典型文献
Zircon classification from cathodoluminescence images using deep learning
文献摘要：
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 clas-sify 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 zir-con classification and presents the great potentiality of deep learning techniques in numerous geoscien-tific disciplines.
文献关键词：
中图分类号：
作者姓名：
Dongyu Zheng;Sixuan Wu;Chao Ma;Lu Xiang;Li Hou;Anqing Chen;Mingcai Hou
作者机构：
State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation,Chengdu University of Technology,Chengdu 610051,PR China;Key Laboratory of Deep-time Geography&Environment Reconstruction and Applications of Ministry of Natural Resources&Institute of Sedimentary Geology,Chengdu University of Technology,Chengdu 610051,PR China;Program of Geology and Geophysics,Missouri University of Science and Technology,Rolla 65401,USA;College of Earth Sciences,Chengdu University of Technology,Chengdu 610051,PR China
文献出处：
引用格式：
[1]Dongyu Zheng;Sixuan Wu;Chao Ma;Lu Xiang;Li Hou;Anqing Chen;Mingcai Hou-.Zircon classification from cathodoluminescence images using deep learning)[J].地学前缘（英文版）,2022(06):111-121
A类：
Cathodoluminescence,DCNNs,geoscien
B类：
Zircon,classification,from,cathodoluminescence,images,using,deep,learning,widely,used,heavy,mineral,geochronological,geochemical,research,because,can,extract,important,information,understand,history,genesis,rocks,has,various,types,accurate,examination,prerequisite,procedure,before,further,analysis,CL,imaging,most,reliable,ways,classify,zircons,However,current,conducted,by,manual,which,consuming,bias,prone,requires,expertise,An,automated,free,method,absent,but,necessary,To,this,end,convolutional,neural,networks,transfer,are,applied,study,common,igneous,metamorphic,hydrothermal,atlas,over,these,three,created,trained,results,that,distinguish,indicated,highest,accuracy,Although,similar,textures,pose,great,challenges,successfully,This,demonstrates,presents,potentiality,techniques,numerous,tific,disciplines
AB值：
0.488831
相似文献
机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
