A Self-Organizing Map (SOM) was designed with the aim of integrating and searching for patterns in airborne geological and geophysical gamma-spectrometric and magnetic data of the Serra Leste region, Carajás Mineral Province. SOM is an unsupervised Artificial Neural Network method that performs a non-linear mapping from a high-dimensional data space to a 2-dimensional grid, whereas preserving the topological relations in the original data. The SOM grid can be efficiently used in an integrated visualization and understanding of the internal relationships in the data. The K-means algorithm is applied to the SOM grid to reduce the number of mapped patterns so as to facilitate interpretation. Unfolding of the clustered SOM grid associates each mapped pattern with the spatial position of each data point. The SOM reclassified map was compared with a classified map obtained with the Fuzzy C-means method for the same input data and with the same number of classes. The results show the potentiality of SOM in producing higher quality integrated maps to support mineral exploration.
Carajás Mineral Province; airborne geophysics; self-organizing maps; fuzzy clustering; mineral potential mapping
