Research on quantitative inversion of ion adsorption type rare earth ore based on convolutional neural network
点击次数：
影响因子：
3.661
DOI码：
10.3389/feart.2022.1086325
所属单位：
1 Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitori
发表刊物：
Frontiers in Earth Science
关键字：
ion adsorption rare earth ore, remote sensing, quantitative inversion, convolutional neural network model, GF-2 image, RE2O3 content
摘要：
Rare earth resource is a national strategic resource, which plays an essential role in the field of high technology research and development. In this paper, we aim to use remote sensing quantitative inversion prospecting technology, use surface-tosurface mode, and model inversion and evaluation through convolutional neural network model to achieve a new research method for large-scale, low-cost, rapid and efficient exploration of ion-adsorbed rare earth ore. The results show that the RE2O3 content of samples has significant negative correlation with the second, third and fourth band of GF-2 image, but has no significant correlation with the first band of GF-2 image; the convolution neural network model can be used to reconstruct the RE2O3 content. The content distribution map of RE2O3 obtained by inversion is similar to that of geochemical map, which indicates that the convolution neural network model can be used to invert the RE2O3 content in the sampling area. The quantitative inversion results show that the content distribution characteristics of ion adsorption rare earth ore in the study area are basically consistent with the actual situation; there are two main high anomaly areas in the study area. The high anomaly area I is a known mining area, and the high anomaly area II can be a prospective area of ion adsorption type rare earth deposit. It shows that the remote sensing quantitative inversion prospecting method of ion adsorption type rare earth deposit based on Convolutional Neural Networks (CNN) model is feasible.
第一作者：
Gong Cheng
论文类型：
期刊论文
通讯作者：
Huan Li, Xiaoqing Deng, Yuying Ban
论文编号：
000918726400001
学科门类：
工学
一级学科：
地质资源与地质工程
文献类型：
J
卷号：
10
页面范围：
1-14
ISSN号：
2296-6463
是否译文：
否
收录刊物：
SCI
附件：
学位：博士学位
学科：地质资源与地质工程. 地质学. 测绘科学与技术
所在单位：地球科学与信息物理学院