Grillo OpenEEW Archive
Grillo OpenEEW Archive
OpenEEW publishes raw IoT accelerometer records for earthquake early-warning research as public JSON Lines objects partitioned by country, device and time, plus sensor-location metadata. Records include country_code, device_id, x, y and z sample arrays, device and cloud timestamps and sampling rate; no AWS account or key is needed.
Required Attribution:
OpenEEW was accessed on DATE from https://registry.opendata.aws/grillo-openeew.
Coverage
The registry names deployed sensors in Mexico, Chile, Puerto Rico and Costa Rica, while the checked bucket also exposes a New Zealand record prefix. Archive paths checked begin by November 2017, and the registry states an update frequency of approximately every five minutes. Coverage depends on participating devices, their uptime and their locations.
Verdict
A raw ground-motion signal for building and testing earthquake detection models across a distributed sensor network. It is unprocessed accelerometer data rather than a confirmed earthquake catalog or production alert stream. Device coverage, sampling continuity, timing differences and local noise must be handled before comparing stations. The registry advertises five-minute updates, but consumers should inspect object dates because the checked sample and location files are historical.
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Use in your agent
Load one OpenEEW sensor window into pandas and Parquet. Checked against the provider's documentation on Nov 1, 2026.
Access
- No AWS account or key. S3 listing and record objects are public.
- This bounded example reads one five-minute Mexico sensor object from 2017.
Request
curl -O 'https://grillo-openeew.s3.amazonaws.com/records/country_code=mx/device_id=000/year=2017/month=12/day=01/hour=00/00.jsonl'Format
JSON Lines records with country_code, device_id, x/y/z arrays, Unix device_t and cloud_t timestamps and sr sampling rate.
Load into pandas and Parquet
Needs Python 3 with requests, pandas and pyarrow.
"""One OpenEEW five-minute sensor object -> pandas -> Parquet. No key."""
import io
import requests
import pandas as pd
URL = "https://grillo-openeew.s3.amazonaws.com/records/country_code=mx/device_id=000/year=2017/month=12/day=01/hour=00/00.jsonl"
r = requests.get(URL, timeout=60)
r.raise_for_status()
df = pd.read_json(io.BytesIO(r.content), lines=True)
df["device_t"] = pd.to_datetime(df["device_t"], unit="s", utc=True)
df["cloud_t"] = pd.to_datetime(df["cloud_t"], unit="s", utc=True)
df.to_parquet("openeew_mx_000_2017_12_01_0000.parquet", index=False)
print(len(df), "accelerometer rows", df["device_t"].min().isoformat(), "to", df["device_t"].max().isoformat(), "columns", ", ".join(df.columns))Run by us on 2026-11-01 (python 3.13.5 | pandas 3.1.0 | pyarrow 26.0.0 | requests 2.34.2). Result: 284 accelerometer rows 2017-12-01T00:00:00.460999966+00:00 to 2017-12-01T00:04:59.250999927+00:00 columns country_code, device_id, x, y, z, device_t, cloud_t, sr
Limits
- Rate limit: Not stated.
- The registry states updates occur approximately every five minutes.
- Public S3 record objects are partitioned by country, device, year, month, day and hour.
Quirks
- device_t and cloud_t are Unix seconds with fractional precision, not milliseconds.
- Each row contains arrays of axis samples; explode x, y and z together before sample-level analysis.
- Raw acceleration is not an event label, and sensor noise or gaps can resemble signal changes.
Terms and attribution
- AWS data is CC BY-SA 4.0; credit the source and preserve share-alike obligations.
- The repository's Apache-2.0 statement covers the project software, not the AWS data licence.
Sources
- https://registry.opendata.aws/grillo-openeew/: “Update Frequency Approximately every 5 minutes”
- https://registry.opendata.aws/grillo-openeew/: “AWS CLI Access (No AWS account required)”
- https://github.com/openeew/openeew#license: “OpenEEW data on AWS is licensed under CC BY-SA 4.0”
- https://github.com/openeew/openeew: “Entire archive of unprocessed accelerometer data from Mexico, Chile, and other countries”
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