Applying a wide and deep learning model to core-scan XRF data to reconstruct mineral assemblages for Pleistocene paleolake Olduvai, Tanzania

Mchenry LJ, Kodikara GR, Stanistreet IG, Stollhofen H, Njau J, Schick K, Toth N (2026)

Publication Type: Journal article

Publication year: 2026

Journal

Quaternary Research Cambridge University Press

DOI: 10.1017/qua.2025.10060

Abstract

Paleolake coring initiatives result in large datasets from various proxies taken at different resolutions, ranging from continuous scans to samples collected at coarser intervals. Higher-resolution data (e.g., core-scan X-ray fluorescence [XRF]) can detect short-duration changes in the paleolake and help identify unit boundaries with precision; however, interpreting the causes of such changes may require sampling and more intensive laboratory analysis like X-ray diffraction (XRD). This study applies a published wide and deep learning model, developed for the Olduvai Gorge Coring Project (OGCP) 2014 cores from the Pleistocene Olduvai basin, Tanzania, to reconstruct the mineral assemblages from saline-alkaline paleolake Olduvai using core-scan XRF data and core lithology. A classification model (predicting mineral presence or absence) and a regression model (predicting relative abundances of minerals) yielded predictions for two OGCP cores (2A and 3A), which were compared with published XRD mineral data and detailed core sedimentological descriptions. The models were excellent at identifying dolomite-rich layers, carbonate-rich intervals, intervals of sandstone within claystone, and altered tuffs within claystone and at predicting whether illitic or smectitic clays dominate. The models struggled with less-altered tuffs and with zeolites in non-tuff sediments, especially when XRD identified chabazite and erionite (rather than phillipsite) as the dominant, non-analcime zeolite.

Authors with CRIS profile

Harald Stollhofen Lehrstuhl für Geologie (Exogene Dynamik)

Involved external institutions

University of Wisconsin-Milwaukee US United States (USA) (US) The University of Liverpool GB United Kingdom (GB) Stone Age Institute US United States (USA) (US)

How to cite

APA:

Mchenry, L.J., Kodikara, G.R., Stanistreet, I.G., Stollhofen, H., Njau, J., Schick, K., & Toth, N. (2026). Applying a wide and deep learning model to core-scan XRF data to reconstruct mineral assemblages for Pleistocene paleolake Olduvai, Tanzania. Quaternary Research. https://doi.org/10.1017/qua.2025.10060

MLA:

Mchenry, Lindsay J., et al. "Applying a wide and deep learning model to core-scan XRF data to reconstruct mineral assemblages for Pleistocene paleolake Olduvai, Tanzania." Quaternary Research (2026).

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