https://github.com/pyrocko/qseek

The earthquake detection and localization framework 🔥
https://github.com/pyrocko/qseek

earth-observation earthquakes seismicity seismology

Last synced: 10 days ago
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The earthquake detection and localization framework 🔥

• Host: GitHub
• URL: https://github.com/pyrocko/qseek
• Owner: pyrocko
• License: gpl-3.0
• Created: 2023-04-27T17:04:06.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2025-09-28T10:43:56.000Z (18 days ago)
• Last Synced: 2025-09-28T12:33:56.489Z (18 days ago)
• Topics: earth-observation, earthquakes, seismicity, seismology
• Language: Python
• Homepage: https://pyrocko.github.io/qseek/
• Size: 2.18 MB
• Stars: 44
• Watchers: 6
• Forks: 8
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-seismology - Qseek (pyrocko) - The friendly earthquake detector.  (Source parameter estimation)

README

          
# Qseek

*Data-driven Earthquake Detection*

[![uv](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json)](https://github.com/astral-sh/uv)

[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)

[![Pre-commit and Tests](https://github.com/pyrocko/qseek/actions/workflows/tests.yaml/badge.svg)](https://github.com/pyrocko/qseek/actions/workflows/tests.yaml)

[![Python 3.10+](https://img.shields.io/badge/Python-3.10+-blue.svg)](https://python.org/)

[![PyPI - Version](https://img.shields.io/pypi/v/qseek)](https://pypi.org/project/qseek/)

[![Documentation](https://img.shields.io/badge/read-documentation-blue)](https://pyrocko.github.io/qseek/)

Qseek is a an automatic, data-driven earthquake detection and localisation tool designed for large seismic data sets. It combines neural network phase annotations with a stacking-and-migration and an adaptive octree localisation approach.

Key features are:

* Earthquake phase detection using machine-learning model from [SeisBench](https://github.com/seisbench/seisbench), pre-trained on different data sets:

  * [PhaseNet (Zhu and Beroza, 2018)](https://doi.org/10.1093/gji/ggy423)

  * [EQTransformer (Mousavi et al., 2020)](https://doi.org/10.1038/s41467-020-17591-w)

  * [OBSTransformer (Niksejel and Zahng, 2024)](https://doi.org/10.1093/gji/ggae049)

  * LFEDetect

* Ray tracers:

  * Constant velocity

  * 1D Layered velocity model

  * 3D fast-marching velocity model (NonLinLoc compatible)

* Earthquake magnitudes and other features:

  * Local magnitudes (ML) with different attenuation models

  * Moment Magnitudes (MW) based on modelled attenuation curves ([Dahm et al., 2024](https://doi.org/10.26443/seismica.v3i2.1205))

  * Ground motion attributes (e.g. PGA, PGV, ...)

* Station Corrections

  * SST: station specific corrections

  * SSST: source specific station corrections

Qseek is built on top of [Pyrocko](https://pyrocko.org).

## Documentation

Online documentation is available at .

## Installation

From [PyPi](https://pypi.org/project/qseek/).

```sh

pip install qseek

```

Installation from GitHub.

```sh

pip install git+https://github.com/pyrocko/qseek

```

## Project Initialisation

Print the default config with

```sh

qseek config

```

Edit the `my-project.json`

Start the earthquake detection with

```sh

qseek search search.json

```

## Packaging

The simplest and recommended way of installing from source:

### Development

Local development through pip.

```sh

cd qseek

uv pip install -e .

```

The project utilizes pre-commit for clean commits, install the hooks via:

```sh

pre-commit install

```

## Citation

Please cite Qseek as:

> Isken, M., Niemz, P., Münchmeyer, J., Büyükakpınar, P., Heimann, S., Cesca, S., Vasyura-Bathke, H., & Dahm, T. (2025). Qseek: A data-driven Framework for Automated Earthquake Detection, Localization and Characterization. Seismica, 4(1). 

## License

Contribution and merge requests by the community are welcome!

Qseek was written by Marius Paul Isken and is licensed under the GNU GENERAL PUBLIC LICENSE v3.