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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.

a BEPAD window

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

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