Quantitative estimation of SiO2 content in igneous rocks using thermal infrared spectra with a neural network approach

Develops a method for estimating the SiO₂ content of igneous rocks using thermal infrared reflectance spectra, aiming to utilize it in the remote sensing of thermal spectral emission. Silicate minerals, which are the major components of the Earth´s surface, display their strongest fundamental molecular vibration bands in the thermal infrared region (8-12 μm). The wavelengths of these so-called “reststrahlen bands” are systematically related to the SiO ₂ content of rocks. Pattern matching (using a back-propagating neural network approach) between simulated remotely sensed data and the SiO₂ content was performed. This approach was evaluated by comparing the spectrally estimated SiO₂ content with the chemically determined SiO₂ content for a separate set of rock samples. The estimated error between the spectrally estimated and chemically determined SiO₂ contents for most samples was within 7%. Future multiband satellite sensors of the Earth´s thermal emission will have much higher spectral and spatial resolution than existing ones, and should be able to detect these spectral trends