MINERÍA la mejor puerta de acceso al sector minero EDICIÓN 588 / SEPTIEMBRE 2026 36 Exploraciones MAPEO PROSPECTIVO MINERAL DE PÓRFIDOS-SKARN DE COBRE EN FRANJA ANDAHUAYLAS-YAURI CON REDES CONVOLUCIONALES DE GRAFOS Abstrac This study evaluates the application of Graph Convolutional Networks (GCN) for mineral prospectivity mapping (MPM) of copper porphyry-skarn in the Andahuaylas-Yauri belt, a significant metallogenic zone in southern Peru. Publicly available geoscientific data, including geochemistry, lithology, and lithological contacts, were integrated into evidence maps and used to train four machine learning models: Random Forest (RF), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Graph Convolutional Network (GCN). Spatial cross-validation using k-means clustering was employed to mitigate spatial bias arising from the limited number of known mineral occurrences.
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