Deep learning with laser-induced breakdown spectroscopy (LIBS) for the classification of rocks based on elemental imaging

In geological research, the identification and classification of rock lithology plays an important role in many fields such as resource exploration, earth evolution and paleontology research. Laser-induced breakdown spectroscopy (LIBS), which is capable of real-time, on-situ, micro-destructive determination of the elemental composition of any substance (solid, liquid, or gas), has been developed as a technology for 'geochemical fingerprinting' in a variety of geochemical applications. However, for rock samples with coarse grains, the bulk analysis based on the average spectrum is insufficient. This study proposes a new method for identifying multiple types of rocks, which utilizes the rapid multi-element compositional imaging capability of LIBS, and combines with the deep learning theory. The LIBS-based images characterizing the spatial distribution of elements on rock surface were achieved firstly, and then were classified by the Inception-v3 network combined with the transfer learning method. In addition, to solve the problem of the small scale of the image dataset obtained in the laboratory, specific data augmentation methods such as cutting-recombining and filtering were proposed. Moreover, the superior of this method was verified by the three classification experiments of shale, gneiss and granite.

Automated summary

In this study, a new method for identifying multiple types of rocks was proposed by using LIBS technology combined with deep learning theory. The method achieved characterization of spatial distribution of elements on rock surface through LIBS-based images and classification using the Inception-v3 network. Specific data augmentation methods were proposed to address the small scale of the image dataset obtained in the laboratory. The superiority of this method was verified through three classification experiments of shale, gneiss and granite.