Detecting mineralization via white mica spectral footprints: From field-based sampling to satellite hyperspectral remote sensing

• Both SWIR spectra and GF-5 hyperspectral data can obtain the alteration information. • The spectral feature parametrization enables the estimation for GF-5 imagery. • A spectral feature-based exploration indication model is established. • Hyperspectral remote sensing technology and SWIR spectroscopy are combined. The utilization of shortwave infrared (SWIR) spectroscopy serves as a potent technique in mineral identification and hydrothermal alteration mapping, and spectral characteristics of some altered minerals have been indicators for targeting porphyry-epithermal deposits. Its application relies on acquiring ground/lab spectral data from surface and/or drill hole samples, with potential satellite remote sensing applications remaining more effort. In this study, the Duolong mineral district served as the test area for alteration mapping, spectral characterization, and mineral exploration. A spectral feature parameterization method and an exploration indication model were proposed based on the SWIR spectra and Gaofen-5 (GF-5) hyperspectral images. The results showed that the hydrothermally altered minerals in the Duolong mineral district are mainly composed of white mica, kaolinite, and chlorite, with minor amounts of sulphate and carbonate. The minerals exhibited characteristic alteration zoning patterns commonly observed in porphyry Cu systems, and there is a clear overlap between the zones. SWIR analysis conducted in the Gaerqin deposit, specifically revealed the spatial variation of both the position (Pos2200) and depth (Dep2200) of the Al-OH absorption feature in white mica surrounding the granodiorite porphyry. That is, the shorter Pos2200 (< 2203 nm) and higher Dep2200 (> 0.32) of white mica in the surface ring zone, the closer to the center. Meanwhile, the support vector regression (SVR) method was used to construct a deposit-scale spectral feature-based exploration indication model for white mica, incorporating the Pos2200 and Dep2200 parameters. Based on the spatial distribution of white mica derived from GF-5 imagery, this model facilitated the prediction of potential hydrothermal and/or mineralization centers, showing good correspondence with the known locations of magmatic rocks and ore deposits in the Duolong mineral district. This study demonstrates that integrating satellite remote sensing and mineral spectral measurement not only reveals the significance of white mica spectral footprints but also enhances the accuracy and convenience of remote sensing mineral exploration prospecting, thereby providing novel insights for future mineral exploration.