Abstract
With the ChemCam instrument, laser-induced breakdown spectroscopy (LIBS) has successively contributed to Mars exploration by determining the elemental compositions of soils, crusts, and rocks. The American Perseverance rover and the Chinese Zhurong rover respectively landed on Mars on February 18 and May 15, 2021, further increase the number of LIBS instruments on Mars. Such an unprecedented situation requires a reinforced research effort on the methods of LIBS spectral data analysis. Although the matrix effects correspond to a general issue in LIBS, they become accentuated in the case of rock analysis for Mars exploration, because of the large variation of rock compositions leading to the chemical matrix effect, and the difference in surface physical properties between laboratory standards (in pressed powder pellet, glass or ceramic) used to establish calibration models and natural rocks encountered on Mars, leading to the physical matrix effect. The chemical matrix effect has been tackled in the ChemCam project with large sets of laboratory standards offering a good representation of various compositions of Mars rocks. The present work more specifically deals with the physical matrix effect which is still lacking a satisfactory solution. The approach consists in introducing transfer learning in LIBS data treatment. For the specific application of total alkali-silica (TAS) classification of rocks (either with a polished surface or in the raw state), the results show a significant improvement in the ability to predict of pellet-based models when trained together with suitable information from rocks in a procedure of transfer learning. The correct TAS classification rate increases from 25% for polished rocks and 33.3% for raw rocks with a machine learning model, to 83.3% with a transfer learning model for both types of rock samples.
Highlights
With the ChemCam instrument, laser-induced breakdown spectroscopy (LIBS) has successively contributed to Mars exploration by determining the elemental compositions of soils, crusts, and rocks
In order to emphasize the improvement with transfer learning, we first present the results obtained with the machine learning models trained using the 18 training pellet samples and validated using the 2 isolated pellets and the 12 validation rocks respectively for the 3 concerned oxides, SiO2, Na2O and K 2O
Fig. 1) TTTTT S3 U2 S2 T O3 O2 O1 RTRBBRR Pc from natural rocks with a polished surface or in a raw state. Such scenario corresponds to the important application of analysis of rocks with LIBS on Mars, the used experimental configuration compared to the current rovers on Mars remains still quite different, concerning the ambient gas, the laser excitation, as well as the spectrum detection
Summary
Analytical performances with the machine learning model. In order to emphasize the improvement with transfer learning, we first present the results obtained with the machine learning models trained using the 18 training pellet samples and validated using the 2 isolated pellets and the 12 validation rocks respectively for the 3 concerned oxides, SiO2, Na2O and K 2O. This means that the participation of the 8 rock samples in the training data set together with the retained pellet samples with common selected features, effectively takes into account the physical matrix effect and reinforces the robustness of the models for prediction for rock samples, including isolated rocks totally unknown by the models.
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