Data mining of the best spectral indices for geochemical anomalies of copper: A study in the northwestern Junggar region, Xinjiang

Abstract

Abstract Hyperspectral remote sensing allows sampling at high temporal resolutions as well as rapid and non-destructive characterization of a wide range of mineralization, enabling identification of element content through spectral features. It provides data for prospecting in areas without sufficient geochemical data, and thus is of vital significance in prospecting for ores in such regions. However, approaches for remotely sensing elements are still lacking, particularly for element content. In this study, a level analysis of Cu content via spectral indices in the northwestern Junggar region, Xinjiang, was conducted. Based on four levels (0–100\xa0ppm, 100–1000\xa0ppm, 1000–10,000\xa0ppm, and >10,000\xa0ppm) of Cu content and corresponding spectral reflectance, simple and useful spectral indices for estimating Cu content at different levels were explored. The best wavelength domains for a given type of index were determined from four types of spectral indices by screening all combinations using correlation analysis. The coefficient of determination (R2) for Cu was calculated for all indices derived from the spectra of rock samples and was found to range from 0.02 to 0.75. With sensitive wavelengths and a significant correlation coefficient (R2\xa0=\xa00.63, P