Feasibility of estimating copper content using visible and near-infrared spectra of bedrock, weathered rock and soil - a case study of the Baogutu stock II, Xinjiang, China

Abstract

Geochemical anomalies are important symbols for prospecting. To determine a new method for identifying geochemical anomalies rapidly, this study examines the possibility of using visible and near-infrared spectra of bedrock, weathered rock, and soil to assess copper content in the Baogutu stock II, Xinjiang, China. Partial least squares regression was employed to study the relationship between visible and near-infrared spectra and the ore-forming element contents of collected bedrock samples, weathered rock samples, and soil samples. More specifically, our study aims to achieve three objectives. First, to explore the correlations between the contents of different elements and the correlations between the visible and near-infrared spectra and the ore-forming element contents for the three sample types. Second, to evaluate the performance of the reflectance-based partial least squares regression model. The models for iron in bedrock samples and copper in soil samples were chosen for estimating copper content. Third, to assess the effectiveness of data transformation methods in the partial least squares regression model. In our case study, the square root-based models for iron in bedrock samples and copper in soil samples with coefficients of determination for prediction are 0.614 and 0.409 respectively, maximized the model performance. Therefore, it is evident that visible and near-infrared spectra can be an alternative method for estimating ore-forming element contents based on bedrock, weathered rock, and soil samples.