Mineral prospecting mapping with conditional generative adversarial network augmented data.

[Display omitted] • A quantitative mineral prediction model including 14 prediction indicators is established. • The deep learning model of cGAN is proposed for data augmentation of geoscientific data. • Using the CNN model as an indirect metric to measure the performance of different data augmentation strategies. Mineral Prospectivity Mapping (MPM) plays a pivotal role in identifying geo-anomalies that are indicative of potential mineralization, drawing upon various geological, geophysical, geochemical, and remote sensing data. With the increasing availability of such data in recent years, data-driven MPM methods have proven effective in discovering new mineral deposits, especially when utilizing machine learning techniques to unveil complex relationships between exploration data and mineral occurrences. Deep learning, specifically Convolutional Neural Networks (CNN), has demonstrated its superiority in this regard. However, these models encounter challenges due to the limited and imbalanced nature of geological exploration data. In this study, we address these challenges by proposing the adoption of a Conditional Generative Adversarial Network (cGAN) strategy for data augmentation. Additionally, we employ the sliding window algorithm for comparative analysis, and CNNs are utilized to assess the effectiveness of various data augmentation strategies. The results indicate that the cGAN-based data augmentation strategy exhibits higher resistance to overfitting, a critical concern in MPM applications. Furthermore, this research successfully delineated a prospectivity map for gold deposits in the Hezuo-Meiwu district, Gansu, China, providing valuable insights for future exploration efforts. [ABSTRACT FROM AUTHOR]