A Geologically Constrained Variational Autoencoder for Mineral Prospectivity Mapping

Deep learning algorithms (DLAs) are becoming popular tools for mineral prospectivity mapping. However, purely data-driven DLAs frequently ignore expert and domain knowledge, imposing difficulty in interpretability from a geological perspective. The efficient integration of geological knowledge into DLAs remains challenging in geosciences. In this study, a geologically constrained variational autoencoder (VAE) was proposed to map prospectivity for gold mineralization in the Baguio District of the Philippines. A spatial nonlinear correlation between an ore-forming controlling feature and locations of mineral deposits was built as part of the loss function for constructing a geologically constrained VAE. A comparative study of a geologically constrained and a traditional VAE demonstrated that the former can enhance the probabilities in areas with high potential for locating mineralization and increase the interpretability of the obtained results.

Automated summary

In this study, a geologically constrained variational autoencoder (VAE) was proposed to map prospectivity for gold mineralization in the Baguio District of the Philippines. The geologically constrained VAE can enhance the probabilities in areas with high potential for mineralization and increase the interpretability of the obtained results compared to traditional VAE methods.