Determining the location of the Bayan Obo rare earth elements mineralization body by the transfer learning method

Abstract

Bayan Obo is the largest rare earth element (REE) deposit in the world. The occurrence of REE is closely related to the dolomite in this area. Dolomite serves as the mother rock of REE mineralization and the ore body. How to accurately locate and characterize dolomite is the key to determining the distribution of REE and estimating its reserves. A large amount of geophysical work has been conducted in this area, including a dense seismic array, various electromagnetic methods, gravity and aeromagnetic surveys, as well as numerous petrophysical property measurements. To fully leverage the results obtained by these geophysical methods and develop an understanding of the physical property structure, a multisource geophysical data fusion technology is developed. First, various physical property profiles obtained from inversion on the same profile are converted into images with identical resolution and dimension. Then, an image adaptive feature extraction technique based on transfer learning is used to extract features of different scales from multisource images. Subsequently, the fusion image is reconstructed based on the local nearest neighbor weighted average feature fusion rule to obtain the final fusion result. This aids in identifying the spatial appearance pattern of the target for detection. Given the physical characteristics of the mineralized dolomite, which has high density, high resistivity, and high magnetic susceptibility, its location and shape can be defined in the fusion image. The results indicate that the occurrence depth of dolomite can extend up to 1500 m and the dolomite has a southward tilt as one of its primary structural characteristics. The predicted range of dolomite distribution is consistent with the formation range revealed by drilling, making it a reliable basis for predicting the distribution of rare earth ore bodies.