Unsupervised 3D seismic horizon tracking faces a key limitation: signal-based propagators provide accurate trace-level alignment but often fail near faults, whereas texture-driven deep models are more robust to discontinuities, typically at the cost of labeled data requirements and reduced trace-level precision. We propose a self-supervised fusion of both paradigms in which signal-derived local horizon correspondences act as domain-specific priors to train a texture-based deep learning model. Specifically, we estimate reliable trace-to-trace flows from reflector slopes and use them to form positive pairs in a contrastive objective, while restricting training to high-confidence neighborhoods, optionally augmented with a fault mask. The objective is not to infer ambiguous correspondences close to discontinuities, but to preserve horizon identity across them. As a result, the network learns voxel-wise embeddings that preserve local signal continuity while enabling horizon propagation beyond discontinuities through similarity search. Experiments on the public F3 dataset and a faulted synthetic dataset achieve lower mean absolute error (MAE) than unsupervised baselines and competitive performance against a semi-supervised method using a single labeled slice.

翻译：无监督三维地震层位追踪面临一个关键限制：基于信号的传播器能提供精确的迹级对齐，但在断层附近往往失败；而纹理驱动的深度模型对不连续性更具鲁棒性，但通常以标注数据需求和降低的迹级精度为代价。我们提出一种自监督融合两种范式的方法，其中信号衍生的局部层位对应关系作为领域先验知识，用于训练基于纹理的深度学习模型。具体而言，我们从反射层斜率估计可靠的迹间流，并将其用于对比学习目标中的正样本对构建，同时将训练限制在高置信度邻域内，并可选择性地用断层掩码增强。这一学习目标并非推断不连续性附近的模糊对应关系，而是保持跨不连续性的层位同一性。最终，网络学习到的体素级嵌入既能保持局部信号连续性，又能通过相似性搜索实现跨不连续性的层位传播。在公共F3数据集和含断层合成数据集上的实验表明，该方法与无监督基线相比均方绝对误差（MAE）更低，且其性能可与使用单条标注切片的半监督方法相媲美。