No abstract

China's Tianwen-1 Mars rover carries a laser-induced breakdown spectroscopy (LIBS) payload named MarSCoDe to analyze the mineral and rock composition on Mars. MarSCoDe is expected to experience a wide working temperature range of about 100 °C, which will lead to a spectral shift of up to ∼40 pixels (∼8.13 nm). Even worse, drastic changes in temperature and environment may cause a loss or increase of some spectral lines of an on-board calibration Ti target. An elastic particle swarm optimization (PSO) approach is proposed to fulfill the on-board spectral calibration of MarSCoDe under this harsh condition. Through establishing a standard wavelength set (SWS) and an individual particle wavelength set (PWS), and further elastically selecting a part of PWS to compare with SWS, the problem of spectral shift and number mismatch can be solved gradually with the evolution of the particle swarm. Some tests of standard lamps and Ti with MarSCoDe, placed in a Mars simulation environment chamber (MSEC) in a temperature range of 70 °C, were completed. Compared with the standard spectrum of the Ti target (obtained at 20 °C), the spectral shifts of the first, second, and third channels are approximately 0.33 nm (5 pixels), 0.85 nm (6.4 pixels), and 8.09 nm (39.8 pixels), respectively, at −40 °C before correction; after PSO correction, the spectral shifts are greatly reduced to up to 0.015 nm, and specially for the 626.28 nm line, the spectral shift is reduced from 8.09 nm to about 0 nm. Experimental results demonstrate that the PSO-based approach can not only correct the on-board spectral shift but also solve the number mismatch of spectral lines of MarSCoDe in the harsh working environment of Mars. Further, it can be extended to the on-board calibration of other spectral payloads for deep space exploration.

A Mars Surface Composition Detector (MarSCoDe) instrument mounted on Zhurong rover of Tianwen-1, adopts Laser-Induced Breakdown Spectroscopy (LIBS), with no sample preparation or dust and coatings ablation required, to conduct rapid multi-elemental analysis and characterization of minerals, rocks and soils on the surface of Mars. To test the capability of MarSCoDe LIBS measurement and quantitative analysis, some methods of multivariate analysis on olivine samples with gradient concentrations were inspected based on the spectra acquired in a Mars-simulated environment before the rover launch in 2020. Firstly, LIBS spectra need preprocessing, including background subtraction, random signal denoising, continuum baseline removal, spectral drift correction and wavelength calibration, radiation calibration, and multi-channel spectra subset merging. Then, the quantitative analysis with univariate linear regression (ULR) and multivariate linear regression (MLR) are performed on the characteristic lines, while principal component regression (PCR), partial least square regression (PLSR), ridge, least-absolute-shrinkage-and-selection-operator (LASSO) and elastic net, and nonlinear analysis with back-propagation (BP) are conducted on the entire spectral information. Finally, the performance on the quantitative olivine analyzed by MarSCoDe LIBS is compared with the mean spectrum and all spectra for each sample and evaluated by some statistical indicators. The results show that: (1) the calibration curve of ULR constructed by the characteristic line of magnesium and iron indicates the linear relationship between the spectral signal and the element concentration, and the limits of detection of forsterite and fayalite is 0.9943 and 2.0536 (c%) analyzed by mean spectra, and 2.3354 and 3.8883 (c%) analyzed by all spectra; (2) the R2 value on the calibration and validation of all the methods is close to 1, and the predicted concentration estimated by these calibration models is close to the true concentration; (3) the shrinkage or regularization technique of ridge, LASSO and elastic net perform better than the ULR and MLR, except for ridge overfitting on the testing sample; the best results can be obtained by the dimension reduction technique of PCR and PLSR, especially with PLSR; and BP is more applicable for the sample measured with larger spectral dataset.