[P] ML for oil exploration using seismic interpretation

I am working on applying AI/ML to seismic interpretation for oil exploration

The problems are classic pattern recognition but with hard constraints:

• Very low signal to noise ratio

• Sparse and uncertain labels

• Features that are visually interpretable to geoscientists but difficult to formalize (continuity, terminations, subtle amplitude changes)

Typical use cases include reservoir body detection (channels, lobes) and separating geological signal from acquisition or processing artifacts.

For people who have worked on scientific or medical style imagery:

• Do weakly supervised or self supervised approaches actually hold up in this kind of data?

• What are the main failure modes when data quality and labels are poor?

• Where do models usually break compared to expectations from papers?

Looking for practical insight rather than theory.

Thanks for yall help 🙂