期刊
SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY
卷 171, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.sab.2020.105930
关键词
LIBS; PLS; Artificial neural network; ChemCam; SuperCam
类别
资金
- National Aeronautics and Space Administration (NASA) [NNX15AP84A]
- NASA Mars Exploration Program [NNH13ZDA018O]
Due to its relatively simple and versatile nature, laser-induced breakdown spectroscopy experiments can yield enormous amount of data that normally needs to be preprocessed to remove background signal, electron continuum, and noise, and for some applications, correct for the instrument response function and normalize the signal prior to conducting spectroscopic analysis. In experiments where the focus is on the analysis of samples of similar composition, preprocessing can be repetitive and tedious. We show that preprocessing of such LIBS data can be performed in an automated or semi-automated manner using machine learning tools. To demonstrate this approach, we apply partial least squares regression and artificial neural networks on two laser-induced breakdown spectra datasets. The first dataset is used to develop predictive models for abundances of various elements in geological samples analyzed by a laboratory model of ChemCam. The second dataset consists of spectra obtained from ChemCam as it interrogates Martian targets. We show that using the two machine learning techniques, we can predict the preprocessed spectra of samples with a relatively high accuracy for both datasets.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据