The recent development in remote sensing imagery and the use of remote sensing detection feature spectrum information together with the geochemical data is very useful for the surface element quantitative remote sensing inversion study. This aim of this article is to select appropriate methods that would make it possible to have rapid economic prospecting. The Qishitan gold polymetallic deposit in the Xinjiang Uygur Autonomous Region, Northwest China has been selected for this study. This paper establishes inversion maps based on the contents of metallic elements by integrating geochemical exploration data with ASTER and WorldView-2 remote sensing data. Inversion modelling maps for As, Cu, Hg, Mo, Pb, and Zn are consistent with the corresponding geochemical anomaly maps, which provide a reference for metallic ore prospecting in the study area. ASTER spectrum covers short-wave infrared and has better accuracy than WorldView-2 data for the inversion of some elements (e.g., Au, Hg, Pb, and As). However, the high spatial resolution of WorldView-2 drives the final content inversion map to be more precise and to better localize the anomaly centers of the inversion results. After scale conversion by re-sampling and kriging interpolation, the modeled and predicted accuracy of the models with square interpolation is much closer compare with the ground resolution of the used remote sensing data. This means our results are much satisfactory as compared to other interpolation methods. This study proves that quantitative remote sensing has great potential in ore prospecting and can be applied to replace traditional geochemical exploration to some extent.

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-to-surface 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.