From machine learning to transfer learning in laser-induced breakdown spectroscopy analysis of rocks for Mars exploration

Abstract With the ChemCam instrument, laser-induced breakdown spectroscopy (LIBS) has successively contributed to Mars exploration by determining the elemental compositions of soils, crusts, and rocks. The American Perseverance rover and the Chinese Zhurong rover respectively landed on Mars on Febru...

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Autores principales:	Chen Sun, Weijie Xu, Yongqi Tan, Yuqing Zhang, Zengqi Yue, Long Zou, Sahar Shabbir, Mengting Wu, Fengye Chen, Jin Yu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/86ed1e4b51e1418db67364c6f7dc0545
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spelling	oai:doaj.org-article:86ed1e4b51e1418db67364c6f7dc05452021-11-08T10:46:46ZFrom machine learning to transfer learning in laser-induced breakdown spectroscopy analysis of rocks for Mars exploration10.1038/s41598-021-00647-22045-2322https://doaj.org/article/86ed1e4b51e1418db67364c6f7dc05452021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-00647-2https://doaj.org/toc/2045-2322Abstract With the ChemCam instrument, laser-induced breakdown spectroscopy (LIBS) has successively contributed to Mars exploration by determining the elemental compositions of soils, crusts, and rocks. The American Perseverance rover and the Chinese Zhurong rover respectively landed on Mars on February 18 and May 15, 2021, further increase the number of LIBS instruments on Mars. Such an unprecedented situation requires a reinforced research effort on the methods of LIBS spectral data analysis. Although the matrix effects correspond to a general issue in LIBS, they become accentuated in the case of rock analysis for Mars exploration, because of the large variation of rock compositions leading to the chemical matrix effect, and the difference in surface physical properties between laboratory standards (in pressed powder pellet, glass or ceramic) used to establish calibration models and natural rocks encountered on Mars, leading to the physical matrix effect. The chemical matrix effect has been tackled in the ChemCam project with large sets of laboratory standards offering a good representation of various compositions of Mars rocks. The present work more specifically deals with the physical matrix effect which is still lacking a satisfactory solution. The approach consists in introducing transfer learning in LIBS data treatment. For the specific application of total alkali-silica (TAS) classification of rocks (either with a polished surface or in the raw state), the results show a significant improvement in the ability to predict of pellet-based models when trained together with suitable information from rocks in a procedure of transfer learning. The correct TAS classification rate increases from 25% for polished rocks and 33.3% for raw rocks with a machine learning model, to 83.3% with a transfer learning model for both types of rock samples.Chen SunWeijie XuYongqi TanYuqing ZhangZengqi YueLong ZouSahar ShabbirMengting WuFengye ChenJin YuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Chen Sun Weijie Xu Yongqi Tan Yuqing Zhang Zengqi Yue Long Zou Sahar Shabbir Mengting Wu Fengye Chen Jin Yu From machine learning to transfer learning in laser-induced breakdown spectroscopy analysis of rocks for Mars exploration
description	Abstract With the ChemCam instrument, laser-induced breakdown spectroscopy (LIBS) has successively contributed to Mars exploration by determining the elemental compositions of soils, crusts, and rocks. The American Perseverance rover and the Chinese Zhurong rover respectively landed on Mars on February 18 and May 15, 2021, further increase the number of LIBS instruments on Mars. Such an unprecedented situation requires a reinforced research effort on the methods of LIBS spectral data analysis. Although the matrix effects correspond to a general issue in LIBS, they become accentuated in the case of rock analysis for Mars exploration, because of the large variation of rock compositions leading to the chemical matrix effect, and the difference in surface physical properties between laboratory standards (in pressed powder pellet, glass or ceramic) used to establish calibration models and natural rocks encountered on Mars, leading to the physical matrix effect. The chemical matrix effect has been tackled in the ChemCam project with large sets of laboratory standards offering a good representation of various compositions of Mars rocks. The present work more specifically deals with the physical matrix effect which is still lacking a satisfactory solution. The approach consists in introducing transfer learning in LIBS data treatment. For the specific application of total alkali-silica (TAS) classification of rocks (either with a polished surface or in the raw state), the results show a significant improvement in the ability to predict of pellet-based models when trained together with suitable information from rocks in a procedure of transfer learning. The correct TAS classification rate increases from 25% for polished rocks and 33.3% for raw rocks with a machine learning model, to 83.3% with a transfer learning model for both types of rock samples.
format	article
author	Chen Sun Weijie Xu Yongqi Tan Yuqing Zhang Zengqi Yue Long Zou Sahar Shabbir Mengting Wu Fengye Chen Jin Yu
author_facet	Chen Sun Weijie Xu Yongqi Tan Yuqing Zhang Zengqi Yue Long Zou Sahar Shabbir Mengting Wu Fengye Chen Jin Yu
author_sort	Chen Sun
title	From machine learning to transfer learning in laser-induced breakdown spectroscopy analysis of rocks for Mars exploration
title_short	From machine learning to transfer learning in laser-induced breakdown spectroscopy analysis of rocks for Mars exploration
title_full	From machine learning to transfer learning in laser-induced breakdown spectroscopy analysis of rocks for Mars exploration
title_fullStr	From machine learning to transfer learning in laser-induced breakdown spectroscopy analysis of rocks for Mars exploration
title_full_unstemmed	From machine learning to transfer learning in laser-induced breakdown spectroscopy analysis of rocks for Mars exploration
title_sort	from machine learning to transfer learning in laser-induced breakdown spectroscopy analysis of rocks for mars exploration
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/86ed1e4b51e1418db67364c6f7dc0545
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From machine learning to transfer learning in laser-induced breakdown spectroscopy analysis of rocks for Mars exploration

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