Laser-induced breakdown spectroscopy chemometrics for ChemCam Mars in situ data analysis based on deep learning and pretrained-model-based transfer learning

Abstract

As an in situ and stand-off detection technique, Laser-Induced Breakdown Spectroscopy (LIBS) has been successfully applied in Mars exploration, by installing the LIBS instrument on three Mars rover payloads, i.e. ChemCam, SuperCam and MarSCoDe. Effective analysis of the Mars in situ data requires high-performance LIBS chemometrics. Deep learning methods like convolutional neural networks (CNNs) have been demonstrated to be powerful as LIBS chemometrics, but they need many labeled samples for model training. Since Mars exploration is a typical scenario where labeled samples are scarce, a natural idea is to take advantage of a large laboratory database. However, the profile discrepancies between Mars in situ spectra and laboratory spectra can be a prominent challenge for the joint use of the two-source data. To address this issue, conventional solutions focus on formulating data conversion strategies to make the different-source data more similar. Such a methodology can certainly yield positive effects, but it requires quite a few common samples and laborious design, and the data similarity would anyhow be limited. In order to employ deep learning for LIBS analysis, this study proposes a new scheme by integrating with a transfer learning technique, which focuses on “knowledge transfer” rather than conventional “data transfer”. ChemCam LIBS data quantification is taken as an example to illustrate the effectiveness. Specifically, a deep CNN model was constructed and trained based on 59760 LIBS spectra collected by a ChemCam laboratory duplicate, then this pretrained CNN model carried out transfer learning, i.e. the model parameters underwent a fine retraining based on 175 in situ spectra acquired from ChemCam calibration targets on Mars, and finally the CNN model after transfer learning was employed to analyze 575 other ChemCam in situ spectra from 3 Martian natural targets as a generalization performance testing. The results from the proposed transfer learning integrated scheme have been compared with the results from four alternative schemes merely including deep learning, as well as with the results from an exquisite scheme developed by the ChemCam team. Regarding the overall quantification accuracy, our scheme can noticeably surpass the four alternative deep learning schemes and show approximately equal performance to the ChemCam scheme. The results indicate that transfer learning is a promising booster for deep learning methods to accurately and efficiently analyze Mars in situ data collected from SuperCam, MarSCoDe, and future payloads.