GRIDINT RESEARCH · DATA HUB
Seismic.
Rigorous reporting on the planet's recent seismicity — USGS events organized and verified on schedule, every figure traceable back to the source that owns it.
Every giant, on one clock.
Plot it on any two axes.
The shape of the records.
Records by depth class
SOURCE · USGS FDSN event service (M5.5+, ~4 months) · data cutoff 2026-08-26 · counted from the records
How this hub was made.
What this set is. The big-shocks archive on its own cut — a different population from the half-century M7.5+ record the report publishes, and both are kept because forcing one file onto two honest populations would be the false line the launch scatter refused. 178 records. This pack states no retrieved date — when it was pulled is not recorded, and is not guessed at here.
Records are the USGS FDSN event service's own, M5.5 and above over roughly four months, pulled on a six-hour schedule. Depth classes follow the 70/300 km convention. We never create data and never provide it: downloads here are verification extracts — the working records with their receipts, so you can check us against the source.
Every edition, dated.
- Edition 1 published 2026-08-27 · data cutoff 2026-08-26 · first edition
A hub changes only as a new dated edition, on the institute's word — never on a schedule. Collection continues underneath; publication is deliberate.
Verify this hub.
Verification extracts — the working records with their receipts, attribution first, pointing at the origin. We never provide data; the source is the authority, this hub is the lens.
Cite this hub
GRIDINT Research (2026). Seismic data hub, edition 1 (data cutoff 2026-08-26). https://gridint.ai/data/seismic
@misc{gridint_seismic_2026,
title = {Seismic data hub, edition 1},
author = {{GRIDINT Research}},
year = {2026},
note = {Data cutoff 2026-08-26; source: USGS FDSN event service (M5.5+, ~4 months)},
url = {https://gridint.ai/data/seismic}
}
In pandas
import pandas as pd
D = pd.read_json("https://gridint.ai/demo/seismic/explorer-records.json")
records = pd.DataFrame(D["rows"].tolist(), columns=["mag", "depth_km", "lat", "lon", "depth_class", "place"])
The first row of that extract, as it stands: [5.5, 528, -23.35, 179.82, "Deep (>300km)", "south of the Fiji Islands"] — the values are the receipt, whatever a column is called.