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

研究成果: Article

9 引用 (Scopus)

抜粋

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.

元の言語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
出版物ステータスPublished - 2018 7

ASJC Scopus subject areas

  • Analytical Chemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Spectroscopy

フィンガープリント Signal preprocessing of deep-sea laser-induced plasma spectra for identification of pelletized hydrothermal deposits using Artificial Neural Networks' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

  • これを引用