README.md

metadata

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, 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.