Review of mineral recognition and its future

• Published in: Applied geochemistry Vol. 122; p. 104727
• Main Authors: Lou, Wei, Zhang, Dexian, Bayless, Richard C.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2020
• Subjects: automation; chemical composition; Convolutional neural network; diagenesis; Facial recognition; geochemistry; Mineral analysis technology; Mineral recognition; mineralization; minerals; qualitative analysis; Raman spectroscopy; rocks; scanning electron microscopy; trace elements; X-ray diffraction; X-ray fluorescence spectroscopy; Mineral recognition; Mineral analysis technology; Convolutional neural network; Facial recognition
• ISSN: 0883-2927; 1872-9134
• DOI: 10.1016/j.apgeochem.2020.104727

Mineral identification is a basic skill in geological studies, and is useful for characterizing rocks and tracing diagenesis and mineralization processes. Traditional methods of observation under a microscope are subject to many complex factors such as the limitations of resolution and magnification, so they are poor in qualitative analysis, and inefficient. With the expansion of geological prospecting, it is necessary to provide information for all minerals, pores and trace elements in rocks. So, mineral identification has started to rely on advanced microbeam mineral analysis techniques. This paper summarizes the common mineral analysis techniques such as Raman spectroscopy, X-ray fluorescence spectrometry (XRF), X-ray diffraction (XRD), Scanning electron microscopy (SEM), and Automated mineralogy (AM) systems. These microbeam technologies now approach a semi-automated analysis process, and most of these methods mainly detect the chemical composition of the mineral, rather than the mineral's optical characteristics which are the most basic properties of minerals. Therefore, this study proposes a method that can use mineral's optical features for automatic classification, mineral recognition based on convolutional neural network (CNN) and face recognition technology. The feasibility, research status and outlook of this method are also discussed. The proposed method uses convolution neural network technology to automatically extract the optical characteristics of minerals for mineral identification. Successful application of these techniques will have profound application value by reducing the cost and time needed to process and identify minerals. [Display omitted] •The working mechanism and characteristics of many common microbeam mineral analysis techniques are summarized.•This study pointed out that these microbeam technologies mainly rely on the chemical composition and crystal structure of minerals.•This study proposes a method using mineral's optical features combined with neural network (CNN) for mineral automatic classification.•The feasibility, research status and outlook of the proposed method were discussed.