Auto encoder generative adversarial networks - based mineral prospectivity mapping in Lhasa area, Tibet

Abstract

When conducting Mineral Potential Mapping (MPM) using multiple sources of data such as geology, geochemistry, and geophysics, it often encounters the challenges of complex and diverse data distributions within these datasets. Enhancing the capability to extract nonlinear data features and further uncover metallogenic information is a crucial research objective. This study utilizes the C-A multifractal approach to extract anomalous information related to metallogenic elements, employs compositional data analysis methods for quantitatively extracting geochemical associations of mineralization, and utilizes GIS spatial analysis techniques to quantitatively extract predictive indicators from various data sources, including geology, geophysics, and remote sensing, to construct an MPM prediction dataset. Building upon the foundation of AutoEncoder (AE), this study introduces a discriminator and employs the AEGAN (Auto Encoder Generative Adversarial Network) algorithm, which combines AutoEncoder and Generative Adversarial Network (GAN), for metallogenic prospectivity prediction in the Lhasa region. Compared to AE algorithms, AEGAN combines the strengths of AE and GAN, significantly improving the model's ability to reconstruct input data through the interaction between the generator and discriminator. Additionally, this study designs comparative experiments with AE, and the results demonstrate that the AEGAN model can more accurately identify the correlation between high anomaly areas and polymetallic deposits, providing a more precise delineation of anomalous extents. The Area Under the Receiver Operating Characteristic Curve (AUC) further validates the superior performance of the AEGAN model. These findings indicate that the AEGAN model exhibits outstanding capabilities in learning the internal connections and features among multiple data sources, holding significant potential for practical applications in mineral exploration.