Abstract
Remote sensing (RS) technologies have emerged as efficient and cost-effective tools for the rapid identification of hydrothermal alteration zones, thereby supporting mineral exploration. The Zafarghand area in northeastern Isfahan Province, situated within the Central Iran structural zone of the Urmia–Dokhtar magmatic arc, hosts a porphyry system with multiple alteration types, including phyllic, propylitic, argillic, potassic, and silicic zones. This study aimed to develop an unsupervised and data-driven framework for mapping hydrothermal alteration using high-resolution satellite imagery. To achieve this, Sentinel-2 data were processed through a β-Variational Autoencoder (β-VAE) Deep Learning (DL) model, enriched by Discrete Wavelet Transform (DWT), and subsequently clustered with a Gaussian Mixture Model (GMM). The proposed approach successfully identified alteration zones and iron-bearing minerals (Fe²⁺, Fe³⁺, and iron oxides) at a 20-m spatial resolution. Validation using geochemical data showed classification accuracies of 94.74% and 95.74% for the phyllic and propylitic zones, respectively. In addition, buffered pixel-based validation using confusion matrices yielded overall accuracies of 94.5% and 86.9% for the phyllic and propylitic maps, confirming the statistical robustness of the proposed framework. Due to the limited distribution of argillic and silicic alteration, the depth of the potassic zone, and the lack of sufficient geochemical data for their validation, this study mainly focused on identifying phyllic and propylitic alteration along with iron oxides.These findings demonstrate that integrating DL, wavelet analysis, and probabilistic clustering with Sentinel-2 imagery provides a robust framework for detecting hydrothermal alteration zones. While the current study primarily focused on phyllic, propylitic, and iron-oxide zones due to limitations in data availability, the framework is transferable to other porphyry systems, provided that suitable reference data exist for validation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1038/s41598-026-36349-w.