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

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1-7
Number of pages7
JournalSpectrochimica Acta - Part B Atomic Spectroscopy
Volume145
DOIs
Publication statusPublished - 2018 Jul

Keywords

  • Artificial Neural Networks (ANNs)
  • Chemical analysis
  • Laser-induced breakdown spectroscopy (LIBS)
  • Signal processing

ASJC Scopus subject areas

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

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