AI & ML Are Transforming Seismic Interpretation – Here’s How
The seismic interpretation software landscape is undergoing a seismic shift. Thanks to advances in artificial intelligence (AI) and machine learning (ML), what once took geoscientists weeks or months now takes hours or days, with greater precision and objectivity. At subsurfaceAI, we’re proud to be at the forefront of this transformation, helping geoscientists make better decisions, faster.
Here’s a snapshot of how AI/ML is reshaping seismic interpretation workflows—and what it means for the future of geoscience.
Step One: Clean, Clear Seismic Data
Quality input is everything. AI-powered data conditioning and super-resolution tools drastically improve the clarity of seismic volumes. Deep learning models—like U-Nets—can suppress noise and sharpen thin beds, revealing features that were previously blurred or invisible. This gives interpreters a cleaner foundation for all downstream tasks, from attribute analysis to horizon tracking.
Smarter Correlation, Less Manual Work
Traditionally, correlating well logs and picking formation tops required aligning curves one-by-one—tedious and error-prone. With ML, this becomes a pattern recognition task. Algorithms like dynamic time warping and neural nets now auto-correlate hundreds of wells, delivering consistent picks in hours instead of weeks. The result? More robust stratigraphic frameworks with fewer mistakes.
Seamless Seismic-to-Well Ties
Aligning well data with seismic is crucial but time-intensive. AI now helps by optimizing synthetic-to-real seismic matching—dramatically accelerating well ties. Instead of tying a few “key” wells manually, we can now calibrate full fields. This leads to better velocity models, more accurate depth conversions, and fewer drilling surprises.
Fault & Horizon Interpretation at Scale
Fault detection and horizon picking used to rely on manual picks and amplitude following—methods prone to human error and bias. AI flips the script. CNNs and unsupervised models can scan entire 3D seismic volumes, outputting fault probability cubes or continuous horizon surfaces in one go. More importantly, they often detect subtle features humans miss. Our tools don’t just accelerate interpretation—they improve it.
Uncovering Hidden Geological Patterns
Seismic facies classification is another big win. AI can handle dozens of attributes at once, clustering data into meaningful geologic segments using both unsupervised and supervised learning. Whether it’s channel sands, turbidites, or shales, facies volumes help geoscientists understand depositional environments faster and in more detail than manual methods allow.
Predicting Rock & Fluid Properties
Why stop at structure? AI now predicts quantitative properties—like porosity, lithology, and saturation—directly from seismic. By training on well log data, ML models generate “pseudo-logs” at every trace, creating 3D property volumes that rival traditional inversion methods. This helps teams pinpoint sweet spots, estimate hydrocarbon volumes, and reduce uncertainty in development plans.
Geobody Extraction – Smarter, Faster
Geobodies—like bars, channels or reefs—are vital to reservoir development. AI models trained to recognize specific geologic shapes can extract these in seconds. We’ve seen AI identify channel systems or salt bodies that were nearly invisible in raw data. Once extracted, geobodies feed directly into volume and economic estimates, improving planning and de-risking exploration.
More Accurate Velocity Models
Velocity models are the backbone of depth conversion. Instead of making velocity surfaces or interpolating between wells, ML learns the relationships between well ties, stratigraphy, and interval velocities to produce 3D velocity volumes with detailed, geologically consistent models. The result? Depth predictions that match real drilling data, honouring multiple picks of the same well tops in horizontal wells, improving everything from reservoir mapping to well placement.
Augmenting, Not Replacing, the Geoscientist
Importantly, AI doesn’t replace human interpreters—it empowers them. The role of the geoscientist is evolving from manual mapper to critical thinker. With AI handling the repetitive, data-heavy work, interpreters can focus on geologic reasoning and scenario evaluation. The synergy between AI tools and domain expertise yields the best results.
Real-World Wins
User friendly software tools developed by subsurfaceAI Inc. have already delivered measurable benefits. From improved well placement in turbidite fields to faster seismic-to-well calibration across shale plays, the outcomes are clear: faster turnaround, better predictions, and fewer surprises in the field. One case even showed that AI-led facies identification led to 15% higher production from optimally placed wells.
Looking Ahead: Foundation Models & Multimodal Integration
The future is even more exciting. Foundation models trained on broad datasets are beginning to generalize across geologies and tasks. We see a world where interpreters interact with data through natural language—asking an AI to “find probable gas-charged channels” and receiving instant, interpretable answers. Integration of logs, seismic, core photos, and production data into unified models will become standard, making subsurface understanding more holistic than ever.
In Conclusion
AI and ML have moved from theory to practice in seismic interpretation. They enhance accuracy, accelerate workflows, and make the interpreter’s job more impactful. At subsurfaceAI Inc., we believe this is just the beginning. The tools are here. The data is rich. And the potential for smarter, faster decisions is enormous.
As we continue to push boundaries in subsurface analysis, we invite geoscientists, data scientists, and innovators to explore what’s possible with us.
