Convolutional neural networks (CNNs), a prominent deep learning approach, have garnered significant interest in the field of mineral potential mapping (MPM) due to their capability to capture and learn spatial features that traditional algorithms tend to overlook. The effectiveness of CNNs is closely tied to the quantity of training data available, thereby impacting the outcomes of MPM. Moreover, uncertainties arising from delineation of negative samples can compromise the reliability of MPM assessments. To deal with these challenges, we propose the utilization of an autoencoder-based anomaly detection technique for the purpose of annotating locations in an unsupervised manner. Subsequently, a CNN is trained using the unsupervised annotated data to generate a Fe prospectivity model within a specific region in Iran. To validate the effectiveness of the proposed method, we first perform an MPM with insufficient positive training samples that extract from the location of known occurrences. We then execute another MPM on a set of samples labeled based on the reconstruction error of an autoencoder network. When comparing the two prospectivity models, namely using augmented data or inadequate samples, it is evident that modeling with augmented data outperforms the MPM model trained with insufficient samples. This confirms the effectiveness of the adopted approach and shows that the unsupervised labeling technique proposed in this work can significantly improve the performance of the CNN in MPM.
