MULTIVARIATE AND ENSEMBLE MANGANESE CALIBRATION MODELS FOR SUPERCAM

Abstract

International audienceOperator (LASSO) multivariate techniques with blended submodels; similar to the calibration model used from ChemCam [6], and then compared this model to ensemble methods [5,7]. Blended submodels split the data into smaller portions, trains linear models on these portions, and then optimizes the blend ranges of the submodels to cover the full data range [8]. The process of creating optimized submodels is time consuming, and may not yield the best model possible. Ensemble methods are non-linear, and would negate the need to train and optimize submodels. The response of the instrument to atomic emission is likely non-linear, and thus ensemble methods are likely to have better success in calibration than our previous attempts using LASSO, PLS, etc. Ensemble methods tested include Gradient Boosting, Random Forests, and Extra Trees [9]. Methods: Data Collection and Pre-processing. A standard set, for which MnO content is known, consisting of 252 training and 70 test standards, was analyzed using the SuperCam flight model from 1.6 m distance (3 average spectra were collected on each standard consisting of 50 shots averaged in each point) under a Mars-like atmosphere [5]. The standard set covers a range of Mn compositions from 0.0009-76 wt% MnO and contains a variety of rock matrices (e.g., rock, mineral, Mn ores). No outliers were removed. We use the Python Hyperspectral Analysis Tool [10] and the associated graphical interface for point spectra analysis [10] to preprocess the data and evaluate multivariate regression models. Ensemble methods were trained using Python scikit-learn [7,9]. Each spectrum is normalized by the sum of the total emission for each detector [5]. A "peak area" (PA) preprocessing technique is used [6], where local minima and maxima of the average spectra of the dataset is determined. The process then bins the emission between each pair of minima and assigns the result to the wavelength of the corresponding maximum. We compared full spectra with peak area spectra for this work. Based on preliminary work, we masked wavelengths ≥750 nm, where there are no Mn emission lines, to remove lines from alkali, minor elements, and oxygen, all of which had some influence on the LASSO model