Spectral mixture modelling and spectral stratigraphy in carbonate lithofacies mapping

Carbonate areas have been studied extensively since carbonate depositional environments have high economic potential and are likely to contain petroleum resources. Systematic mapping of carbonate lithofacies from remote sensing data can be done using the traditional practice of photogeologic interpretation. Additionally, spectral characterization yields compositional information complementary to the structural information that the photogeologic map provides. The Montalbán area in northern-central Spain is an ideal target for photointerpretation due to the impressive display of structural features and lithologic differentiation. Therefore most image interpretation studies in this area have focused on such an exercise neglecting largely the spectral information available. In this contribution, field and laboratory spectroscopy in conjunction with image analysis are used to construct a spectral stratigraphic column for the units exposed in the area. The findings from this analysis are used to select minerals likely to exist in the image. Furthermore, carbonate endmember spectra for a number of lithofacies are extracted from the image data. Both the pure mineral spectra and the lithofacies spectra are used as input for a spectral unmixing analysis to obtain abundance estimates of mineral- and rock-types for each pixel in the image. Image classification assumes that a pixel can be assigned to a single spectral class although most pixels are mixed in the sense that their spectral response is the product of a number of signatures together constituting the observed reflectance characteristics. Spectral unmixing acknowledges this fact and aims at separating the spectral contributions of a number of endmember spectra at a pixel support. Thus spectral unmixing yields abundance estimates for each endmember together summing-up to the 100% reflectance measured in the image. Besides this conceptual advantage of unmixing over classification, spectral unmixing, by contrast to classification, also provides an error assessment by comparing the observed pixel spectrum with the calculated mixed spectrum. This error matrix can be used to assess the accuracy of the unmixing systematics as well as the proper selection of endmembers.