Salt-dome delineation is a critical, high-impact task in subsurface geological interpretation, driving decisions in hydrocarbon exploration, reservoir modeling, and drilling safety. While convolutional encoder-decoder architectures have delivered significant improvements in automated salt segmentation, their widespread application is severely limited by data sovereignty concerns, dataset bias, and the scarcity of labeled seismic volumes. This paper introduces FedSaltNet, a Federated Learning (FL) framework explicitly engineered for robust, generalizable, and privacy preserving salt-dome segmentation. We couple a lightweight Small U-Net backbone, chosen for its efficiency and regularization properties with a novel Foreground-Weighted (FG-WEIGHTED) aggregation strategy designed to tackle domain-specific class imbalance. Through an extensive comparative study emulating non-IID conditions across four diverse seismic datasets (TGS, SEAM, F3, GBS), we demonstrate two critical findings: The FG-WEIGHTED algorithm effectively mitigates data heterogeneity, yielding a 4.0% relative improvement in Intersection over Union (IoU) over the best conventional FL method. The simple U-Net architecture proved essential, outperforming the higher capacity ResNet-18 U-Net variant by 166% in average IoU, underscoring the necessity of architectural simplicity in data-constrained federated environments. FedSaltNet provides a validated, high-performance solution that establishes the viability of federated deep learning for collaborative, next-generation subsurface interpretation.

翻译：盐丘圈定是地下地质解释中一项关键且高影响力的任务，驱动着碳氢化合物勘探、储层建模和钻井安全的决策。虽然卷积编码器-解码器架构在自动盐分割方面取得了显著改进，但其广泛应用受到数据主权问题、数据集偏差以及标记地震数据稀缺性的严重限制。本文提出了FedSaltNet，这是一个专门为稳健、可泛化且隐私保护的盐丘分割而设计的联邦学习框架。我们将轻量级Small U-Net骨干网络（因其高效性和正则化特性而选择）与一种新颖的针对领域特定类别不平衡问题的前景加权聚合策略相结合。通过在四个不同地震数据集（TGS、SEAM、F3、GBS）上模拟非独立同分布条件进行广泛比较研究，我们展示了两个关键发现：前景加权算法有效缓解了数据异质性，交并比相比最佳传统联邦学习方法相对提升了4.0%；简单的U-Net架构被证明至关重要，其平均交并比比更高容量的ResNet-18 U-Net变体高166%，突显了在数据受限的联邦环境中架构简单性的必要性。FedSaltNet提供了一个经过验证的高性能解决方案，确立了联邦深度学习在协作式下一代地下解释中的可行性。