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Conference Papers Year : 2021

Initial Major Element Quantification Using SuperCam Laser Induced Breakdown Spectroscopy

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

Abstract

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 and versions

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

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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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