DOI: 10.5281/zenodo.15543201
title: Supplementary Materials and Supplementary Text for the Submission "Automated Detection and Chemical Characterization of Anomalous Grains in Scientific Ocean Drilling Legacy Cores"

description: This is a zipped version of the supplementary material for a manuscript draft, which will be submitted to Climate of the Past. As the dataset is extensive, the journal requires submitting all supplementary data to a repository before submitting the manuscript. Below you will find the abstract of the currently valid draft of the manuscript before submission:

[Submitted Manuscript]Automated Detection and Chemical Characterization of Anomalous Grains in Scientific Ocean Drilling Legacy Cores

Gerald Auer1, David De Vleeschouwer2, Arisa Seki3, Anna Joy Drury4,5, Yusuke Kubo6, Minoru Ikehara7, Junichiro Kuroda8, and the ReC23-01 scientists*

1Department of Earth Sciences, NAWI Graz Geocenter, University of Graz, Graz, 8010, Austria

2Institute of Geology and Palaeontology, University of Münster, Münster, Germany 

3Faculty of Science, Shinshu University, Matsumoto, Nagano, Japan (now at Fukada Geological Institute, Bunkyo-ku, Tokyo, Japan)

4School of Geography, Geology, and the Environment, University of Leicester, Leicester, UK

5Department of Earth Sciences, University College London, London, UK

6Kochi Institute for Core Sample Research, Japan Agency for Marine-Earth Science and Technology, Kochi, Japan

7Marine Core Research Institute, Kochi University, Kochi, Japan

8Atmosphere and Ocean Research Institute, The University of Tokyo, Kashiwa, Chiba, Japan

*see team list for all team authors and their affiliations

Correspondence to: Gerald Auer (gerald.auer@uni-graz.at)

Abstract. The dynamic behavior of glaciers under varying climatic conditions plays a crucial role in Earth's climate systems, necessitating reliable records of glacial extent throughout the Cenozoic era. Ice-rafted debris (IRD) provides insights into past iceberg activity, glacial erosion rates, sediment transport, and meltwater delivery. Polar IRD reconstructions can enhance our understanding of ice sheet responses to climate shifts, however, traditional methods for detecting IRD are often destructive and labor-intensive. This study proposes a new methodological framework for detecting IRD in marine sediments as a proxy for reconstructing polar paleoclimate variability and glacial dynamics. Our new multi-proxy approach utilizes micro-X-ray fluorescence (μXRF) 2D imaging and computed tomography (CT) 3D scanning on five Deep Sea Drilling Project (DSDP) Site 266 archive half-sections. Our findings reveal that non-destructive scanning techniques have significant potential to effectively identify and quantify IRD, with μXRF providing high-resolution geochemical mapping and CT imaging enabling three-dimensional visualization of sediment structures. Machine learning classification of CT data using the Waikato Environment for Knowledge Analysis (WEKA) in the Fiji software package is a powerful tool for fast detection of anomalous components in IODP cores. However, we established that the finding of such coupled machine learning-CT approaches must be ground-truthed to ensure accuracy: our application of automated (k-means cluster analyses) chemical fingerprinting based on the μXRF data revealed many of the grains identified in the CT classification were false positives due to contamination from drill pipe flakes. We confirmed this contamination using scanning electron microscopy energy dispersive spectroscopy (SEM-EDS) on targeted samples. Fundamentally, our multi-proxy approach underscores that this approach alone cannot provide the geochemical fingerprinting necessary for unequivocal IRD identification. We therefore recommend a combined approach applying μXRF for geochemical fingerprinting and CT for structural detection to ensure a comprehensive, non-destructive method for IRD verification and preliminary provenance analysis. Ultimately, our work underscores the importance of applying stringent ground truthing to machine learning and high-resolution image datasets to ensure the data quality necessary for robust palaeoclimate reconstructions.

 

Keywords: Ice-rafted debris, Machine-learning in Geosciences, Non-destructive paleoclimate proxies, Marine sediment cores.