Horizon interpretation is one of the most critical tasks in any seismic interpretation project, whether you are working with 2D or 3D seismic data. Horizons define the boundaries between geological layers and are essential for understanding the subsurface architecture of oil and gas reservoirs. Yet, horizon picking is notoriously time-consuming and prone to interpreter bias, especially across large seismic datasets. Accurate and efficient horizon interpretations can directly impact exploration and production (E&P) outcomes by accelerating decision-making and reducing the risk associated with subsurface uncertainties.
Traditionally, seismic interpreters spend weeks or even months manually picking horizons section by section, especially when working on extensive 3D seismic surveys. For example, interpreting five horizons across a 6,000 km² 3D seismic survey could take an experienced geophysicist over three months of dedicated work. Today, with advanced AI-powered seismic interpretation tools like those offered by SubsurfaceAI, the same task can be completed in just a few days — dramatically transforming both productivity and confidence in the results.
In this blog, we explore why AI should play a central role in your seismic interpretation workflows, how it enables faster and more accurate horizon picking, and why upgrading from traditional legacy software to an AI-infused seismic interpretation platform is essential for modern subsurface analysis.
The Shift from Manual to AI-Powered Horizon Interpretation
The world is witnessing a rapid transformation driven by artificial intelligence across industries, and seismic interpretation is no exception. While traditional interpretation methods rely on following waveform characteristics such as peaks, troughs, and zero crossings, these approaches can struggle when seismic data quality is variable, noise levels are high, or when geological features are complex.
AI offers a fundamentally different approach. By leveraging pattern recognition, deep learning, and sequence prediction, AI can detect and classify seismic patterns beyond the limitations of manual or traditional auto-tracking methods. SubsurfaceAI’s technology represents the cutting edge of this evolution, combining powerful AI algorithms with advanced auto-tracking capabilities, allowing interpreters to extract horizons with greater speed and precision.
Horizons: The Foundation of Seismic Interpretation
Horizons represent stratigraphic boundaries — the surfaces separating geological layers deposited through time. Interpreting these boundaries accurately is critical for constructing subsurface models, estimating reservoir volumes, planning drilling programs, and reducing exploration risks.
However, horizons are rarely simple or continuous in real-world data. Faulting, stratigraphic pinch-outs, complex depositional features such as channels and reefs, and seismic noise can obscure or disrupt horizons, making them difficult to trace manually. Even skilled interpreters can introduce inconsistencies when picking horizons across hundreds or thousands of kilometers.
This is where AI steps in to revolutionize the process.
The AI-Infused Horizon Interpretation Workflow
SubsurfaceAI’s horizon interpretation technology is built around a four-step process designed to combine human expertise with AI’s computational power:
- Build an AI model for seismic sequence prediction. Interpreters start by picking key horizons on a limited number of representative seismic sections. These labeled sections are used to train a Seismic Sequence AI (SSA) model, teaching the AI to recognize patterns corresponding to different seismic sequences.
- Validate the AI model. Once trained, the SSA model is validated through blind testing on sections excluded from the training set. This step ensures the model’s predictions are accurate, reliable, and generalize well to new data.
- Apply the SSA model across the survey. The validated SSA model is then applied to the entire 3D seismic volume or all 2D sections in the survey. This produces a Sequence Volume or Sequence Sections that classify the seismic data into distinct sequences across the entire dataset.
- Extract horizons. Horizons are extracted directly from the classified Sequence Volume or Sections. When available, fault probability volumes can be integrated to guide horizon extraction through structurally complex areas, improving continuity and accuracy.
Why AI-Based Horizon Picking Outperforms Traditional Methods
AI-based horizon interpretation has clear advantages over manual or waveform-based auto-tracking techniques:
✅ Robustness to seismic noise. Traditional methods falter when data quality is compromised. AI models, trained on real seismic patterns, are far more resilient to noise and can deliver reliable horizon picks even in challenging datasets.
✅ Beyond peaks and troughs. AI doesn’t rely solely on simple waveform features. Instead, it recognizes entire reflection patterns, enabling it to identify geological horizons that don’t align with consistent picks or troughs — for example, horizons affected by polarity reversals, subtle stratigraphic features, or discontinuous reservoirs like channel sands.
✅ Dramatic efficiency gains. Horizon picking that previously required months of labor-intensive interpretation can now be completed in a matter of days. As noted earlier, SubsurfaceAI reduces the time to interpret five horizons across a massive 6,000 km² 3D survey from three months to just three days, without sacrificing accuracy.
✅ Seamless integration of expert knowledge. By training AI models on interpreter-labeled data, SubsurfaceAI allows interpreters to embed their geological knowledge directly into the AI, capturing and digitizing decades of experience that can be reused, refined, and shared with colleagues.
✅ Reuse of interpretation across projects. AI models trained on one dataset can be applied to other seismic surveys with similar geological settings, multiplying the value of initial interpretation efforts. SubsurfaceAI’s advanced model and label management system makes it easy to store, manage, and deploy AI models across corporate projects, maximizing efficiency and consistency across your asset teams.
Complementing Your Geological Intuition with AI
Rather than replacing interpreters, AI augments your geological intuition by taking over repetitive and time-consuming tasks, freeing you to focus on high-value interpretation work such as integrating geological, petrophysical, and engineering data. When interpreters pick key horizons to train AI models, they transfer their expertise into the system, which can then scale that knowledge across the entire survey — and beyond.
SubsurfaceAI’s centralized corporate database ensures that AI models and labeled data are organized and accessible, making it easy for teams to share insights, standardize interpretations, and accelerate projects, even when working across multiple assets or regions.
Real-World Impact: Speed, Consistency, and Reduced Risk
By dramatically reducing the time required for horizon interpretation, SubsurfaceAI helps companies make faster E&P decisions — essential when competing for acreage, optimizing drilling schedules, or evaluating potential acquisitions. Faster interpretations mean more time to integrate additional data, refine subsurface models, and reduce exploration and development risks.
Moreover, the consistency offered by AI reduces interpretation variability between different interpreters or teams, ensuring that critical decisions are based on reliable, reproducible data.
Embrace the Future of Seismic Interpretation
AI-powered seismic interpretation is not a futuristic luxury — it’s a practical, proven solution available today. By integrating AI into your seismic workflows, you gain the ability to interpret horizons more quickly and accurately, capture and reuse your team’s geological expertise, and standardize interpretations across projects and teams.
Would you like to see how SubsurfaceAI can revolutionize your seismic interpretation projects? Contact our team today or visit subsurfaceai.ca to learn how you can transform your workflows, reduce interpretation time, and make better-informed E&P decisions.
