arXiv:2606.16271v1 Announce Type: cross 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 fl
The increasing sophistication of AI models and the critical need for more accurate subsurface analysis in energy exploration and management are driving this innovation now.
Improved seismic horizon tracking with AI reduces exploration risk and optimizes resource extraction, impacting energy security and technological efficiency.
This method introduces a more robust and self-supervised approach to 3D seismic analysis, combining the strengths of traditional signal processing with advanced deep learning, reducing reliance on extensive labeled datasets.
- · Oil and Gas Exploration Companies
- · Geophysical Software Vendors
- · AI/ML Research in Geosciences
- · Energy Sector Data Scientists
- · Traditional Seismic Interpretation Services
- · Manual Data Labeling Operations
More efficient and accurate identification of geological structures for resource exploration and carbon sequestration.
Reduced operational costs and improved success rates in drilling and reservoir management.
Potential acceleration of new energy resource discoveries and optimized management of existing reserves, impacting global energy supply dynamics.
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Read at arXiv cs.LG