| --- |
| license: mit |
| pretty_name: Seismic Ensemble |
| --- |
| |
| # Seismic Ensemble — Data & Model Store |
|
|
| This repo holds the data and model artefacts for the code at |
| [irp-jas25](https://github.com/ese-ada-lovelace-2025/irp-jas25), a project on multi-class seismic event discrimination (earthquake / deep earthquake / explosion / |
| nuclear explosion / volcanic eruption / noise) for CTBT-style monitoring, using a six-member ensemble |
| (four deep models, two classical) with a stacked meta-learner on top. It accompanies the paper |
| *"Robust and Explainable Multi-Class Seismic Event Discrimination through Benchmarking and Ensemble |
| Learning."* |
|
|
| ## Start here: `CANONICAL/tune5/` |
|
|
| `CANONICAL/tune5/` is the definitive run behind the final report — everything you need to reproduce the |
| paper's tables and figures lives here, and nothing outside it is required to do so. |
|
|
| - **Model checkpoints**: `CANONICAL/tune5/{barama,kasburg,swaine,kong,li,pignatelli}/best_retrain/` — one |
| directory per ensemble member, each with its trained weights (`model.pt` for the three deep models, |
| `model.joblib` for the three classical ones), the config it was trained with, and its Optuna tuning |
| history under the sibling `optuna/` folder. In the paper these six go by codenames: `barama` = CNN, |
| `kasburg` = ViT, `swaine` = SWP-Net, `kong` = Phys-CNN, `li` = XGBoost, `pignatelli` = SVM. |
| - **Main results**: `CANONICAL/tune5/ensemble_hand_plus_ssl_temperature/` and `CANONICAL/tune5/ensemble_hand_plus_ssl` are Temperatuer and Platt |
| - calibrated model outcomes respectively. |
| - **Everything else** in this folder (`exp_vol_*`, `exp_spatial_*`, `exp_hparam_*`, `exp_pick_perturbation`, |
| `ensemble_depth_*`, `anomaly_*`, `screening_*`, `pretrain_mae*`, `tune5_fewshot_*`, |
| `north_korea_padding_attribution`, `meta_learner_shap_comparison`) backs one specific robustness check, |
| ablation, or appendix figure in the paper — these are the raw outputs behind those sections, not |
| alternative models to pick between. |
| - `CANONICAL/tune5/results/splits/` holds the exact train/val/test split used everywhere above, so any of |
| it can be reproduced end to end. |
|
|
| ## Everything else in the repo |
|
|
| `checkpoints/`, `experiments/`, `processed/`, and `UNIFIED/` are kept for completeness and traceability — |
| they're the trail of earlier iterations (`tune4_colab`, `ssl1_colab`, `exp1_colab`, and so on) that either |
| fed into what became `tune5` or were tried and set aside along the way. `UNIFIED/` `processed/` are the |
| dataset at two stages of the pipeline (unified archives and full processing applied)s. |
| If you're only after the results and checkpoints that the paper actually reports, you want |
| `CANONICAL/tune5/` above. |
|
|
