Quantifying calcium carbonate and organic carbon content in marine sediments from XRF-scanning spectra with a machine learning approach
Geochemical variations of sedimentary records contain vital information for understanding paleoenvironment and paleoclimate. However, to obtain quantitative data in the laboratory is laborious, which ultimately restricts the temporal and spatial resolution. Quantification based on fast-acquisition a...
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Springer Science and Business Media LLC
2022
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ftawi:oai:epic.awi.de:57599 2023-07-16T04:00:59+02:00 Quantifying calcium carbonate and organic carbon content in marine sediments from XRF-scanning spectra with a machine learning approach Lee, An-Sheng Chao, Weng-Si Liou, Sofia Ya Hsuan Tiedemann, Ralf Zolitschka, Bernd Lembke-Jene, Lester 2022-12-02 application/pdf https://epic.awi.de/id/eprint/57599/ https://epic.awi.de/id/eprint/57599/1/s41598-022-25377-x.pdf https://hdl.handle.net/10013/epic.23bed104-719f-4e35-8fc4-6b509655ae3d unknown Springer Science and Business Media LLC https://epic.awi.de/id/eprint/57599/1/s41598-022-25377-x.pdf Lee, A. S. , Chao, W. S. orcid:0000-0002-8218-7598 , Liou, S. Y. H. , Tiedemann, R. orcid:0000-0001-7211-8049 , Zolitschka, B. and Lembke-Jene, L. orcid:0000-0002-6873-8533 (2022) Quantifying calcium carbonate and organic carbon content in marine sediments from XRF-scanning spectra with a machine learning approach , Scientific Reports, 12 (1) . doi:10.1038/s41598-022-25377-x <https://doi.org/10.1038/s41598-022-25377-x> , hdl:10013/epic.23bed104-719f-4e35-8fc4-6b509655ae3d EPIC3Scientific Reports, Springer Science and Business Media LLC, 12(1), 11 p., ISSN: 2045-2322 Article isiRev 2022 ftawi https://doi.org/10.1038/s41598-022-25377-x 2023-06-25T23:20:01Z Geochemical variations of sedimentary records contain vital information for understanding paleoenvironment and paleoclimate. However, to obtain quantitative data in the laboratory is laborious, which ultimately restricts the temporal and spatial resolution. Quantification based on fast-acquisition and high-resolution provides a potential solution but is restricted to qualitative X-ray fluorescence (XRF) core scanning data. Here, we apply machine learning (ML) to advance the quantification progress and target calcium carbonate (CaCO3) and total organic carbon (TOC) for quantification to test the potential of such an XRF-ML approach. Raw XRF spectra are used as input data instead of software-based extraction of elemental intensities to avoid bias and increase information. Our dataset comprises Pacific and Southern Ocean marine sediment cores from high- to mid-latitudes to extend the applicability of quantification models from a site-specific to a multi-regional scale. ML-built models are carefully evaluated with a training set, a test set and a case study. The acquired ML-models provide better results with R2 of 0.96 for CaCO3 and 0.78 for TOC than conventional methods. In our case study, the ML-performance for TOC is comparably lower but still provides potential for future optimization. Altogether, this study allows to conveniently generate high-resolution bulk chemistry records without losing accuracy. Article in Journal/Newspaper Southern Ocean Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center) Pacific Southern Ocean Scientific Reports 12 1 |
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Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center) |
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ftawi |
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description |
Geochemical variations of sedimentary records contain vital information for understanding paleoenvironment and paleoclimate. However, to obtain quantitative data in the laboratory is laborious, which ultimately restricts the temporal and spatial resolution. Quantification based on fast-acquisition and high-resolution provides a potential solution but is restricted to qualitative X-ray fluorescence (XRF) core scanning data. Here, we apply machine learning (ML) to advance the quantification progress and target calcium carbonate (CaCO3) and total organic carbon (TOC) for quantification to test the potential of such an XRF-ML approach. Raw XRF spectra are used as input data instead of software-based extraction of elemental intensities to avoid bias and increase information. Our dataset comprises Pacific and Southern Ocean marine sediment cores from high- to mid-latitudes to extend the applicability of quantification models from a site-specific to a multi-regional scale. ML-built models are carefully evaluated with a training set, a test set and a case study. The acquired ML-models provide better results with R2 of 0.96 for CaCO3 and 0.78 for TOC than conventional methods. In our case study, the ML-performance for TOC is comparably lower but still provides potential for future optimization. Altogether, this study allows to conveniently generate high-resolution bulk chemistry records without losing accuracy. |
format |
Article in Journal/Newspaper |
author |
Lee, An-Sheng Chao, Weng-Si Liou, Sofia Ya Hsuan Tiedemann, Ralf Zolitschka, Bernd Lembke-Jene, Lester |
spellingShingle |
Lee, An-Sheng Chao, Weng-Si Liou, Sofia Ya Hsuan Tiedemann, Ralf Zolitschka, Bernd Lembke-Jene, Lester Quantifying calcium carbonate and organic carbon content in marine sediments from XRF-scanning spectra with a machine learning approach |
author_facet |
Lee, An-Sheng Chao, Weng-Si Liou, Sofia Ya Hsuan Tiedemann, Ralf Zolitschka, Bernd Lembke-Jene, Lester |
author_sort |
Lee, An-Sheng |
title |
Quantifying calcium carbonate and organic carbon content in marine sediments from XRF-scanning spectra with a machine learning approach |
title_short |
Quantifying calcium carbonate and organic carbon content in marine sediments from XRF-scanning spectra with a machine learning approach |
title_full |
Quantifying calcium carbonate and organic carbon content in marine sediments from XRF-scanning spectra with a machine learning approach |
title_fullStr |
Quantifying calcium carbonate and organic carbon content in marine sediments from XRF-scanning spectra with a machine learning approach |
title_full_unstemmed |
Quantifying calcium carbonate and organic carbon content in marine sediments from XRF-scanning spectra with a machine learning approach |
title_sort |
quantifying calcium carbonate and organic carbon content in marine sediments from xrf-scanning spectra with a machine learning approach |
publisher |
Springer Science and Business Media LLC |
publishDate |
2022 |
url |
https://epic.awi.de/id/eprint/57599/ https://epic.awi.de/id/eprint/57599/1/s41598-022-25377-x.pdf https://hdl.handle.net/10013/epic.23bed104-719f-4e35-8fc4-6b509655ae3d |
geographic |
Pacific Southern Ocean |
geographic_facet |
Pacific Southern Ocean |
genre |
Southern Ocean |
genre_facet |
Southern Ocean |
op_source |
EPIC3Scientific Reports, Springer Science and Business Media LLC, 12(1), 11 p., ISSN: 2045-2322 |
op_relation |
https://epic.awi.de/id/eprint/57599/1/s41598-022-25377-x.pdf Lee, A. S. , Chao, W. S. orcid:0000-0002-8218-7598 , Liou, S. Y. H. , Tiedemann, R. orcid:0000-0001-7211-8049 , Zolitschka, B. and Lembke-Jene, L. orcid:0000-0002-6873-8533 (2022) Quantifying calcium carbonate and organic carbon content in marine sediments from XRF-scanning spectra with a machine learning approach , Scientific Reports, 12 (1) . doi:10.1038/s41598-022-25377-x <https://doi.org/10.1038/s41598-022-25377-x> , hdl:10013/epic.23bed104-719f-4e35-8fc4-6b509655ae3d |
op_doi |
https://doi.org/10.1038/s41598-022-25377-x |
container_title |
Scientific Reports |
container_volume |
12 |
container_issue |
1 |
_version_ |
1771550383157542912 |