A Deep Learning Approach for Augmenting Tabular Geoscience Data

In recent years, artificial intelligence has been increasingly applied in geosciences; however, the scarcity of labeled data limits its effectiveness. Existing data augmentation methods using conditional generative adversarial networks (cGANs) have succeeded in fields like finance and medicine but often struggle to account for the multi-scale features and strong correlations inherent in geoscience data, making direct adaptation challenging. This study introduces an improved cGANs—Improved Conditional Geoscience GAN (ICG-GAN)—specifically designed for augmenting tabular geoscience data. ICG-GAN utilizes continuous features as conditional inputs to preserve their physical properties and correlations, employing a classification-based voting mechanism to predict and impute discrete features. This approach effectively overcomes the limitations of traditional cGANs when learning from sparse samples. To systematically assess the model’s performance, we developed a comprehensive, multi-dimensional evaluation framework. Using core analysis data as a case study, experimental results show that ICG-GAN more accurately replicates the original data distribution and achieves significant performance improvements compared to the leading CTGAN model across six evaluation metrics. This study presents a novel method for augmenting tabular geoscience data, with broad applications in resource exploration, geological mapping, and environmental monitoring, while providing insights for enhancing the performance and robustness of data-driven geoscientific models.