Signal preprocessing of deep-sea laser-induced plasma spectra for identification of pelletized hydrothermal deposits using Artificial Neural Networks
Signal preprocessing of deep-sea laser-induced plasma spectra for identification of pelletized hydrothermal deposits using Artificial Neural Networks
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DOI：
10.1016/j.sab.2018.03.015
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
Soichi Yoshino;B. Thornton;Tomoko Takahashi;Y. Takaya;T. Nozaki
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作者：
Soichi Yoshino;B. Thornton;Tomoko Takahashi;Y. Takaya;T. Nozaki
本研究探讨的方法来分析激光诱导击穿光谱（LIBS）信号产生的水浸深海热液矿床照射长脉冲（>100 ns），使用人工神经网络（ANN）进行分析。人工神经网络需要大量的训练数据才能有效。出于这个原因，我们提出了将全场光谱信号预处理为人工神经网络适当形式的方法，人工增加了训练数据量。ANN的训练使用的数据集的信号从浸渍的造粒热液存款样品，使用所提出的方法进行预处理。该方法将识别准确率从82.5%提高到90.1%，并显著提高了学习速度。结果表明，人工神经网络可以用来构建一个通用的方法来识别热液矿床的长脉冲水下LIBS信号，而不需要显式的峰值检测。
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.