---
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.