This work discusses the use of semantic import approach (SI) as a way to characterize geological boundaries in spatial analysis procedures. The technique applies fuzzy sets to represent the nature of the lithological boundaries (defined and inferred) in a spatial inference model used to map potential areas for radioactive mineral prospecting in the Poços de Caldas alkaline complex. The procedure permits use of more efficient lithological units as diagnostic criteria in a prospecting model developed in GIS. Linear fuzzy functions were applied to create continuous surfaces that express more faithfully the spatial distribution of each lithological unit. The final lithological map was integrated with other field evidences (e.g. gamma-ray total counts and structural features) through a weighted means sum. The resulting product was arbitrarily sliced in four potential categories (null, low, medium and high). The fuzzy scenario was qualitatively and quantitatively compared with previous results obtained by Almeida-Filho (1995). This author used boolean algebra and weighted means based models to map potential areas. The comparison of both procedures showed that the fuzzy scenario was slightly better. A target area of 30,43 Km² (4,18% of the alkaline complex) included 27 mineral occurrences, whereas each previous model encompassed only 24 in an approximately similar area. We also assessed the correlation of mineral occurrences in relation to the high and medium potential classes for the different scenarios. The high and medium potential classes of the fuzzy scenario showed posterior probabilities 12.9 and 5.7 higher than the prior probabilities. These values were, respectively, 12.6 and 4.97 for the same classes defined by Almeida Filho (1995). For the potential class of the Boolean model this value was 5.78. These results show that fuzzy set theory enables a better refinement of the data modeling, improving the ability to spatially represent geological boundaries as continuous surfaces.
semantic import approach for boundaries; fuzzy sets; spatial analysis; GIS
