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 (2020). Machine Learning for automatic identification of new minor species. Journal of Quantitative Spectroscopy and Radiative Transfer (Early Access).

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.

Viewing alternatives

Metrics

Public Attention

Altmetrics from Altmetric

Number of Citations

Citations from Dimensions

Item Actions

Export

About

  • Item ORO ID
  • 72579
  • Item Type
  • Journal Item
  • ISSN
  • 0022-4073
  • Project Funding Details
  • Funded Project NameProject IDFunding Body
    NOMADNot SetNot Set
    Science operations for UVIS and CaSSIS on the ExoMars Trace Gas OrbiterST/R005761/1UKSA UK Space Agency
    Modelling and retrieval of martian dust, ice and ozone from ExoMars NOMAD dataST/P001262/1UKSA UK Space Agency
    Characterizing the Martian water cycle by assimilating ExoMars 2016 Trace Gas Orbiter dataST/R001405/1UKSA 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.
  • SWORD Depositor
  • Jisc Publications-Router
  • Depositing User
  • Jisc Publications-Router

Recommendations