Quantitative in situ mapping of elements in deep-sea hydrothermal vents using laser-induced breakdown spectroscopy and multivariate analysis
Quantitative in situ mapping of elements in deep-sea hydrothermal vents using laser-induced breakdown spectroscopy and multivariate analysis
This study describes a method to quantify the chemical composition of deep-sea hydrothermal deposits in situ using laser-induced breakdown spectroscopy (LIBS). Partial least squares (PLS) regression analysis is applied to spectra obtained using a long laser pulse with a duration of 150 ns. The number of measurements needed to address the spatial heterogeneity of samples is determined through high-resolution mapping of the elemental distribution in rock samples. PLS applied to laboratory measured seawater-submerged samples achieved an average relative error (RE) of 25% for Cu, Pb, and Zn compared to benchmark concentration values in cross-validation and validation studies, where both the benchmark concentration values and LIBS spectral data are made available with this publication. The PLS model was applied to LIBS signals obtained in situ from hydrothermal deposits at 1000 m depth in the ocean. The results show that target inhomogeneity limits the accuracy of the surface LIBS measurements compared to benchmark values from bulk analysis of samples. Making multiple measurements with small position offsets at each location improves the accuracy of estimates compared to an equivalent number of measurements at a single position. Maps of element distribution generated using quantified in situ data demonstrate how chemical survey outputs can be generated by combining LIBS with multivariate analysis. This enables real-time chemical feedback during deep-sea operations and chemical surveys in situations or with platforms where sample recovery is not possible.
Deep-sea explorations, In situ chemical analysis, Laser-induced breakdown spectroscopy (LIBS), Multivariate analysis, Partial least squares regression analysis, Seafloor mineral resources
Takahashi, Tomoko
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Thornton, Blair
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Yoshino, Soichi
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Sakka, Tetsuo
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Ohki, Koichi
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Ohki, Toshihiko
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Nozaki, Tatsuo
72088ad3-6a8e-44b5-8192-968b3530a545
Takaya, Yutaro
5d136173-0577-4dfe-9d9e-6e20a64606c0
April 2020
Takahashi, Tomoko
937057f6-8e83-4a7f-b11f-b549c94afdf6
Thornton, Blair
8293beb5-c083-47e3-b5f0-d9c3cee14be9
Yoshino, Soichi
4da2ce3b-a3f1-498f-b940-90b39580e99c
Sakka, Tetsuo
fd41ffee-1abd-46cb-99dc-94bb96aecc38
Ohki, Koichi
0591f5f7-dcf3-45b0-a6ee-9b026b7b82a6
Ohki, Toshihiko
e8962f22-9e79-46b8-8b97-8a99c3dc47d4
Nozaki, Tatsuo
72088ad3-6a8e-44b5-8192-968b3530a545
Takaya, Yutaro
5d136173-0577-4dfe-9d9e-6e20a64606c0
Takahashi, Tomoko, Thornton, Blair, Yoshino, Soichi, Sakka, Tetsuo, Ohki, Koichi, Ohki, Toshihiko, Nozaki, Tatsuo and Takaya, Yutaro
(2020)
Quantitative in situ mapping of elements in deep-sea hydrothermal vents using laser-induced breakdown spectroscopy and multivariate analysis.
Deep Sea Research Part I: Oceanographic Research Papers, 158, [103232].
(doi:10.1016/j.dsr.2020.103232).
Abstract
This study describes a method to quantify the chemical composition of deep-sea hydrothermal deposits in situ using laser-induced breakdown spectroscopy (LIBS). Partial least squares (PLS) regression analysis is applied to spectra obtained using a long laser pulse with a duration of 150 ns. The number of measurements needed to address the spatial heterogeneity of samples is determined through high-resolution mapping of the elemental distribution in rock samples. PLS applied to laboratory measured seawater-submerged samples achieved an average relative error (RE) of 25% for Cu, Pb, and Zn compared to benchmark concentration values in cross-validation and validation studies, where both the benchmark concentration values and LIBS spectral data are made available with this publication. The PLS model was applied to LIBS signals obtained in situ from hydrothermal deposits at 1000 m depth in the ocean. The results show that target inhomogeneity limits the accuracy of the surface LIBS measurements compared to benchmark values from bulk analysis of samples. Making multiple measurements with small position offsets at each location improves the accuracy of estimates compared to an equivalent number of measurements at a single position. Maps of element distribution generated using quantified in situ data demonstrate how chemical survey outputs can be generated by combining LIBS with multivariate analysis. This enables real-time chemical feedback during deep-sea operations and chemical surveys in situations or with platforms where sample recovery is not possible.
Text
Takahashi_DSR_2020
- Accepted Manuscript
More information
Submitted date: 21 January 2020
Accepted/In Press date: 25 January 2020
e-pub ahead of print date: 27 February 2020
Published date: April 2020
Additional Information:
Funding Information:
The project is funded by the Japanese Ministry of Education, Culture, Sports, Science and Technology under the Program for the development of fundamental tools for the utilization of marine resources. We gratefully acknowledge the support from members of the steering panel; T. Takeuchi, T. Fujii, K. Iizasa, the late K. Tamaki, T. Ura, H. Sugimatsu, T. Yamamoto, K. Okino and J. Ishibashi. The study was supported in part by the JSPS Grant-in-Aid for JSPS Fellows (No. 268488 ). The authors thank the Hyper-Dolphin 3000 team and R/V Natsushima and R/V Shinseimaru crew for their assistance during the NT16-01 and KS-18-J03 cruises. We thank A. Matsumoto, M. Yelameli and S. Miyagawa for their support collecting and analysing samples and their help during experiments with ChemiCam, and T. Sato and K. Yasukawa for discussions and advice for analytical methods. The cross-validation and validation dataset, composition data for all measured elements, and raw spectra with calibration data are released with this publication for future benchmark studies: https://github.com/ocean-perception/chemicam_open_database .
Publisher Copyright:
© 2020 The Author(s)
Keywords:
Deep-sea explorations, In situ chemical analysis, Laser-induced breakdown spectroscopy (LIBS), Multivariate analysis, Partial least squares regression analysis, Seafloor mineral resources
Identifiers
Local EPrints ID: 437906
URI: http://eprints.soton.ac.uk/id/eprint/437906
ISSN: 0967-0637
PURE UUID: 6b1a37f8-6835-4ff3-8950-42e72c4e6c7e
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Date deposited: 24 Feb 2020 17:30
Last modified: 16 Mar 2024 06:26
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Contributors
Author:
Tomoko Takahashi
Author:
Soichi Yoshino
Author:
Tetsuo Sakka
Author:
Koichi Ohki
Author:
Toshihiko Ohki
Author:
Tatsuo Nozaki
Author:
Yutaro Takaya
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