Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/64457
Título
Machine learning methods applied to combined Raman and LIBS spectra: Implications for mineral discrimination in planetary missions
Autor
Año del Documento
2023
Editorial
Wiley
Descripción
Producción Científica
Documento Fuente
Journal of Raman Spectroscopy, 2023, vol. 54, n. 11, p. 1353-1366
Abstract
The combined analysis of geological targets by complementary spectroscopic techniques could enhance the characterization of the mineral phases found on Mars. This is indeed the case with the SuperCam instrument onboard the Perseverance rover. In this framework, the present study seeks to evaluate and compare multiple machine learning techniques for the characterization of carbonate minerals based on Raman-LIBS (Laser-Induced Breakdown Spectroscopy) spectroscopic data. To do so, a Ca-Mg prediction curve was created by mixing hydromagnesite and calcite at different concentration ratios. After their characterization by Raman and LIBS spectroscopy, different multivariable machine learning (Gaussian process regression, support vector machines, ensembles of trees, and artificial neural networks) were used to predict the concentration ratio of each sample from their respective datasets. The results obtained by separately analyzing Raman and LIBS data were then compared to those obtained by combining them. By comparing their performance, this work demonstrates that mineral discrimination based on Gaussian and ensemble methods optimized the combine of Raman-LIBS dataset outperformed those ensured by Raman and LIBS data alone. This demonstrated that the fusion of data combination and machine learning is a promising approach to optimize the analysis of spectroscopic data returned from Mars.
Materias Unesco
25 Ciencias de la Tierra y del Espacio
Palabras Clave
Data combination
LIBS
Machine learning
PCA
Raman
ISSN
0377-0486
Revisión por pares
SI
DOI
Patrocinador
Agencia Estatal de Investigación, grant (PID2022-142490OB-C32)
Ministerio de Economía y Competitividad (MINECO),Grant/Award Number (RDE2018-102600-T)
Ministerio de Economía y Competitividad (MINECO),Grant/Award Number (RDE2018-102600-T)
Propietario de los Derechos
© 2023 The Author(s)
Idioma
eng
Tipo de versión
info:eu-repo/semantics/publishedVersion
Derechos
openAccess
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