AI USE CASE
Seismic Data Interpretation via Deep Learning
Accelerate subsurface mapping for exploration teams using deep learning on seismic datasets.
What it is
Deep learning models trained on seismic waveform data can automate fault detection, horizon picking, and lithology classification tasks that typically require weeks of manual geophysicist review. Automated interpretation can reduce analysis cycle times by 50–70%, enabling faster drill/no-drill decisions. Teams report cutting exploration cycle costs by 20–35% while improving subsurface model accuracy. The approach is particularly impactful in data-rich basins where legacy 2D/3D seismic surveys already exist.
Data you need
Large volumes of preprocessed 2D or 3D seismic survey data (SEG-Y format or equivalent), ideally with expert-labelled horizons and faults for supervised training.
Required systems
- data warehouse
Why it works
- Close collaboration between ML engineers and senior geophysicists throughout model design and validation.
- Starting with a well-labelled legacy dataset from a known basin before expanding to new areas.
- Deploying an explainability layer so geophysicists can audit and correct model predictions.
- Establishing a continuous re-training pipeline as new survey data becomes available.
How this goes wrong
- Insufficient labelled training data leads to poor generalisation across new geological settings.
- Geophysicists distrust model outputs and revert entirely to manual interpretation, eliminating ROI.
- GPU infrastructure costs and data pipeline complexity exceed initial budget estimates significantly.
- Models trained on one basin fail to transfer to different lithologies without costly re-training.
When NOT to do this
Do not pursue this if your seismic dataset covers fewer than two or three surveys with minimal expert annotation — the model will lack sufficient signal to outperform a junior geophysicist.
Vendors to consider
Sources
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