Dataset and Code Accompanying "Challenges and Learning from Exploring Deep Metric Learning for Identifying Seismic Stratigraphy"
<p>Dataset and code related to the work outlined in "Challenges and Learning from Exploring Deep Metric Learning for Identifying Seismic Stratigraphy".</p> <p>File Description:</p> <ul> <li>seis_seg.h5 - trained model weights.</li> <li>topseis_temp.npy - seismic survey.</li> <li>train_demo.ipynb - training demo including synthetic modelling pipeline.</li> <li>inference_and_plot - inference and reproducing main results.</li> </ul> <p>Pre-calculated phase volume and encoded features for reproducing figures:</p> <ul> <li>unwrapped_phase_volume_full.npy.</li> <li>features_full_ds222_ps32_s222.npy.</li> <li>indices_full_ds222_ps32_s222.npy.</li> </ul>
ShareScore
36/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 4
- Harmonization
- 4
- Access
- 20
- Reuse readiness
- 8
- Engagement
- 0
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