A key property of Variational Autoencoders (VAEs) is their ability to learn a continuous latent space in which similar input patterns are mapped to nearby regions (Kingma & Welling, 2014). As a result, inputs sharing similar characteristics tend to be organized smoothly in latent space. This behavior has been widely observed across multiple domains. In computer vision, for example, VAEs have been successfully applied to benchmark datasets such as handwritten digits (LeCun et al., 1998), where they learn structured latent representations that group visually similar digits (Zemouri, 2020), as illustrated in Figure 1. In anomaly detection tasks, this same property allows the model to distinguish normal patterns from outliers by identifying samples that deviate from the learned latent distribution (An & Cho, 2015). Similarly, in medical imaging, VAEs have been extensively used for anomaly detection and representation learning, effectively capturing complex anatomical patterns and variability across imaging modalities (Chen et al., 2018; Baur et al., 2020). In the context of mineral exploration, subcrops exhibiting geological features associated with porphyry systems are expected to occupy nearby regions in latent space, enabling the definition of a representative latent signature and the identification of analogous patterns across the study area. This study proposes an unsupervised mineral prospectivity mapping framework based on a Convolutional Variational Autoencoder (VAE-CNN), which learns intrinsic geological patterns directly from multi-source datasets without requiring explicit non-deposit labeling. To evaluate spatial generalization, the study area is subdivided into western and eastern sectors: deposits in the western sector are used to define the porphyry latent signature, while those in the eastern sector are reserved for independent validation. The methodology follows a two-stage approach. First, the VAE-CNN is trained to learn latent representations of spatial patterns across the study area. Then, subcrops associated with confirmed porphyry deposits are projected into this latent space and aggregated to define a Figure 13 – Handwritten digits organized in latent space according to their spatial patterns, where visually similar digits form coherent groups. Adapted from Zemouri (2020).
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