标题：Zircon classification from cathodoluminescence images using deep learning

入藏号：WOS:000889072400003

中国科学院文献情报中心期刊分区：地球科学1区TOP（2022）

本校作者：郑栋宇，马超，向路，侯立，陈安清，侯明才

来源出版物：GEOSCIENCE FRONTIERS  卷: 13  期: 6  文献号: 101436

出版年：2022

第一地址：成都理工大学

关键词：Zircon;Cathodoluminescence image;Deep learning;Transfer learning

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摘要： Zircon is a widely-used heavy mineral in geochronological and geochemical research because it can extract important information to understand the history and genesis of rocks. Zircon has various types, and an accurate examination of zircon type is a prerequisite procedure before further analysis. Cathodoluminescence (CL) imaging is one of the most reliable ways to classify zircons. However, current CL image examination is conducted by manual work, which is time-consuming, bias-prone, and requires expertise. An automated and bias-free method for zircon classification is absent but necessary. To this end, deep convolutional neural networks (DCNNs) and transfer learning are applied in this study to classify the common types of zircons, i.e., igneous, metamorphic, and hydrothermal zircons. An atlas with over 4000 CL images of these three types of zircons is created, and three DCNNs are trained using these images. The results of this study indicate that the DCNNs can distinguish hydrothermal zircons from other zircons, as indicated by the highest accuracy of 100%. Although similar textures in igneous and metamorphic zircons pose great challenges for zircon classification, the DCNNs successfully classify 95% igneous and 92% metamorphic zircons. This study demonstrates the high accuracy of DCNNs in zircon classification and presents the great potentiality of deep learning techniques in numerous geoscientific disciplines. (C) 2022 China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V.

文章链接地址: https://www.sciencedirect.com/science/article/pii/S1674987122000895?via%3Dihub