GeoFedNet: Federated learning for privacy-aware, robust, and generalizable seismic interpretation

Abstract

Seismic structural interpretation is crucial for understanding subsurface geology, particularly in hydrocarbon exploration, as it aids in identifying reservoir formations, assessing drilling risks, and optimizing resource extraction. However, developing a widely generalizable model for seismic interpretation remains challenging due to the limited availability of large-scale public datasets, variations in seismic surveys, and privacy constraints that hinder data sharing. These factors lead to inconsistencies in model performance across diverse datasets, limiting the applicability of existing approaches. To address this gap, we propose a federated learning-based framework for seismic interpretation, enabling distributed model training without requiring direct data sharing. In this approach, local models are trained independently across different clients, and a global model is aggregated to improve generalization across heterogeneous datasets. This method not only preserves data confidentiality but also mitigates challenges related to labeled data scarcity and class imbalance, allowing clients with limited data to benefit from collaborative learning. We evaluate GeoFedNet on three key seismic interpretation tasks: seismic structure classification, salt detection, and facies segmentation. Across all tasks, GeoFedNet achieves performance within 1%–3% of centralized models while significantly outperforming isolated local models by up to 15% in accuracy and generalization. These results demonstrate that our framework can effectively learn from non-IID and imbalanced data without compromising performance. GeoFedNet also shows improved robustness to client variability and better minority class recognition, which are critical in real-world subsurface interpretation scenarios. These findings highlight the potential of federated learning in enabling hydrocarbon companies to collaboratively train robust seismic interpretation models while maintaining data privacy, ultimately improving exploration efficiency and informed decision-making.