Contrastive Learning for Seismic Horizon Tracking with Domain-Specific Priors

• URL: http://arxiv.org/abs/2606.16271v1
• Date: Mon, 15 Jun 2026 06:19:47 GMT
• Title: Contrastive Learning for Seismic Horizon Tracking with Domain-Specific Priors
• Abstract summary: We propose a self-supervised fusion of signal-derived local horizon correspondences and texture-based deep learning models.<n>The network learns voxel-wise embeddings that preserve local signal continuity while enabling horizon propagation beyond discontinuities.
• Score: 45.20665924864221
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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