DOI number:10.3389/feart.2022.1086325
Affiliation of Author(s):1 Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitori
Journal:Frontiers in Earth Science
Key Words:ion adsorption rare earth ore, remote sensing, quantitative inversion, convolutional neural network model, GF-2 image, RE2O3 content
Abstract: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.
First Author:Gong Cheng
Indexed by:Journal paper
Document Code:000918726400001
Discipline:Engineering
First-Level Discipline:Geological Resources and Geological Engineering
Document Type:J
Volume:10
Page Number:1-14
ISSN No.:2296-6463
Translation or Not:no
Included Journals:SCI