How SubsurfaceAI Delivers Core Steps—Today
Artificial intelligence (AI) and machine learning (ML) are no longer side projects in subsurface workflows; they are embedded in the daily practice of geology and geophysics (G&G). The key question is no longer “Can AI help?” but “How do we deploy AI so interpreters move faster and stay in control?”
This article answers that question through the lens of SubsurfaceAI—an AI-driven interpretation and modeling platform that operationalizes all six core steps of seismic interpretation, from raw data conditioning to reservoir characterization.
Bottom line: AI will not replace interpreters. It will replace repetitive clicking, manual cross-plotting, and tedious rework that slow them down. SubsurfaceAI embodies this shift with a production-ready, coding-free platform designed for human-in-the-loop QC at every stage. That’s why multiple supermajors have already adopted SubsurfaceAI: it delivers faster cycles, more consistent results, and clear uncertainty for better business decisions.
Seismic Interpretation in the AI Era: At a Glance
- All steps, one platform: data conditioning, seismic–well ties, fault picking, horizon picking, velocity modeling for time–depth conversion, and reservoir characterization.
- No coding required: point-and-click workflows with guided wizards keep geoscientists productive—no Python, no scripts.
- Interpreter in control: seed-and-review loops, uncertainty maps, and audit trails make AI outputs transparent and editable.
- Built for operations: scalable processing on large 3D volumes, robust handling of noisy data, and full project governance.
- Immediate impact: order-of-magnitude productivity gains, faster velocity updates, and deployable property maps with quantified uncertainty.
- Integration: ML models integrate seismic attributes, well data, production, and microseismic into unified results for decision-making.
- Data Conditioning: Clean, Balance, and Prepare
Purpose
Enhance signal quality—improving S/N ratios, suppressing multiples and footprint, regularizing sampling, and balancing amplitudes for reliable downstream interpretation.
How SubsurfaceAI does it
- AI denoising with self-supervised U-Net models (Noise2Void/Noise2Self).
- Multiple suppression via CNNs in t–x/tau–p domains, with physics-based SRME for QC.
- Interpolation and inpainting using context encoders and low-rank tensor methods.
- Wavelet/spectral balance via neural blind deconvolution with geologic guardrails.
Why it works for non-coders
Simple presets (“Denoise,” “Destripe,” “Inpaint”) with intuitive sliders—no notebooks, no parameter hunts.
Case snapshot
On a noisy public 3D, SubsurfaceAI reduced false positives in fault probabilities and streamlined manual cleanup.
- Seismic–Well Ties: Fast, Defensible Alignment
Purpose
Accurate synthetics, wavelet estimation, and time–depth functions—the foundation of mapping and inversion.
How SubsurfaceAI does it
- Neural blind deconvolution for spatially consistent wavelets.
- Dynamic Time Warping (DTW) for local misties.
- Log completion with gradient-boosted trees for missing density.
- Uncertainty-aware T–Z curves via spline/GP fits.
Why it works for non-coders
A guided wizard with QC plots and misfit curves makes alignment transparent.
Case snapshot
Across 500+ wells, SubsurfaceAI standardized the wavelet and applied DTW locally, reducing variability and improving tie quality.
- Fault Picking: Probability Volumes to Editable Surfaces
Purpose
Faster, more complete detection of discontinuities and fault networks.
How SubsurfaceAI does it
- 3D U-Nets and structure-tensor inputs for robust probabilities.
- Transformer context to maintain continuity.
- Post-processing (NMS, thinning, RANSAC) for geologic coherence.
- Active learning loops with quick user feedback.
Why it works for non-coders
One click generates fault probabilities; uncertainty maps show where human editing adds the most value.
Case snapshot
Recovered major corridors and subtle splays missed in manual interpretation, while focusing human time on ambiguous zones.
- Horizon Picking: Constrained Tracking, Basin-Scale Consistency
Purpose
Track reflectors through structural complexity, thin beds, and facies changes—while honoring faults.
How SubsurfaceAI does it
- Multi-horizon segmentation with U-Net/DeepLab.
- Vision Transformers for basin-scale continuity.
- RGT/Wheeler-domain integration for cycle-skip control.
- Constraint engine (seeds, ordering, fault planes).
Why it works for non-coders
Minimal seeding plus stratigraphic rules; AI handles the rest. Wheeler-domain viewer highlights possible skips.
Case snapshot
On a turbidite margin, horizons tracked coherently through muted fills, preventing mis-ranking and yielding a clean, editable stack.
- Velocity Modeling & Time–Depth Conversion
Purpose
Reliable interval velocity models and T–Z functions for depth mapping and volumetrics.
How SubsurfaceAI does it
- CNN-assisted semblance autopicking with confidence scoring.
- Gaussian Process trends fusing checkshots/VSP.
- Physics-guided updates accelerated by Fourier Neural Operators.
- Bayesian T–Z fusion for credible intervals.
Why it works for non-coders
Users see AI-proposed velocity functions on familiar panels with standard QC plots.
Case snapshot
Reduced click-counts by an order of magnitude and delivered depth maps with uncertainty bands that improved well-planning discussions.
- Reservoir Characterization: From Attributes to Properties
Purpose
Predict lithofacies, porosity, net-to-gross, and geobodies—with quantified uncertainty.
How SubsurfaceAI does it
- Supervised models (RF, XGBoost, 3D CNNs).
- Contrastive/self-supervised learning where labels are scarce.
- Physics-informed inversion hybrids for realistic predictions.
- Generative ensembles (VAEs/diffusion) for scenario analysis.
Why it works for non-coders
Step-by-step templates with SHAP explainability and residual maps standard.
Case snapshot
Generated net-to-gross volumes with uncertainty maps that guided appraisal wells toward the highest-value information.
- Subsurface Data Integration
ML models fuse seismic attributes, well logs, production data, and microseismic measurements into a unified subsurface model for decision-making. Outputs include high-resolution sweet-spot maps and 3D property grids that delineate the spatial distribution of target reservoirs with higher resolution, improved accuracy, and greater confidence.
Why Supermajors Choose SubsurfaceAI
- Cutting-edge, operational today: U-Nets, Transformers, diffusion models—wrapped with domain constraints and QC guardrails.
- Immediate business value: faster fault/horizon frameworks, standardized ties, velocity updates, and property maps with defensible uncertainty.
- No-code usability: designed for geoscientists, not coders.
- Interpreter authority preserved: AI proposes; interpreters decide.
- Enterprise governance: reusable labels and models stored in a governed corporate repository.
Will AI Replace Interpreters?
No. AI replaces repetition and inconsistency, not expertise. Interpreters evolve into system architects and scientific editors—curating inputs, setting constraints, validating plausibility, and communicating uncertainty.
SubsurfaceAI is built for this reality: delivering speed and consistency while keeping geoscientists in control.
Operating Principles Baked into SubsurfaceAI
- Human-in-the-loop: active learning, tie misfit metrics, and velocity confidence bands.
- Physics + data: SRME and wave-equation checks alongside learned denoising and surrogate updates.
- Governance & reproducibility: versioned models, input lineage, repeatable runs across vintages and assets.
Conclusions
SubsurfaceAI proves that AI in seismic interpretation is not a research demo but a working platform that spans the seven essential steps:
- Data conditioning
- Seismic–well ties
- Fault picking
- Horizon picking
- Velocity modeling & time–depth conversion
- Reservoir characterization
- Subsurface data integration
It is robust, intuitive, and coding-free—allowing G&G experts to focus on transforming data into geologic insight. That’s why leading operators, including supermajors, are standardizing on SubsurfaceAI to modernize interpretation and realize immediate value in E&P operations.
If you’re ready to shift time from drawing to deciding, SubsurfaceAI is ready today.
