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GeoDeep AI

AI made for the subsurface, and answerable to your experts.

GeoDeep AI brings interpretation, inversion and forecasting models that know their limits. Every result arrives with a confidence measure, a model version and a clear label that sets it apart from human work.

Meet the agents Request a pilot

GeoDeep AI modules

  • AI Interpretation Engine

    Models for faults, horizons, salt bodies, facies and many more objects.

  • Seismic Foundation Model

    Learns general patterns from large volumes of unlabeled seismic data.

  • Active Learning

    Sends experts only the regions where the model is least certain.

  • Labeling Studio

    Experts label data in 2D and 3D, and disagreements are kept rather than lost.

  • Synthetic Data Factory

    Generates realistic synthetic seismic and geological training data.

  • Analog Search

    Finds similar geological structures and seismic patterns across your data.

  • Model Registry

    Versions, approvals and model cards for every model in production.

  • AI Agents

    Specialist agents for data, QC, interpretation, reservoir, drilling and production.

  • Decision Support

    Compares alternative scenarios and documents the reasoning behind a decision.

Models

Twenty model families for subsurface objects.

  1. 01 Fault segmentation
  2. 02 Fault sticks and surfaces
  3. 03 Horizon extraction
  4. 04 Multiple horizon extraction
  5. 05 Salt body segmentation
  6. 06 Channel detection
  7. 07 Seismic facies classification
  8. 08 Geobody segmentation
  9. 09 Unconformity detection
  10. 10 Stratigraphic termination classification
  11. 11 Gas chimneys and direct hydrocarbon indicators
  12. 12 Seismic denoising
  13. 13 Trace interpolation
  14. 14 Super resolution
  15. 15 Velocity model prediction
  16. 16 Impedance and elastic inversion
  17. 17 Lithology and fluid probability
  18. 18 Closure and prospect detection
  19. 19 4D change detection
  20. 20 Analog structure search

Foundation model

One foundation, adapted to every basin.

GeoDeep pretrains a shared 3D encoder on large volumes of unlabeled seismic data, then adapts it to each task and field with a small number of expert labels. Splits by survey and tests that hold out one survey at a time show how a model behaves on data it has never seen.

  1. 01 Models for specific tasks
  2. 02 Shared 3D encoder
  3. 03 Multitask pretraining
  4. 04 Pretraining on your own unlabeled data
  5. 05 GeoDeep Seismic Foundation Model
  6. 06 Adaptation with few labels
  7. 07 Adapters per region and field

Active learning

Experts teach the model where it matters most.

  1. The model analyzes the full volume.

  2. It finds regions with low confidence and high information value.

  3. The expert corrects only those regions.

  4. Corrections are stored as training labels.

  5. The model is retrained and compared in a blind test.

  6. It reaches production only after approval.

Confidence

Every answer comes with its confidence.

  • Deep ensembles, Monte Carlo dropout and test time augmentation
  • Calibrated probabilities and conformal prediction
  • Detection of unfamiliar data the model was not trained on
  • Model disagreement maps and a clear confidence status

Model governance

Models are governed like any critical asset.

  1. Define the problem and acceptance metrics

  2. Freeze the dataset version

  3. Check label quality

  4. Split the data by survey

  5. Train a baseline

  6. Run a blind test

  7. Expert assessment

  8. Publish a model card

  9. Register as a candidate

  10. Approve for production

  11. Monitor drift and performance

  12. Retrain with new data

Every model card records

  • Purpose, training data and data rights
  • Supported regions, survey types and input formats
  • Metrics, known limits and behavior on unfamiliar data
  • Uncertainty method, approving expert and last validation date
  • Cases where the model must not be used

Evaluation

Measured on what geoscientists care about.

High accuracy alone does not make a model ready. GeoDeep also measures geological continuity, boundary quality, generalization to new surveys and how often experts accept the result.

Task Key metrics
Fault segmentation Dice, IoU, precision, recall, fault continuity
Horizon extraction MAE in samples or milliseconds, gap rate, topological consistency
Salt segmentation Dice, boundary F1, Hausdorff distance
Facies classification Macro F1, recall per class, confusion matrix
Denoising SNR, SSIM, frequency preservation, interpreter review
Inversion RMSE, correlation, well holdout, residual
Velocity model Model error, image focusing, well tie
Production forecast MAE, MAPE, interval coverage, backtest
Risk prediction Recall, false alarm rate, lead time
Uncertainty Calibration error, coverage, detection of unfamiliar data

GeoDeep AI agents

Model power, in the hands of specialist agents.

Eleven agents prepare data, check quality, draft interpretations, explain results and write reports. Each one works only through validated tools and with expert approval.

Meet the agents

Cases

Cases from this layer.

All cases

Pilot

Start with a pilot on your own data.

We set up a secure pilot environment for you and load one of your 3D surveys together with its wells. Your own experts then judge the results against the way you work today.

Request a pilot info@geodeep.ai