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Communication Dans Un Congrès Année : 2021

Initial Major Element Quantification Using SuperCam Laser Induced Breakdown Spectroscopy

Ryan Anderson
  • Fonction : Auteur
Olivier Forni
  • Fonction : Auteur
Jens Frydenvang
  • Fonction : Auteur
Agnes Cousin
Samuel Clegg
  • Fonction : Auteur
Roger Wiens
  • Fonction : Auteur
Chip Legett
  • Fonction : Auteur
Paolo Pilleri
  • Fonction : Auteur
Sylvestre Maurice
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Gorka Arana
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Olivier Beyssac
Bruno Bousquet
  • Fonction : Auteur
Baptiste Chide
  • Fonction : Auteur
Elise Clave
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Erwin Dehouck
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Dorothea Delapp
  • Fonction : Auteur
Ari Essunfeld
  • Fonction : Auteur
Thierry Fouchet
Travis Gabriel
  • Fonction : Auteur
Cristina Garcia-Florentino
  • Fonction : Auteur
Olivier Gasnault
Erin Gibbons
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Javier Laserna
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Jeremie Lasue
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Jose Manrique
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Juan Manuel Madariaga
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Raymond Newell
  • Fonction : Auteur
Ann Ollila
Susanne Schroder
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Shiv Sharma
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Justin Simon
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Pablo Sobron
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David Vogt
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Résumé

SuperCam uses Laser Induced Breakdown Spectroscopy (LIBS) to collect atomic emission spectra from targets up to ~7 meters from the Perseverance rover. Due to the complexity of LIBS physics and the diversity of geologic materials, we use an empirical approach to major element (SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, K2O) quantification, based on a suite of 1198 SuperCam laboratory spectra of 334 standards, including the rover calibration targets. SuperCam LIBS spectra are pre-processed by subtracting "dark" (passive/non-LIBS) spectra, denoising, continuum removal, instrument response correction, conversion to radiance, and wavelength calibration. For quantification, the spectra are masked to remove noisy sections of the spectrum and normalized by dividing signal in each spectrometer by the total signal from that spectrometer. We also found that the additional preprocessing steps of peak binning and/or per-channel standardization improved the results in some cases. These data are used to train multivariate regression models, with parameters optimized using cross-validation to avoid overfitting. We considered a variety of regression algorithms including Partial Least Squares (PLS), Least Absolute Selection and Shrinkage Operator (LASSO), Ridge, Elastic Net, Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting Regression (GBR), Local Elastic Net, and blended sub-models. Models were selected based on test-set performance, accuracy of predictions of the onboard calibration targets, comparison of Mars and laboratory spectra, and the geochemical plausibility of Mars results. In some cases we found that the average of the predictions of several algorithms gave better results than any single method. Accuracy of predictions is estimated as the root mean squared error of prediction (RMSEP) for the test set. As additional spectra are collected from Mars, we continue to validate and improve upon this initial SuperCam elemental quantification. Areas of investigation include calibration transfer, probabilistic regression methods, and regression models for additional elements.Figure 1: Test set predictions vs actual compositions for each major element. Perfect predictions would fall on the line. RMSEP measures the accuracy of the model in wt.%.
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Dates et versions

obspm-03903783 , version 1 (16-12-2022)

Identifiants

Citer

Ryan Anderson, Olivier Forni, Jens Frydenvang, Agnes Cousin, Samuel Clegg, et al.. Initial Major Element Quantification Using SuperCam Laser Induced Breakdown Spectroscopy. AGU Fall Meeting 2021, Dec 2021, Nouvelle-Orléans, United States. ⟨obspm-03903783⟩
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