Towards in-situ chemical classification of seafloor deposits: application of Neural Networks to underwater Laser-induced breakdown spectroscopy
Towards in-situ chemical classification of seafloor deposits: application of Neural Networks to underwater Laser-induced breakdown spectroscopy
Laser-induced breakdown spectroscopy (LIBS) is a form of chemical analysis that can determine the elemental composition of targets. LIBS has been used for underwater exploration, at over 1000 m depth in active vent fields, and it can potentially form the basis of an efficient screening method prior to detailed sampling or boring surveys. In this study, a method for in-situ chemical classification of hydrothermal deposits, based on metallic element compositions using Cu-Pb-Zn ternary diagrams, is developed that uses Artificial Neural Networks (ANNs). ANNs have the advantage of being able to describe non-linear properties, which is relevant when modeling the spectra fluctuations associated with underwater LIBS. We analyzed the effect of database size and constitution on the classification results with spectra that were both simulated and measured in the laboratory. In our future work, we develop classifiers that use other elements, and extend this method for multi-elemental quantitative analysis.
Yoshino, Soichi
4da2ce3b-a3f1-498f-b940-90b39580e99c
Thornton, Blair
8293beb5-c083-47e3-b5f0-d9c3cee14be9
Takahashi, Tomoko
937057f6-8e83-4a7f-b11f-b549c94afdf6
Yoshino, Soichi
4da2ce3b-a3f1-498f-b940-90b39580e99c
Thornton, Blair
8293beb5-c083-47e3-b5f0-d9c3cee14be9
Takahashi, Tomoko
937057f6-8e83-4a7f-b11f-b549c94afdf6
Yoshino, Soichi, Thornton, Blair and Takahashi, Tomoko
(2017)
Towards in-situ chemical classification of seafloor deposits: application of Neural Networks to underwater Laser-induced breakdown spectroscopy.
In Oceans 2017 - Aberdeen.
IEEE.
5 pp
.
(In Press)
(doi:10.1109/OCEANSE.2017.8084734).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Laser-induced breakdown spectroscopy (LIBS) is a form of chemical analysis that can determine the elemental composition of targets. LIBS has been used for underwater exploration, at over 1000 m depth in active vent fields, and it can potentially form the basis of an efficient screening method prior to detailed sampling or boring surveys. In this study, a method for in-situ chemical classification of hydrothermal deposits, based on metallic element compositions using Cu-Pb-Zn ternary diagrams, is developed that uses Artificial Neural Networks (ANNs). ANNs have the advantage of being able to describe non-linear properties, which is relevant when modeling the spectra fluctuations associated with underwater LIBS. We analyzed the effect of database size and constitution on the classification results with spectra that were both simulated and measured in the laboratory. In our future work, we develop classifiers that use other elements, and extend this method for multi-elemental quantitative analysis.
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Accepted/In Press date: 2017
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Local EPrints ID: 414219
URI: http://eprints.soton.ac.uk/id/eprint/414219
PURE UUID: 5a6e6b3e-0655-40ec-a481-d437f0167cd1
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Date deposited: 19 Sep 2017 16:31
Last modified: 15 Mar 2024 16:02
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Author:
Soichi Yoshino
Author:
Tomoko Takahashi
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