Contrastive Learning for Seismic Horizon Tracking with Domain-Specific Priors

2026-06-15

AI Summary

Unsupervised 3D seismic horizon tracking often fails near faults: signal-driven methods offer high precision but poor robustness, while texture-driven approaches handle discontinuities yet rely on labels and suffer from limited local accuracy. This work proposes a self-supervised contrastive learning framework that integrates both signal and texture cues, introducing for the first time inter-trace flow derived from reflection dip estimation as a domain-specific prior. Positive sample pairs are constructed within high-confidence neighborhoods to propagate horizon identity consistently across faults. The method trains a texture-aware voxel embedding model by combining high-confidence region constraints with an optional fault mask. Evaluated on the public F3 dataset and synthetic data with faults, the approach achieves a mean absolute error (MAE) significantly better than unsupervised baselines and comparable to semi-supervised methods using only a single annotated slice.

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.

Problem

• seismic horizon tracking
• unsupervised learning
• fault discontinuities
• domain-specific priors
• contrastive learning

Innovation

• contrastive learning
• seismic horizon tracking
• domain-specific priors
• self-supervised learning
• fault-robust embedding