University of Texas at Dallas Earthquake Dataset
Project description
UTDQuake
University of Texas at Dallas Earthquake Dataset
Authors
- Emmanuel Castillo (emmanuel.castillotaborda@utdallas.edu)
- Nadine Ushakov (nadine.igonin@utdallas.edu)
- Marine Denolle (mdenolle@uw.edu)
Dataset
The dataset is available on Hugging Face: UTDQuake
Why this dataset matters?
Curated datasets of earthquake events and phase picks are essential for modern seismology, especially in the AI era. While waveform datasets have advanced earthquake detection, multistation picks provide complementary information crucial for phase association and earthquake location.
This dataset offers structured event catalogs, station metadata, and phase picks across networks, supporting reproducible research and the development of data-driven seismological methods.
What’s inside?
| Directory | Format | Description |
|---|---|---|
bank/ |
*.zip |
ObsPlus EventBank datasets, one per network. Can be read directly using ObsPlus EventBank. |
events/ |
*.parquet |
Earthquake event catalogs per network. |
stations/ |
*.parquet |
Station metadata per network. |
picks/ |
*.parquet |
Seismic phase pick datasets per network. |
For details on the contents and schema of each dataset, please refer to the Hugging Face dataset viewer.
To get started, see the Quick Start section below, or click “Use this dataset” on the Hugging Face dataset page for example loading code.
Quick start
Basic Access
import utdquake as utdq
# dataset overview
dataset = utdq.Dataset()
print(dataset)
# network level
network_data = dataset.networks
print(network_data)
dataset.plot_overview(savepath="utdquake.png")
Network Data
# load network
network = dataset.get_network(name="tx")
print(network)
# events
events = network.events
print(events)
# stations
stations = network.stations
print(stations)
# picks
picks = network.picks
print(picks)
Event Bank
Check ObsPlus EventBank for more details.
# get event bank
ebank = network.bank #
# Example: Filter by event_id
ev_ids = events["event_id"].iloc[:5].tolist()
cat = ebank.get_events(event_id=ev_ids)
print(cat)
# Example 2: Other filter (check obsplus.EventBank for more details)
cat2 = ebank.get_events(minmagnitude=4.3)
print(cat2)
Plot
# get Obspy Event
network = dataset.get_network(name="tx")
network.plot_overview(savepath="overview.png")
network.plot_uncertainty_boxplots(savepath="uncertainty_boxplots.png")
network.plot_station_location_uncertainty(savepath="station_location_uncertainty.png")
network.plot_stats(savepath="stats.png")
network.plot_pick_histograms(savepath="histograms.png")
network.plot_pick_stats(savepath="pick_stats.png")
Thanks
Thanks to the UT Dallas HPC team for providing the computational resources for this dataset.
We also thank the seismology and AI communities for their work in earthquake research, and Hugging Face for hosting and sharing open datasets.
We welcome feedback and contributions!
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file utdquake-0.1.tar.gz.
File metadata
- Download URL: utdquake-0.1.tar.gz
- Upload date:
- Size: 40.6 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.11.11
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
e1954abc21ff877b273c6083c8bccc8bfd0835042993375e6ebcb5b174654b71
|
|
| MD5 |
575a5abab4d85b4875622e08deea39bf
|
|
| BLAKE2b-256 |
be10ada9f34c31919de30ec3f37e7dfcf76648fb255a6388b3739e967f9045bf
|
File details
Details for the file utdquake-0.1-py3-none-any.whl.
File metadata
- Download URL: utdquake-0.1-py3-none-any.whl
- Upload date:
- Size: 40.7 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.11.11
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
c41f5e9e9f35acba7c4c557935ae1dc19b946ebc54dec397c5dcd5c65b241530
|
|
| MD5 |
a6e7a27ce3e37c7e053055ebabdd1bd9
|
|
| BLAKE2b-256 |
cc914ff5d1b726bd833a97f4a44ddabdb4b1b1984c2c61b0a8c96145838b423c
|