QUANTITATIVE ESTIMATION OF SIO2 CONTENT IN IGNEOUS ROCKS USING THERMAL INFRARED-SPECTRA WITH A NEURAL-NETWORK APPROACH

The objective of this paper is to develop 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 mu m) The wavelengths of these so-called ''reststrahlen bands'' are systematically related to the SiO2 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.