Identifying zircon provenances using domain-adversarial neural network

• Zircon trace element geochemistry is a pivotal tool for unraveling petrogenesis and the evolutionary history of the Earth’s crust. While two-dimensional (2D) discriminant diagrams are conventionally used to identify parent rock types, the emergence of machine learning (ML) has introduced a transformative research paradigm. ML not only enhances classification accuracy but also resolves the inherent ambiguities found in traditional geochemical diagrams. However, the reliability of current ML models typically depends on the vast archives of labeled samples from the Phanerozoic. When extending research to “deep-time” samples, such as Hadean zircons, the scarcity of labeled data often forces researchers to rely on models trained exclusively on Phanerozoic datasets. This approach is prone to misclassification due to “domain shift,” caused by systematic variations in zircon trace element distributions across different geological eons. To address this challenge, we propose a Domain Adversarial Neural Network (DANN) framework tailored for zircon trace element analysis. By aligning the feature distributions of the source domain (Phanerozoic) and the target domain (Precambrian), the DANN extracts “domain-invariant yet geologically significant” high-dimensional feature representations, effectively mitigating the effects of temporal data bias. Our results demonstrate that DANN significantly outperforms traditional machine learning methods across multiple performance metrics. Furthermore, t-SNE visualization confirms that the source and target domains are effectively aligned within the feature space. When applied to ~4.3 Ga zircon samples from the Jack Hills, the model achieved a classification accuracy of 0.923. This high level of performance underscores the framework’s exceptional generalization capability for identifying unlabeled deep-time samples and its potential for broader applications in Precambrian geology. This study develops a transferable, data‑driven framework for inferring deep‑time geological processes, providing a novel methodology to address the limitations inherent in the traditional principle of uniformitarianism. Furthermore, the framework is extensible to other mineral systems (e.g., apatite, monazite), thereby opening new avenues for quantitatively reconstructing the dynamic evolution of the early Earth.