Machine learning for automatic identification of new minor species

Schmidt, Frédéric; Mermy, Guillaume Cruz; Erwin, Justin; Robert, Séverine; Neary, Lori; Thomas, Ian R.; Daerden, Frank; Ristic, Bojan; Patel, Manish R.; Bellucci, Giancarlo; Lopez-Moreno, Jose-Juan and Vandaele, Ann-Carine (2021). Machine learning for automatic identification of new minor species. Journal of Quantitative Spectroscopy and Radiative Transfer, 259, article no. 107361.

DOI: https://doi.org/10.1016/j.jqsrt.2020.107361

Abstract

One of the main difficulties to analyze modern spectroscopic datasets is due to the extremely large amount of data. For example, in atmospheric transmittance spectroscopy, the solar occultation channel (SO) of the NOMAD instrument onboard the ESA ExoMars2016 satellite called Trace Gas Orbiter (TGO) had produced  ∼ 10 millions of spectra in  ∼ 20000 acquisition sequences since the beginning of the mission in April 2018 until 15 January 2020. Other datasets are even larger with  ∼ billions of spectra for OMEGA onboard Mars Express or CRISM onboard Mars Reconnaissance Orbiter. Usually, new lines are discovered after a long iterative process of model fitting and manual residual analysis. Here we propose a new method based on unsupervised machine learning, to automatically detect new minor species. Although precise quantification is out of scope, this tool can also be used to quickly summarize the dataset, by giving few endmembers (”source”) and their abundances.

The methodology is the following: we proposed a way to approximate the dataset non-linearity by a linear mixture of abundance and source spectra (endmembers). We used unsupervised source separation in form of non-negative matrix factorization to estimate those quantities. Several methods are tested on synthetic and simulation data. Our approach is dedicated to detect minor species spectra rather than precisely quantifying them. On synthetic example, this approach is able to detect chemical compounds present in form of 100 hidden spectra out of 10⁴, at 1.5 times the noise level. Results on simulated spectra of NOMAD-SO targeting CH₄ show that detection limits goes in the range of 100-500 ppt in favorable conditions. Results on real martian data from NOMAD-SO show that CO₂ and H₂O are present, as expected, but CH₄ is absent. Nevertheless, we confirm a set of new unexpected lines in the database, attributed by ACS instrument Team to the CO₂ magnetic dipole.

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About

• Item ORO ID
• 72579
• Item Type
• Journal Item
• ISSN
• 0022-4073
• Project Funding Details

• Funded Project Name	Project ID	Funding Body
  NOMAD	Not Set	Not Set
  Science operations for UVIS and CaSSIS on the ExoMars Trace Gas Orbiter	ST/R005761/1	UKSA UK Space Agency
  Modelling and retrieval of martian dust, ice and ozone from ExoMars NOMAD data	ST/P001262/1	UKSA UK Space Agency
  Characterizing the Martian water cycle by assimilating ExoMars 2016 Trace Gas Orbiter data	ST/R001405/1	UKSA UK Space Agency

• Keywords
• Spectroscopy; Atmosphere; Data mining; Machine learning; Unsupervised; Source separation; Non-negative matrix factorization
• Academic Unit or School
• Faculty of Science, Technology, Engineering and Mathematics (STEM) > Physical Sciences
  Faculty of Science, Technology, Engineering and Mathematics (STEM)
• Copyright Holders
• © 2020 Elsevier Ltd.
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• Depositing User
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