All data contributed to OpenEEW is available as a dataset with AWS OpenData.
This incredible resource dates back to the end of 2017, features data from various countries, and contains some very large magnitude earthquakes (the ones we care about for EEWs!).
A note on terminology
Within the data, we use the term
device for each IoT accelerometer and
record for each set of accelerometer data that the devices send to the cloud. Therefore, the OpenEEW data consists of many records for many devices.
How are records generated?
Each device contains a low-noise MEMS accelerometer that provides acceleration values for the three axes x, y and z. These values are returned in gals, where 1 gal = 1 cm/s². In order to send these values to the cloud in real time, devices collect a small set of triaxial readings, typically either 32 or 125, and send them, together with the Unix time (denoted
device_t) corresponding to the final reading, as a JSON:
sr field contains the accelerometer sample rate, which is the number of samples per second. Using this value, it is possible to assign timestamps to the individual (x, y, z) sample points by subtracting a suitable multiple of
device_t (or from
cloud_t, which is introduced below).
In the above example where the sample rate is 31.25, if each array has length 32, then we would expect a new record approximately every 1.024 seconds.
What happens in the cloud?
Once data reaches the cloud, an additional Unix time is added, denoted
cloud_t, to indicate the time of arrival. Historically this has been useful for sensors that don't include a GPS module fo accurate timekeeping, such as the MX-series. New sensors typically include a GPS module and so the
device_t is preferable over
cloud_t. In rare cases where two records have the same
device_t can be used to determine the correct order (or vice versa).
We also append the
device_id fields to identify which device sent the record. So the final record looks like this:
Where is OpenEEW data stored on AWS?
As shown on the AWS Registry of Open Data, OpenEEW data is available from an S3 bucket called
grillo-openeew in the region
us-east-1. This data is publicly available and does not require an AWS account to access it, although having one provides additional options for working with the data.
You can view and download the OpenEEW records using this file browser.
How are records organized on AWS?
Records are assigned to files according to country and device, and then by date based on the
cloud_t field. A typical file name (or key) has the following structure:
For example, the sample record from above can be found in the file:
Note that the
<minute> value is currently set to increment by five minutes, i.e. 00, 05, 10, etc. The corresponding five-minute intervals of each file are inclusive at the lower end and exclusive at the upper end. For example, if
cloud_t for a record corresponds to exactly 40 minutes and 0 seconds, that record will be assigned to the file ending
/40.jsonl (as opposed to
.jsonl file extension indicates that records are stored as newline-delimited JSON objects. This means each file contains one JSON per line, where each JSON is a single record with a structure as described above.
And device metadata?
In order to work with these records, some basic metadata about the devices is required, especially their locations. This metadata is organized by country and can be found in files named as follows:
We again use the
.jsonl structure for consistency with records.
The complete history of device metadata is included, so that any changes in, say, device location can be tracked. A typical entry looks like this:
effective_to fields, in Unix time, give the dates on which the metadata was valid. These values will never overlap between different rows for a given device, so that a query of the form
effective_from ≤ t ≤ effective_to will never return more than one entry. This is the same as using a
BETWEEN operator in SQL. For simplicity, the
is_current_row field can also be used to check for currently valid metadata.
The vertical_axis and horizontal_axes fields are useful for certain calculations, such as Peak Ground Accelerations.