  With the ChemCam instrument, laser-induced breakdown spectroscopy (LIBS) has
successively contributed to Mars exploration by determining elemental
compositions of the soil, crust and rocks. Two new lunched missions, Chinese
Tianwen 1 and American Perseverance, will further increase the number of LIBS
instruments on Mars after the planned landings in spring 2021. Such
unprecedented situation requires a reinforced research effort on the methods of
LIBS spectral data treatment. 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 composition leading to
the chemical matrix effect, and the difference in morphology between laboratory
standard samples (in pressed pellet, glass or ceramics) used to establish
calibration models and natural rocks encountered on Mars, leading to the
physical matric effect. The chemical matrix effect has been tackled in the
ChemCam project with large sets of laboratory standard samples offering a good
representation of various compositions of Mars rocks. The present work deals
with the physical matrix effect which is still expecting a satisfactory
solution. The approach consists in introducing transfer learning in LIBS data
treatment. For the specific case of total alkali-silica (TAS) classification of
natural rocks, the results show a significant improvement of the prediction
capacity of pellet sample-based models when trained together with suitable
information from rocks in a procedure of transfer learning. The correct
classification rate of rocks increases from 33.3% with a machine learning model
to 83.3% with a transfer learning model.
