We propose a methodology for the identification of rock types using geophysical and geological information. It relies on an algorithm used in machine learning called self-organizing maps, to which we add plausibility filters to ensure that the results respect base geological rules and geophysical measurements. Application in the Yerrida Basin (Western Australia) reveals that the thinning of prospective greenstone belts at depth could be due to deep structures not seen from surface.