5-23%), feldspar (12-30%), carbonate (0-12%) and Fe-oxides (0-9%). Estimates of Al, Ca, Na and K from were used to determine Chemical Index of Alteration, with values ranging from 59-67. These CIA estimates tend to be quite low compared to CIA estimates determined from previous work using bulk elemental abundances by WDS-XRF (CIA >67). Further interrogation of these preliminary results revealed that interphase quartz-illite analyses were assigned a potassium feldspar interpretation. Ultimately we will combine image analysis (e.g., particle shape/habit) with new reference spectra for paleosol interphase matrix material, which together with WDS-XRF and XRD mineralogy calibration can be used to develop a robust methodology for automated mineralogy analysis of paleosols.
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This content will become publicly available on July 9, 2026
Leveraging automated mineralogy analysis to assess paleosol chemical weathering
Paleosol weathering commonly is characterized by an index of alteration determined from bulk rock elemental abundances. Although a variety of indices exist, they all essentially compare the proportion of immobile major elemental oxides associated with refractory minerals (mainly Al2O3 in phyllosilicate clays) to mobile major elements associated with labile minerals (i.e., CaO, Na2O). Higher proportions of immobile major elemental oxides reflects more intense chemical weathering, which is used to infer warmer, wetter climate conditions. However, bulk rock alteration indices are known to be influenced by variability in particle size, effects of quartz dilution, and authigenic mineralization, which are challenging to account for when destructive analytical approaches are used. We tested a non-destructive method of assessing chemical weathering intensity using automated mineralogy analysis, which relies on SEM BSE imaging and EDS spectrum acquisition with automated matching of whole spectra to reference spectra to generate quantitative mineralogy estimates. Our case study focused on 9 Upper Pennsylvanian Spodosol samples from the Appalachian basin, and the dominant mineralogic group identified by automated mineralogy in all samples were phyllosilicate clays (50-85%). Six samples showed >20% feldspar: however, grain shape analysis indicates often micron-scale clay-sized grains with prismatic crystal habit were assigned a potassium feldspar spectra. This is due to the automated mineralogy algorithm identifying a mixed signal from sub-micron quartz/amorphous silica and illite in the X-ray volume as a feldspar x-ray spectrum. Furthermore, the Chemical Index of Alteration calculated from bulk elemental estimates of automated mineralogy results ranged from ~60-65, and A-CN-K ternary plots indicate samples were influenced by K-metasomatism. Using an algorithm that assigns automated mineral identification on the basis of both whole-spectrum matching and particle morphology attributes (including grain shape/crystal habit and size), we can better constrain mineralogical interpretation of fine-grained sedimentary lithologies. This allows automated mineralogy analysis to be used in a new approach for assessing weathering intensity by analyzing the proportion of mobile versus immobile elements in prismatic grains versus equant grains across a distribution of grain sizes in a way that can mitigate or minimize known limitations to the accuracy of alteration indices (e.g., authigenic skins, K-metasomatism).
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- Award ID(s):
- 2219079
- PAR ID:
- 10617811
- Publisher / Repository:
- Goldschmidt 2025
- Date Published:
- Format(s):
- Medium: X
- Location:
- Prauge, Czech Republic
- Sponsoring Org:
- National Science Foundation
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