Fusion of remote sensing and geochemical data using hybrid Variational Autoencoder- BIRCH deep learning algorithm for copper prospectivity mapping

Clustering methods are an essential part of machine learning (ML) algorithms and are widely used to integrate a variety of datasets, such as remote sensing, geochemical, and geological data, for mineral prospectivity mapping (MPM). These methods help exploration geologists identify mineralization zones. However, as geospatial datasets become more complex, nonlinear, and high-dimensional, traditional clustering algorithms often fail to handle and analyze them effectively. To address this challenge, this study presents a new unsupervised deep learning (DL) approach called the hybrid Variational Autoencoder- BIRCH (VAE-BIRCH) algorithm, which was applied for porphyry copper prospectivity mapping. The northern sector of Shahr-e-Babak district in southern Iran, which contains numerous porphyry copper deposits, was selected as a case study. ASTER and Landsat 8-OLI satellite remote sensing data were meticulously processed to highlight argillic, silicic, phyllic, propylitic, and iron oxide alteration zones. Factor analysis was applied to stream geochemical data, which demonstrated strong correlation among Copper (Cu), Lead (Pb) and Zinc (Zn). These elements were then used to generate geochemical evidence layers for the study area. These layers were then passed into a VAE, which reduced the data into a lower-dimensional latent space while keeping the important patterns. The VAE created a probability distribution for each sample in the latent space and sampled from it. Then, based on the importance of the input features, the data were passed to the BIRCH clustering algorithm for clustering. The prediction-area (P-A) plot was used to identify anomaly clusters from the background. For comparison, results from the traditional BIRCH algorithm were also generated. The findings showed that the VAE-BIRCH method has a better prediction rate than the BIRCH method. To validate the result of the model, field surveys and laboratory analyses, including microscopic studies and X-ray fluorescence (XRF) analyses, were conducted. These confirmed the presence of minerals associated with porphyry copper mineralization. Based on these results, this paper recommends applying the hybrid VAE-BIRCH algorithm to other copper mineralization provinces and frontier terranes (pristine or remote zones) for mineral exploration targeting worldwide.