Laser-induced breakdown spectroscopy combined with a convolutional neural network: A promising methodology for geochemical sample identification in Tianwen-1 Mars mission

Abstract

As an in-situ and stand-off detection technique, laser-induced breakdown spectroscopy (LIBS) can perform efficient geochemical sample identification and classification with chemometrics, and therefore LIBS has played a shining role in planetary exploration missions. Unlike in laboratory experiments, the LIBS sampling distance in field detection for planetary exploration naturally varies. The considerable spectral differences caused by the varying distance can be a critical challenge for chemometrics model training and testing. In this research, we address this issue by focusing on the construction of a chemometrics model with powerful learning ability rather than the conventional spectral data processing for distance correction. Specifically, we have investigated the performance of a designed deep convolutional neural network (CNN) on datasets consisting of multi-distance spectra. More than 18,000 LIBS spectra were collected by a duplicate model of the MarSCoDe instrument for China's Tianwen-1 Mars mission, at eight different distances ranging from 2.0 m to 5.0 m. These spectra were acquired from 39 geochemical standard samples, which were classified by the deep CNN. The competence of the CNN is compared with that of four alternative chemometrics, i. e. back-propagation neural network, support vector machine, linear discriminant analysis, and logistic regression. The CNN can surpass the other four algorithms in terms of overall prediction accuracy. In addition, we have inspected the dependence of the CNN performance on the distance number involved in the training set and the data properties of the testing set. Furthermore, it has been revealed that the CNN model can behave even better if an extremely simple distance correction procedure is supplemented. Our results show that CNN is an extraordinary chemometrics for material classification on multi-distance spectra datasets, implying that CNN-LIBS is a promising methodology for geochemical sample identification/classification in Tianwen-1 mission and other future planetary exploration missions, and in even more field detection scenarios with varying sampling distance.