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  • CaCO3 content; machine learning; Marine Sediment Core; Pacific Ocean; Quantification; total organic carbon (TOC); XRF scanning  (1)
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    Publication Date: 2024-04-20
    Description: Thirty marine sediment cores were collected from the subarctic Northwest Pacific (cruise SO264 with R/V SONNE in 2018), the central Drake Passage (cruise PS97 with RV Polarstern in 2016), the Pacific sector of the Southern Ocean (cruise PS75 in 2009/2010) and the Okhotsk Sea (cruise KOMEX I and KOMEX II with R/V Akademik Lavrentyev in 1998 and cruise SO178 in 2004). The cover area is mainly spreading over the high- to mid-latitude Northwest Pacific (37°N-52°N) and the Pacific sector of the Southern Ocean (53°S-63°S), with a water depth coverage from 1211 m to 4853 m. These cores are dominated by pelagic sediments and mainly consist of calcareous and siliceous ooze and non-carbonaceous fine-grained sediments. The drilling locates at the pelagic ocean rather than the nearby area to the shore to avoid too much influence of coastal erosion and fluvial input. The age range of the majority of the sediments spans from Holocene to mid-Pleistocene. All XRF-scanning measurements were carried out with an Avaatech XRF core scanner at the AWI in Bremerhaven. The X-ray excitation scanning settings were 10 kV at 150 mA with no filter for a count time of 10 s, and a rhodium target X-ray tube was deployed. The sample distance was 10 mm, with a 10x12 mm slit size. The presented XRF data are the raw spectra instead of processed elemental profiles. The samples for bulk chemistry measurements were weighed before and after freeze-drying, homogenized and measured for TC and TOC by a CNS-analyzer (Elementar vario EL III) and a carbon/sulfur analyzer (Eltra CS-800) in the laboratories at AWI in Bremerhaven. The CaCO3 content was then calculated from TC and TOC. The high-resolution (10 mm) quantified TOC and CaCO3 contents are products of machine learning applications using the XRF data. The process please refers to the related publication.
    Keywords: CaCO3 content; machine learning; Marine Sediment Core; Pacific Ocean; Quantification; total organic carbon (TOC); XRF scanning
    Type: Dataset
    Format: application/zip, 3 datasets
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