Signal preprocessing of deep-sea laser-induced plasma spectra for identification of pelletized hydrothermal deposits using Artificial Neural Networks

Soichi Yoshino*, Blair Thornton, Tomoko Takahashi, Yutaro Takaya, Tatsuo Nozaki

*この研究の対応する著者

研究成果: Article査読

18 被引用数 (Scopus)

抄録

This study investigates methods to analyze Laser-induced breakdown spectroscopy (LIBS) signals generated from water immersed deep-sea hydrothermal deposits irradiated by a long pulse (>100 ns) that are analyzed using Artificial Neural Networks (ANNs). ANNs require large amounts of training data to be effective. For this reason, we propose methods to preprocess full-field spectral signals into an appropriate form for ANNs artificially increase the amount of training data. The ANN was trained using a dataset of signals from immersed pelletized hydrothermal deposit samples that were preprocessed using the proposed method. The proposed method improved the accuracy of identification from 82.5% to 90.1% and significantly increased the speed of learning. The result shows that the ANN can be used to construct a generic method to identify hydrothermal deposits by long pulse underwater LIBS signals without the need for explicit peak detection.

本文言語English
ページ(範囲)1-7
ページ数7
ジャーナルSpectrochimica Acta - Part B Atomic Spectroscopy
145
DOI
出版ステータスPublished - 2018 7月
外部発表はい

ASJC Scopus subject areas

  • 分析化学
  • 原子分子物理学および光学
  • 器械工学
  • 分光学

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