ABSTRACT

In the process of mineral deposits formation, elements often show abnormal enrichment. Therefore, the inversion of element contents (the prediction of element contents in mineral deposits through hyperspectral data) has some indicative significance to the exploration of mineral resources. In order to solve the problem of nonlinear copper contents inversion using hyperspectral remote sensing, this paper put forward an improved Gradient Boosting Regression Tree (IGBRT) method to invert copper contents. The main innovations of this paper are: (1) replacing the simple average function with the k-nearest-neighbour weighted average function as the node prediction function to improve the accuracy; (2) using the adaptive reduction step instead of the fixed reduction step to improve the efficiency. At the end of the paper, taking the Altun region, Xinjiang province, China as the experimental area, the iron element which was high correlated with the copper was chosen as the intermediate variable, and the contents relationship between them was established to solve the problem of the small number of copper samples in the study area. Using the improved Gradient Boosting Regression Tree method to predict the iron contents, then the copper contents could be predicted through the relationship between them. The results showed that the IGBRT algorithm had the coefficient of determination (R²) of 0.744, which were 0.185 more than the traditional one, and the learning efficiency was increased by 39.4%. The results of this study can provide a reference for remote sensing inversion of copper contents in mineral deposits of uninhabited areas, and have some important significance for the delineation of copper prospects.