The western part of the island of Milos, Greece has undergone widespread, intense alteration associated with a range of mineralization, including seafloor Mn-Fe-Ba, sub seafloor Pb-Zn-Ag, and epithermal Au-Ag. The surrounding country rocks are a mixture of submarine and subaerial calc-alkaline volcanic rocks ranging from basaltic andesite to rhyolite in composition, but are predominantly andesites and dacites. The current surface spatial distribution of the alteration mineralogy is a function not only of the original hydrothermal, but also subsequent tectonic and erosional processes. The high relief and the excellent rock exposure provide ideal conditions to evaluate the potential of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite remote sensing data to identify and differentiate the different styles of alteration mineralisation. Laboratory spectral reflectance and calculated emittance measurements of field samples, supported by XRD analysis and field mapping, were used to support the analysis. Band ratio and spectral matching techniques were applied to the shortwave-infrared (SWIR) reflectance and thermal-infrared (TIR) emissivity imagery separately and were then integrated with topographic data. The band ratio and spectral matching approaches produced similar results in both the SWIR and TIR imagery. In the SWIR imagery, the advanced argillic, argillic and hydrous silica alteration zones were clearly identifiable, while in the TIR imagery, the silicic and advanced argillic alteration zones, along with the country rock, were differentiable. The integrated mineralogical–topographic datasets provided an enhanced understanding of the spatial and altitude distribution of the alteration zones when combined with conceptual models of their genesis, which provides a methodology for the differentiation of the multiple styles of alteration.
Ferrier, G., Naden, J., Ganas, A., Kemp, S., & Pope, R. (2016). Identification of multi-style hydrothermal alteration using integrated compositional and topographic remote sensing datasets. Geosciences, 6(3), 36. https://doi.org/10.3390/geosciences6030036