BEPAD — a benchmark dataset for seismic phase association
A phase associator groups the P and S picks of a seismic network into earthquakes. BEPAD gives thousands of association problems built from real earthquakes and real picks (UTDQuake), with the answer for every pick.
One window (Texas network): (a) earthquakes and stations, (b) every pick at its distance from its earthquake, (c) picks by type.
import bepad
test = bepad.load("test", labels=False) # download the test split from Hugging Face
for w in test: # one window = one association problem
w.picks, w.stations # picks (station, P/S, time) and station coordinates
report = bepad.evaluate(predictions, test) # score your associator
Share your associator and get on the leaderboard in four commands:
bepad new my-method # template folder
bepad run associators/my-method --split test --out results/my-method
bepad submit results/my-method --code associators/my-method # opens a Hugging Face pull request
| What is in a window | several overlapping earthquakes on one network, with real, generated (plausible extra arrivals) and noise picks; every pick labelled with its earthquake and a confidence prob (≥ 0.5 arrival, 0 noise) |
| Difficulty | 23 scenarios: 1–20 earthquakes per window × 5 noise levels, plus earthquakes outside the network |
| Size | per network and scenario: 1,000 train · 200 val · 100 test windows (23 scenarios) |
| Associator authors | fill in bepad.Associator, run on test, submit to the leaderboard — see Contribute your associator |
| Deep learning | bepad.torch.ReleaseWindowDataset — see Train a model |
Code: https://github.com/ecastillot/BEPAD · Data: https://huggingface.co/datasets/ecastillot/BEPAD · Documentation: docs/ (Read the Docs). Install: pip install bepad (or pip install -e . from
this folder; .[dl] for PyTorch, .[build] to rebuild the dataset).
Repository
bepad/ load, evaluate, Associator, leaderboard, torch, plots (what users need)
bepad/build/ pool, labels, windows, release (how the dataset is built)
configs/ pool.yaml (quality control, split, labels), v0.1.yaml, dev.yaml (scenarios, sizes)
docs/ Read the Docs sources; docs/gallery/ = the code of every figure
associators/ contributed associators, one folder each (pyocto = worked example)
pipeline/ the recipe of a release (SLURM): pool -> release -> baselines (pipeline/README.md)
hf/README.md Hugging Face dataset card
License and citation
Code: GPL-3.0. Data: the terms of the original providers, as in UTDQuake. Please cite UTDQuake and the network operators of the data you use.
Metadata
Release files for bepad 0.1.1
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Total release size: 165.5 kB
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